Determination method and device of fluid seepage model, equipment and storage medium

Abstract

The invention discloses a fluid seepage model determination method and device, equipment and a storage medium. The method comprises the steps that at least one rock core model of a target rock stratum is determined; determining pore characteristic parameters of the rock core models, and determining a target rock core model from at least one rock core model; the pore characteristic parameters of the target rock core model are matched with the pore characteristic parameters of a target rock stratum; and determining a fluid seepage model of the target rock stratum according to the target rock core model. According to the technical scheme, the target rock core model of the target rock stratum is determined, fluid seepage simulation is conducted according to the pore space subjected to numerical simulation of the target rock core model, the fluid seepage model of the target rock stratum is determined, and construction of the fluid seepage model is achieved; further, the flow state of water-natural gas in the rock pore structure and the distribution rule of the residual gas can be determined through the fluid seepage model, and a theoretical basis is provided for defining the cause of formation of the residual gas and exploring the two-phase seepage rule.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method, device, equipment and storage medium for determining a fluid seepage model. Background Art

[0002] Low-permeability sandstone gas reservoirs have the characteristics of low porosity and low permeability, micro-nanoscale pore throat size, and water locking phenomenon affecting gas flow, making the development of low-permeability gas reservoirs with edge and bottom water or high water content difficult and the recovery rate low.

[0003] At present, fluid flow simulation in micro-porous media provides an effective means to understand the two-phase flow in the pore throat space. However, the existing technology mainly considers the flow simulation in simple porous media such as filling balls, and focuses on the single-phase flow simulation of natural gas. The actual edge and bottom water gas reservoirs consider the co-seepage behavior of gas and water phases. Therefore, the practicality of the existing technology is poor.

[0004] In order to enable the subsequent gas reservoirs to be developed efficiently and reasonably, a fluid seepage model with high accuracy, strong operability and consistent with the actual situation is needed to simulate the two-phase flow of low-permeability sandstone reservoirs, understand the flow state of water-natural gas in the pore throat structure and the distribution law of residual gas, clarify the cause of residual gas and explore the two-phase seepage law, so as to provide a basis for improving the utilization and potential of residual gas in edge-bottom water gas reservoirs. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for determining a fluid seepage model to realize the construction of the fluid seepage model.

[0006] In a first aspect, an embodiment of the present invention provides a method for determining a fluid seepage model, the method comprising:

[0007] Determine at least one core model of the target rock formation; the core model is a digital model of a three-dimensional porous medium domain representing the rock pore structure in a cubic form;

[0008] Determine the pore characteristic parameters of the core model, and determine a target core model from at least one of the core models; the pore characteristic parameters include porosity, pore radius information, throat radius information, and pore throat coordination number; the pore characteristic parameters of the target core model match the pore characteristic parameters of the target rock formation;

[0009] According to the target core model, a fluid seepage model of the target rock formation is determined; the fluid seepage model includes water-driven gas flow characteristics, flow continuity equations and motion equations of the target rock formation.

[0010] In a second aspect, an embodiment of the present invention further provides a device for determining a fluid seepage model, the device comprising:

[0011] A core model determination module, used to determine at least one core model of the target rock formation; the core model is a digital model of a three-dimensional porous medium domain representing the rock pore structure in a cubic form;

[0012] A target core model determination module is used to determine the pore characteristic parameters of the core model, and determine the target core model from at least one of the core models; the pore characteristic parameters include porosity, pore radius information, throat radius information and pore throat coordination number; the pore characteristic parameters of the target core model match the pore characteristic parameters of the target rock formation;

[0013] The fluid seepage model determination module is used to determine the fluid seepage model of the target rock formation according to the target core model; the fluid seepage model includes the water-driven gas flow characteristics, flow continuity equation and motion equation of the target rock formation.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for determining a fluid seepage model as described in any one of the embodiments of the present invention is implemented.

[0015] In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a method for determining a fluid seepage model as described in any one of the embodiments of the present invention.

[0016] The technical solution of the embodiment of the present invention determines the target core model of the target rock formation, performs fluid seepage simulation based on the pore space numerically simulated by the target core model, determines the fluid seepage model of the target rock formation, and realizes the construction of the fluid seepage model. Then, the flow state of water-natural gas in the pore throat structure and the distribution law of the residual gas can be determined through the fluid seepage model, providing a theoretical basis for clarifying the cause of the residual gas and exploring the two-phase seepage law.

[0017] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a flow chart of a method for determining a fluid seepage model provided in Embodiment 1 of the present invention;

[0020] Figure 2 is a schematic diagram of a binary slice image of a target rock formation provided in Example 1 of the present invention;

[0021] Figure 3 : is a schematic diagram of water-displacement gas simulation of a target core model provided in Example 1 of the present invention;

[0022] Figure 4 This is a graph of residual gas saturation under various production pressure differences and water-gas ratio conditions provided in Example 1 of the present invention.

[0023] Figure 5 is a structural schematic diagram of a device for determining a fluid seepage model provided in a second embodiment of the present invention;

[0024] Figure 6 It is a structural schematic diagram of an electronic device for implementing the method for determining a fluid percolation model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1A flow chart of a method for determining a fluid seepage model is provided for the first embodiment of the present invention. This embodiment is applicable to the determination of a fluid seepage model. The method can be executed by a device for determining a fluid seepage model. The device for determining a fluid seepage model can be implemented in the form of hardware and / or software. The device for determining a fluid seepage model can be configured in any electronic device with network communication and computing capabilities. Figure 1 As shown, the method includes:

[0029] S110. Determine at least one core model of the target rock formation, wherein the core model is a digital model of a three-dimensional porous medium domain that represents the rock pore structure in a cubic form.

[0030] It should be noted that, in order to solve the problem of difficulty in developing low-permeability sandstone gas reservoirs and low recovery, this embodiment studies the fluid flow mechanism of low-permeability sandstone gas reservoirs, and provides help for improving the utilization and potential of residual gas in edge-bottom water gas reservoirs. Therefore, the application scenario of this embodiment is low-permeability sandstone gas reservoirs.

[0031] In this embodiment, the target rock formation is a low-permeability sandstone formation, and the core model is a digital model of a three-dimensional porous medium domain that represents the rock pore structure in a cubic form.

[0032] Due to the complex geometry of the target rock formation, the porous media structures at different locations on the target rock formation may be different. By determining at least one core model of the target rock formation to provide multiple core models, a core model that better matches the porous media structure of the target rock formation, which is a low-permeability sandstone formation, can be determined from the multiple core models to avoid errors.

[0033] As an optional but non-limiting implementation, determining at least one core model of the three-dimensional porous medium model of the target rock formation includes steps A1-A2:

[0034] Step A1, determining a three-dimensional porous medium model of a target rock formation; the three-dimensional porous medium model is a digital model representing the rock pore structure of the target rock formation.

[0035] Step A2: intercept at least one cube from the three-dimensional porous medium model to obtain at least one core model.

[0036] In this embodiment, the three-dimensional porous medium model is a digital model representing the pore structure of the target rock formation. Further, at least one core model is obtained by intercepting at least one cube model in the three-dimensional porous medium model.

[0037] The core model in this embodiment is a model that digitally represents the microstructure of a real core, presenting information such as the pore structure and mineral distribution inside the rock in the form of three-dimensional data, and can provide important data for studying the physical properties of rocks and the seepage behavior of fluids in rocks in the fields of petroleum engineering and geological research.

[0038] Since the data volume of the three-dimensional porous medium model of the target rock formation is large, this embodiment obtains a core model that can represent the porous medium of the target rock formation by intercepting a cube from the three-dimensional porous medium model of the target rock formation, thereby reducing the amount of calculated data and improving data calculation efficiency.

[0039] As an optional but non-limiting implementation, determining the three-dimensional porous medium model of the target rock formation includes steps B1-B4:

[0040] Step B1, cutting a target core column from a target rock formation.

[0041] Step B2: Scan the target core column by CT to obtain multiple two-dimensional grayscale slice images.

[0042] Step B3: binarize the two-dimensional grayscale slice image to obtain a binary slice image.

[0043] Step B4: reconstruct the plurality of binary slice images in three dimensions to obtain a three-dimensional porous medium model of the target rock formation.

[0044] In this embodiment, by collecting the full-diameter core of the target rock layer in the low-permeability reservoir sandstone block, drilling the full-diameter core and drying it, a target core column is obtained, and the target core column can be a core column with a diameter of about 2 cm and a length of about 2.86 cm. The target core column can represent the rock structure and pore space structure of the target rock layer.

[0045] Furthermore, some imaging techniques can be used to obtain a series of slice images of the target core column. In this embodiment, a nano-CT scanner is used to perform micro-CT scanning and imaging of the target core column, and corresponding working parameters are set to obtain multiple two-dimensional grayscale slice images of the target core column. These grayscale images show the structural characteristics of the sandstone reservoir from different angles and different levels, including the rock skeleton and pore space.

[0046] Next, before binarization, it may be necessary to pre-process and denoise the two-dimensional grayscale slice image. By denoising the two-dimensional grayscale slice image, noise points caused by the imaging device itself or external interference can be removed. Mean filtering, median filtering and other methods can be used to improve the image quality and make the subsequent binarization more accurate. Image enhancement can also be performed to enhance the contrast between pores and rock skeletons to better distinguish the two.

[0047] In this embodiment, according to the multiple two-dimensional grayscale slice images obtained, the CT image grid size is set to 300×300 pixels, and the two-dimensional grayscale slice images are gridded. The discrete convolution method can be used to filter the multiple two-dimensional grayscale slice images to obtain the denoised two-dimensional grayscale slice images.

[0048] Furthermore, according to the different responses of the pore structure and rock skeleton to the grayscale value in the sandstone reservoir image, a suitable threshold is selected for binarization. The grayscale image is converted into a binary image containing only two values, 0 and 1. Usually, a certain grayscale value is set as the threshold. For example, the pixel points above the threshold are assigned a value of 1, indicating the rock skeleton part; the pixel points below the threshold are assigned a value of 0, indicating the pore part. There are many methods for selecting the threshold, such as the bimodal method based on the histogram (if the grayscale histogram of the image shows an obvious bimodal distribution, the grayscale value corresponding to the valley between the two peaks can be selected as the threshold), the maximum inter-class variance method (different categories are distinguished by calculating the threshold that maximizes the inter-class variance, here, pores and rock skeletons), etc., to ensure that the binarized image can accurately reflect the pore structure of the reservoir.

[0049] Specifically, an image with a grid size of 300×300 is selected, and the discrete data of the two-dimensional grayscale slice image is filtered by the discrete convolution method to obtain a two-dimensional grayscale slice image with denoised points, where the filtering expression is:

[0050]

[0051] Wherein, g(i,j) is the gray value of the unit grid in the gridded image of the two-dimensional grayscale slice image, a dimensionless quantity with a value range of 0-255; 0 and 1 are the values ​​of the unit grid in the two-dimensional grayscale slice image of the low permeability sandstone reservoir. When the value of the unit grid in the two-dimensional grayscale slice image of the low permeability sandstone reservoir is 1, it is displayed in black.

[0052] Furthermore, the denoised two-dimensional grayscale slice image can be binarized by a threshold segmentation method to obtain a binary slice image. All the binary slice images are reconstructed by a three-dimensional reconstruction technology to obtain a three-dimensional porous medium model represented by a three-dimensional porous medium domain of a low-permeability sandstone reservoir.

[0053] See also Figure 2 Shown is a schematic diagram of the binary slice map of the target rock formation, reflecting the porous media field of the low-permeability sandstone reservoir, and the red area represents the pore space.

[0054] Specifically, the basic principle of 3D reconstruction is to reassemble a series of 2D slice images into a 3D spatial structure model according to their spatial position relationship. That is, stack each binary slice image in the correct order and position in 3D space, while considering the spatial continuity and correlation between slices, so as to restore the real 3D porous media structure inside the low-permeability sandstone reservoir. Commonly used 3D reconstruction methods include voxel method, triangular patch method and contour-based interpolation reconstruction method.

[0055] In this embodiment, all binary slice images can be reconstructed in three dimensions by voxel method, and the three-dimensional space can be divided into tiny cubic units. Each cubic unit is assigned a value (0 or 1) according to the pixel value of the corresponding position in the corresponding binary slice image, and finally all cubic units are combined to form a three-dimensional porous medium model represented by the three-dimensional porous medium domain of the target rock formation.

[0056] S120, determining pore characteristic parameters of the core model, and determining a target core model from at least one of the core models; the pore characteristic parameters of the target core model match the pore characteristic parameters of the target rock formation.

[0057] In this embodiment, the pore characteristic parameters include porosity, pore radius information, throat radius information and pore throat coordination number. The pore radius information includes pore radius and pore radius distribution information, and the throat radius information includes throat radius and throat radius distribution information.

[0058] It should be noted that porosity refers to the percentage of pore volume in rock to the total volume of rock. Pore radius and throat radius are radius indicators of pores and throats, which can be average radius, maximum radius or shortest radius, etc. The larger the average radius of pores and throats, the better the connectivity of pore space and the stronger the seepage capacity of liquid in pores. The pore throat coordination number refers to the number of pores connected to each throat. The larger the pore throat coordination number, the better the connectivity between pores.

[0059] In practical applications, if the digital core model is represented in the form of a grayscale image, a suitable grayscale threshold can be set according to the difference in grayscale values ​​between pores and rock skeletons. Grayscale values ​​greater than the threshold are considered to be rock skeletons, and those less than the threshold are pores. The porosity of the core model is estimated by calculating the ratio of the sum of the grayscale values ​​of the pore part to the sum of the grayscale values ​​of the entire model.

[0060] In addition, the core model can be image analyzed, the pores can be regarded as approximately circular, their equivalent radius can be measured, and the radius distribution can be statistically analyzed to determine the main pore throat radius range and distribution pattern, or the pore radius information and throat radius information can be determined by simulating the fluid injection process using numerical simulation methods.

[0061] In addition, an image recognition algorithm can be used to identify the number of pores connected to each throat to count the pore throat coordination number. The larger the coordination number, the better the pore throat connectivity. In this embodiment, the method for determining the pore characteristic parameters of the core model is not specifically limited.

[0062] As an optional but non-limiting implementation, determining the pore characteristic parameters of the core model and determining a target core model from at least one of the core models comprises steps C1-C3:

[0063] Step C1, determining the pore characteristic parameters of the core model.

[0064] Step C2: Determine the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters.

[0065] Step C3: determining a target core model from the core models according to the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters.

[0066] In this embodiment, the pore characteristic parameters of the core model are pore characteristic parameters determined according to the core model, and the measured pore characteristic parameters are pore characteristic parameters actually measured for the target rock formation. The measured pore characteristic parameters may be experimental data of physical experimental tests on core samples of the target rock formation in a laboratory or calculation results of other verified rock models. Among them, the measured pore characteristic parameters include actually measured porosity, pore radius information, throat radius information, and pore throat coordination number.

[0067] It should be noted that the present embodiment can determine the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters based on the comprehensive evaluation of the weights. For the pore characteristic parameters, they can be decomposed into multiple sub-parameters (such as porosity, pore radius information, throat radius information, and pore throat coordination number, etc.). A weight can be determined for each sub-parameter according to its importance to connectivity, and then the similarity between the sub-parameters of the core model and the corresponding measured sub-parameters can be calculated by relative error or absolute error, and the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters is obtained by weighted summation of the similarities of the sub-parameters.

[0068] Furthermore, in practical applications, the relative error or absolute error between the core model sub-parameter and the corresponding measured sub-parameter can be determined by direct comparison or distribution comparison. Different data forms correspond to different error calculation methods.

[0069] If a sub-parameter has only one exact value, the relative error or absolute error between the core model sub-parameter and the corresponding measured sub-parameter can be determined by direct comparison.

[0070] If the sub-parameter is a density distribution, the relative error or absolute error between the core model sub-parameter and the corresponding measured sub-parameter can be determined by the distribution comparison method. For example, for the pore throat radius distribution, the radii of each pore throat in the core model are different. The probability density function (such as normal distribution, lognormal distribution, etc.) can be fitted to each pore throat radius of the core model and the measured pore throat radius, and then the parameters of the fitted probability density function (such as mean, standard deviation) can be compared. If the pore throat radius distribution of the core model is fitted to the normal distribution N(μ1,σ1), and the measured pore throat radius distribution is fitted to N(μ2,σ2), the relative error of the mean can be calculated as And the relative error of the standard deviation is

[0071]

[0072] Furthermore, if the pore radius information weight is w1, the pore throat coordination number weight is w2, the porosity weight is w3, the throat radius information weight is w4, the relative error between the pore radius of the rock model and the measured pore radius is E1, the relative error between the pore throat coordination number of the rock model and the measured pore throat coordination number is E2, the relative error between the porosity of the rock model and the measured porosity is E3, and the relative error between the throat radius of the rock model and the measured throat radius is E4, then the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters is S = 1-(w1E1+w2E2+w3E3+w4E4).

[0073] This embodiment does not specifically limit the process of determining the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters.

[0074] The target core model is determined from at least one core model by comparing the pore characteristic parameters of the core model with the measured pore characteristic parameters, so that the target core model can represent the rock pore structure characteristics of the target rock formation, thereby improving the accuracy of the digital core model.

[0075] As an optional but non-limiting implementation, determining a target core model from the core model according to the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters includes:

[0076] According to whether the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters meets the preset conditions, a target core model is determined from the core model.

[0077] In this embodiment, the preset condition may be the maximum value of the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters, or the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters is greater than a preset similarity threshold.

[0078] When the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters meets the preset conditions, it means that the core model conforms to the rock structure and porous medium characteristics of the actual target rock formation, and the digital core model can represent the rock pore structure characteristics of the target rock formation.

[0079] S130, determining a fluid seepage model of the target rock formation according to the target core model; the fluid seepage model includes water-driven gas flow characteristics, flow continuity equations, and motion equations of the target core model.

[0080] In this embodiment, the fluid seepage model is constructed by the water-driven gas flow characteristics, flow continuity equation and motion equation of the target core model. The fluid seepage model can represent the seepage behavior of the fluid in the pores of the target rock formation under various conditions, including but not limited to flow velocity, pressure distribution and flow rate changes.

[0081] It should be noted that, in this embodiment, the type of fluid seepage model (Darcy seepage model or non-Darcy seepage model) of the target rock formation is determined according to the pore characteristic parameters of the target core model. Since the target rock formation of this embodiment is low permeability sandstone, the pore characteristic parameters of the target core model correspond to the pore structure characteristics of the low permeability sandstone.

[0082] Furthermore, matching flow continuity equations and motion equations are determined according to the type of fluid seepage model.

[0083] In addition, the inner domain of the target core model is created by the unstructured grid division method, and the pores indicated by the pore characteristic parameters of the target core model are determined. By establishing a fluid flow test simulation for the pores indicated by the pore characteristic parameters of the target core model, the flow continuity equation and the parameters of the motion equation of the target core model, such as the parameters corresponding to the fluid motion state, are determined to obtain the fluid seepage model of the target rock formation.

[0084] As an optional but non-limiting implementation, the fluid seepage model of the target rock formation is determined according to the target core model, including steps D1-D2:

[0085] Step D1, performing fluid flow test simulation according to the pore space indicated by the pore characteristic parameters of the target core model to determine the water-driven gas flow characteristics of the target core model; the water-driven gas flow characteristics include velocity field and pressure field.

[0086] Step D2: Water-drive gas flow characteristics Based on the water-drive gas flow characteristics, the flow continuity equation and the motion equation of the fluid seepage model of the target rock formation are determined.

[0087] In this embodiment, the fluid flow test simulation can be based on the microscopic porous media flow simulation of the Navier-Stokes equations (NS equations), and the inner domain of the target core model is created by the unstructured grid division method, and the calculation area is divided into many small units, including determining the grid size, grid type, and grid quality optimization parameters. For example, for areas with complex pore structures that require accurate simulation of gas flow details (such as narrow pores, key parts of pore connectivity, etc.), a smaller grid size can be set; and for areas such as rock skeletons that have relatively little impact on gas flow, the grid size can be appropriately increased to ensure the simulation accuracy while controlling the number of grids and reducing the amount of calculation.

[0088] In this embodiment, a single-phase gas flow test simulation can be performed to determine the water-driven gas flow characteristics of the fluid seepage model. Specifically, basic physical property parameters such as single-phase gas density and dynamic viscosity are first determined. It should be noted that in practical applications, corresponding adjustments can be made according to the actual temperature, pressure and other conditions of the simulation, because the properties of the gas, such as density and viscosity, usually change with these state parameters.

[0089] Furthermore, corresponding inlet boundary conditions, outlet boundary conditions and wall boundary conditions are set in the single-phase gas flow test simulation. In addition, the single-phase gas flow test simulation includes setting the state of the gas in the target core model at the initial moment. Usually, the gas velocity field can be set to zero at the initial moment, and the pressure field can be uniformly distributed (an initial pressure value can be determined according to the pressure settings of the inlet and outlet and the overall balance of the model).

[0090] In this embodiment, the continuity equation is expressed as:

[0091]

[0092] Where ρ is the fluid density, kg / m 3 ; u is the velocity field in the porous medium domain, m / s; t is the time, s.

[0093] The two-phase motion equation is expressed as:

[0094]

[0095] Where t is the viscous stress tensor; F c is the surface tension within the mesh domain, Pa / m.

[0096] The expression of the viscous stress tensor t is:

[0097]

[0098] Where μ is the fluid dynamic viscosity, mPa·s; I is the unit tensor.

[0099] The interfacial stress F of two-phase fluid c The expression is:

[0100]

[0101] Where, σ is the interfacial tension coefficient, Pa·m; ρ l is the water phase density, kg / m3; ρ g is the density of natural gas, kg / m3.

[0102] The expression of the mass conservation condition of the water and natural gas interface in the continuity equation is:

[0103] ρ l (u l -w)·n lg =ρ g (u g -w)·n lg ;

[0104] In the formula, u l is the velocity field of the water phase, m / s; u g is the velocity field of the natural gas phase, m / s; w is the velocity field of the fluid-fluid interface in the continuous physical space, m / s; n lg Normal vector of a fluid-fluid interface in continuous physical space.

[0105] Natural gas and water are considered incompressible and without phase change in the model, that is, the relationship between the water phase velocity and the natural gas phase velocity in the porous medium domain is:

[0106] u l =u g =w;

[0107] The momentum conservation condition at the interface between water and natural gas in the two-phase fluid momentum equation is expressed as:

[0108] [p l It l ]·n lg =[p g It g ]·n lg +σκn lg ;

[0109] Among them, p l is the pressure of the water phase, Pa; p g is the pressure of the natural gas phase, pa; κ is the interface curvature in the continuous physical space, m -1 .

[0110] The expression of interface curvature κ is:

[0111] Furthermore, a suitable numerical solution method is selected to determine the water-driven gas flow characteristics of the target core model. Commonly used numerical solution methods include finite difference method, finite element method and finite volume method. For example, the finite volume method is widely used in fluid simulation. It uses the grid unit in the inner domain of the core model as the control volume, integrates the control equation of gas flow (such as the Navier-Stokes equation, which can be simplified to the corresponding form in the case of single-phase laminar flow) on each control volume, ensures the conservation of physical quantities such as mass and momentum, and then iteratively solves the discretized equation group to obtain the change of variables such as velocity and pressure of the single-phase gas in the target core model with time and space, and then determines the velocity field and pressure field in the water-driven gas flow characteristics of the target core model. Furthermore, the velocity field and pressure field of the water-driven gas flow characteristics are substituted into the flow continuity equation and the motion equation, and a two-phase fluid seepage model of three-dimensional water-driven gas in low-permeability gas reservoirs is established to determine the density and dynamic viscosity of the gas-liquid two-phase fluid, and simulate the two-phase flow in the low-permeability sandstone reservoir in the real scene according to the two-phase fluid seepage model.

[0112] See also Figure 3 The figure shows a schematic diagram of water-driven gas simulation of the target core model. The red area in the figure is the pore space (inner domain of the target core model). During the gas-liquid two-phase flow, the water phase is quickly closed at the small throats, resulting in residual gas in the small throats. The microscopic capillary force hinders the two-phase flow, which can effectively characterize the two-phase flow in low-permeability sandstone reservoirs in real scenarios.

[0113] As an optional but non-limiting implementation, after determining the fluid seepage model of the target rock formation according to the target core model, the method further includes steps E1-E2:

[0114] Step E1, determining the permeability of the target core model.

[0115] Step E2: Optimizing the water-driven gas flow characteristics of the fluid seepage model according to the difference between the permeability of the target core model and the measured permeability, until the difference between the permeability of the target core model and the measured permeability is less than a preset threshold.

[0116] In this embodiment, permeability is a physical quantity that measures the ability of porous media to allow fluid to pass through. For gas permeability, it indicates the difficulty of gas flowing in the porous medium of rock.

[0117] It should be noted that the permeability can be estimated by the pore structure characteristics of the digital core model. For example, the permeability can be calculated based on the size, shape, connectivity and other factors of the pores and throats using the pore network model method, or it can be calibrated by experimental data. The differential form of Darcy's law is applied to each grid node to establish a linear equation system. The pressure distribution is obtained by solving this equation system, and then the gas phase permeability is calculated based on the relationship between flow rate and pressure gradient.

[0118] Furthermore, the permeability of the target core model can be calculated using Darcy's law according to the pressure field and the velocity field.

[0119] The measured permeability can be the permeability data obtained by conducting physical experiments on core samples (the actual core corresponding to the target core model). By conducting permeability experiments on real core samples (the actual core corresponding to the digital core model), the value of the measured permeability can be determined by the ratio of the difference between the inlet pressure and the outlet pressure to the flow rate.

[0120] In order to verify the accuracy of the permeability obtained by numerical simulation, this embodiment can be calibrated in combination with laboratory experiments. The permeability of the target core model is compared with the measured permeability to determine the difference between the permeability of the target core model and the measured permeability, and the target core model or water-driven gas flow characteristics are adjusted and optimized according to the difference, such as checking the geometric accuracy of the core model, the rationality of the boundary conditions, etc., until the difference between the permeability of the target core model and the measured permeability is less than a preset threshold.

[0121] Furthermore, according to the fluid seepage model of the target rock formation, the simulation software can be used to complete the water-displacement-gas two-phase flow simulation of the low-permeability sandstone reservoir to obtain the water-displacement-gas two-phase seepage law of the low-permeability sandstone reservoir.

[0122] See also Figure 4 The graph shows the residual gas saturation under various production pressure differences and water-gas ratios. Considering the actual production conditions of water-gas ratios of 0.3, 0.6, 1.0, 1.2, 1.5, 1.8, and 2, and the production pressure difference of 3-15MPa, multiple groups of simulations were performed to obtain the relationship between the production pressure difference and the residual gas saturation. The residual gas saturation under different production pressure differences and water-gas ratios was used to obtain the two-phase seepage law of water-driven gas in low-permeability sandstone reservoirs, which can effectively guide the development of bottom water gas reservoirs.

[0123] The technical solution of the embodiment of the present invention determines the target core model of the target rock formation, performs fluid seepage simulation based on the pore space numerically simulated by the target core model, determines the fluid seepage model of the target rock formation, and realizes the construction of the fluid seepage model. Then, the flow state of water-natural gas in the pore throat structure and the distribution law of the residual gas can be determined through the fluid seepage model, providing a theoretical basis for clarifying the cause of the residual gas and exploring the two-phase seepage law.

[0124] Embodiment 2

[0125] Figure 5 This is a schematic diagram of the structure of a device for determining a fluid seepage model provided in the second embodiment of the present invention. This embodiment is applicable to the determination of a fluid seepage model. The device for determining a fluid seepage model can be implemented in the form of hardware and / or software. The device for determining a fluid seepage model can be configured in any electronic device with network communication and computing capabilities. Figure 5 As shown, the device comprises:

[0126] A core model determination module 310 is used to determine at least one core model of the target rock formation; the core model is a digital model of a three-dimensional porous medium domain that represents the rock pore structure in a cubic form;

[0127] The target core model determination module 320 is used to determine the pore characteristic parameters of the core model, and determine the target core model from at least one of the core models; the pore characteristic parameters include porosity, pore radius information, throat radius information and pore throat coordination number; the pore characteristic parameters of the target core model match the pore characteristic parameters of the target rock formation;

[0128] The fluid seepage model determination module 330 is used to determine the fluid seepage model of the target rock formation according to the target core model; the fluid seepage model includes the water-driven gas flow characteristics, flow continuity equation and motion equation of the target rock formation.

[0129] Optionally, the core model determination module includes:

[0130] Determine a three-dimensional porous medium model of the target rock formation; the three-dimensional porous medium model is a digital model representing the rock pore structure of the target rock formation;

[0131] At least one cube is cut from the three-dimensional porous medium model to obtain at least one core model.

[0132] Optionally, the core model determination module includes:

[0133] cutting a target core column from a target rock formation;

[0134] Scan the target core column by CT to obtain multiple two-dimensional grayscale slice images;

[0135] Binarizing the two-dimensional grayscale slice image to obtain a binary slice image;

[0136] A three-dimensional reconstruction is performed on a plurality of the binary slice images to obtain a three-dimensional porous medium model of the target rock formation.

[0137] Optionally, the target core model determination module includes:

[0138] Determining pore characteristic parameters of the core model;

[0139] Determining the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters, wherein the measured pore characteristic parameters are actual pore characteristic parameters of the target rock formation;

[0140] According to the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters, a target core model is determined from the core model.

[0141] Optionally, the target core model determination module includes:

[0142] According to whether the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters meets the preset conditions, a target core model is determined from the core model.

[0143] Optionally, the fluid seepage model determination module includes:

[0144] Performing fluid flow test simulation according to the pores indicated by the pore characteristic parameters of the target core model to determine the water-driven gas flow characteristics of the target core model; the water-driven gas flow characteristics include velocity field and pressure field;

[0145] According to the flow characteristics of water-driven gas, the flow continuity equation and motion equation of the fluid seepage model of the target rock formation are determined.

[0146] Optionally, the fluid seepage model determination module further includes:

[0147] Determine the permeability of the target core model;

[0148] The water-driven gas flow characteristics of the fluid seepage model are optimized according to the difference between the permeability of the target core model and the measured permeability.

[0149] The technical solution of the embodiment of the present invention determines the target core model of the target rock formation, performs fluid seepage simulation based on the pore space numerically simulated by the target core model, determines the fluid seepage model of the target rock formation, and realizes the construction of the fluid seepage model. Then, the flow state of water-natural gas in the pore throat structure and the distribution law of the residual gas can be determined through the fluid seepage model, providing a theoretical basis for clarifying the cause of the residual gas and exploring the two-phase seepage law.

[0150] The device for determining a fluid seepage model provided in the embodiment of the present invention can execute the method for determining a fluid seepage model provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0151] Embodiment 3

[0152] Figure 6A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic 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 electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, 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 examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0153] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0155] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a fluid seepage model.

[0156] In some embodiments, the method for determining the fluid percolation model may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the fluid percolation model described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the fluid percolation model in any other appropriate manner (e.g., by means of firmware).

[0157] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] Computer programs for implementing the methods of the present invention 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0159] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0160] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0162] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0163] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0164] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a fluid seepage model, characterized in that: include: Determine at least one core model of the target rock formation; the core model is a digital model of a three-dimensional porous medium domain representing the rock pore structure in a cubic form; Determine the pore characteristic parameters of the core model, and determine a target core model from at least one of the core models; the pore characteristic parameters include porosity, pore radius information, throat radius information, and pore throat coordination number; the pore characteristic parameters of the target core model match the pore characteristic parameters of the target rock formation; According to the target core model, a fluid seepage model of the target rock formation is determined; the fluid seepage model includes water-driven gas flow characteristics, flow continuity equations and motion equations of the target rock formation.

2. The method according to claim 1, characterized in that Determining at least one core model of a three-dimensional porous medium model of a target rock formation includes: Determine a three-dimensional porous medium model of the target rock formation; the three-dimensional porous medium model is a digital model representing the rock pore structure of the target rock formation; At least one cube is cut from the three-dimensional porous medium model to obtain at least one core model.

3. The method according to claim 2, characterized in that Determine the three-dimensional porous media model of the target rock formation, including: cutting a target core column from a target rock formation; Scan the target core column by CT to obtain multiple two-dimensional grayscale slice images; Binarizing the two-dimensional grayscale slice image to obtain a binary slice image; A three-dimensional reconstruction is performed on a plurality of the binary slice images to obtain a three-dimensional porous medium model of the target rock formation.

4. The method according to claim 1, characterized in that Determining pore characteristic parameters of the core models, and determining a target core model from at least one of the core models, comprises: Determining pore characteristic parameters of the core model; Determining the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters, wherein the measured pore characteristic parameters are actual pore characteristic parameters of the target rock formation; According to the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters, a target core model is determined from the core model.

5. The method according to claim 4, characterized in that Determining a target core model from the core model according to the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters includes: According to whether the similarity between the pore characteristic parameters of the core model and the measured pore characteristic parameters meets the preset conditions, a target core model is determined from the core model.

6. The method according to claim 1, characterized in that According to the target core model, determine the fluid seepage model of the target rock formation, including: Performing fluid flow test simulation according to the pores indicated by the pore characteristic parameters of the target core model to determine the water-driven gas flow characteristics of the target core model; the water-driven gas flow characteristics include velocity field and pressure field; According to the flow characteristics of water-driven gas, the flow continuity equation and motion equation of the fluid seepage model of the target rock formation are determined.

7. The method according to claim 1, characterized in that After determining the fluid seepage model of the target rock formation according to the target core model, it also includes: Determine the permeability of the target core model; The water-driven gas flow characteristics of the fluid seepage model are optimized according to the difference between the permeability of the target core model and the measured permeability.

8. A device for determining a fluid seepage model, characterized in that: include: A core model determination module, used to determine at least one core model of the target rock formation; the core model is a digital model of a three-dimensional porous medium domain representing the rock pore structure in a cubic form; A target core model determination module is used to determine the pore characteristic parameters of the core model, and determine the target core model from at least one of the core models; the pore characteristic parameters include porosity, pore radius information, throat radius information and pore throat coordination number; the pore characteristic parameters of the target core model match the pore characteristic parameters of the target rock formation; The fluid seepage model determination module is used to determine the fluid seepage model of the target rock formation according to the target core model; the fluid seepage model includes the water-driven gas flow characteristics, flow continuity equation and motion equation of the target rock formation.

9. An electronic 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 program, the method for determining the fluid seepage model as described in any one of claims 1 to 7 is implemented.

10. A storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the method for determining a fluid percolation model as described in any one of claims 1 to 7.

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