Surfactant synergistic flooding permeability parameter optimization method based on core scale simulation

Abstract

This application provides a method for optimizing surfactant-enhanced permeation displacement parameters based on core-scale simulation. The method includes selecting real core samples from the target reservoir, preprocessing the samples, and constructing a three-dimensional digital core model of the target reservoir; selecting surfactants based on the reservoir characteristics; establishing a surfactant-enhanced permeation displacement model at the core scale based on the surfactant's enhanced permeation displacement characteristics in the porous medium of the target reservoir and the oil-water two-phase flow characteristics; iteratively solving the enhanced permeation displacement model through parallel computation to achieve dynamic simulation of the enhanced permeation displacement process at the core scale, and outputting the distribution data of the pressure field, fluid saturation field, and surfactant concentration field within the three-dimensional digital core model; and determining the target parameter combination for surfactant-enhanced permeation displacement based on the distribution data. This method aims to improve the utilization rate of crude oil.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development technology, and in particular to a method for optimizing surfactant-enhanced permeation parameters based on core-scale simulation. Background Technology

[0002] Ultra-low permeability tight / shale oil resources are an important alternative energy source for ensuring energy security. These oil reservoirs generally have micro- and nano-sized pores and extremely low permeability, making it difficult to achieve effective displacement through conventional water injection development.

[0003] Currently, the industry mainly uses large-scale volumetric fracturing technology in horizontal wells to construct artificial fracture networks, combined with water injection and huff-and-puff development models to increase crude oil production. However, in actual development, the seepage process is affected by the displacement pressure differential, and the fracturing fractures are prone to water channeling, resulting in low crude oil utilization within the matrix. Summary of the Invention

[0004] This application provides a method for optimizing surfactant-enhanced permeation parameters based on core-scale simulation, in order to improve the utilization rate of crude oil.

[0005] In a first aspect, embodiments of this application provide a method for optimizing surfactant-enhanced permeation parameters based on core-scale simulation, comprising: selecting real core samples of the target reservoir; preprocessing the real core samples to construct a three-dimensional digital core model of the target reservoir; selecting surfactants based on the reservoir characteristics of the target reservoir; establishing a surfactant-enhanced permeation model at the core scale based on the enhanced permeation characteristics of surfactants in the porous media of the target reservoir and the oil-water two-phase flow characteristics; iteratively solving the enhanced permeation model through parallel computing to achieve dynamic simulation of the enhanced permeation process at the core scale, and outputting the distribution data of pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model; and determining the target parameter combination for surfactant-enhanced permeation based on the distribution data, wherein the target parameter combination is used to determine the parameters for surfactant-enhanced permeation of the target reservoir.

[0006] In one possible implementation, a real core sample from the target reservoir is selected, and after preprocessing the real core sample, a three-dimensional digital core model of the target reservoir is constructed, including:

[0007] Authentic core samples from the target reservoir were selected, cleaned, and dried to obtain pre-processed authentic core samples. Multi-scale scanning was performed on the pre-processed authentic core samples to generate a series of grayscale images reflecting the pore structure distribution. Based on the correlation between each pixel and its adjacent similar pixels in the series of grayscale images, a non-local mean filtering algorithm was used for weighted calculation to obtain filtered grayscale images. The filtered grayscale images were input into an image segmentation model based on a fully convolutional neural network to output segmented structural images. Three-dimensional spatial fusion and mesh reconstruction were performed on the segmented structural images along the axial direction of the multi-scale scan to generate a three-dimensional digital core model.

[0008] In one possible implementation, based on the synergistic seepage displacement characteristics of surfactants in the porous media of the target reservoir and the oil-water two-phase flow characteristics, a surfactant-enhanced seepage displacement model at the core scale is established, including: determining the synergistic seepage displacement characteristics of surfactants in the porous media of the target reservoir, wherein the synergistic seepage displacement characteristics include the modification parameters of surfactant on the wettability of rock surfaces in the porous media, the diffusion coefficient of surfactant in the fluid, and the adsorption amount and adsorption rate of surfactant on rock surfaces in the porous media; determining the oil-water two-phase flow characteristics in the porous media of the target reservoir, wherein the oil-water two-phase flow characteristics include the initial oil-water interfacial tension, the oil-water viscosity ratio, and the basic relative permeability of the porous media to oil and water; based on the oil-water two-phase flow characteristics, determining the governing equations describing the flow of incompressible fluids as the core equations of the oil-water two-phase flow characteristics; and based on the surfactant's effect on the porous media... The modification parameters of rock surface wettability were used to construct a wettability modification sub-model based on a pre-defined adsorption model. This sub-model characterizes the dynamic correlation between surfactant concentration and changes in rock surface wettability in porous media. Based on the diffusion coefficient of surfactant in fluid, a diffusion sub-model was constructed using the fluid flow velocity field. This sub-model characterizes the convective-diffusion transport process of surfactant in porous media. Based on the adsorption amount and adsorption rate of surfactant on the rock surface in porous media, an adsorption retention sub-model was constructed using the initial oil-water interfacial tension. This sub-model characterizes the quantitative correlation between the dynamic adsorption-desorption process of surfactant on the rock surface in porous media and changes in oil-water interfacial tension. Based on the wettability modification sub-model, diffusion sub-model, adsorption retention sub-model, and core equation, a surfactant-enhanced permeation displacement model at the core scale was obtained.

[0009] In one possible implementation, the enhanced permeation displacement model is iteratively solved through parallel computing to achieve dynamic simulation of the enhanced permeation displacement process at the core scale, and output the distribution data of pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model. This includes: decomposing the three-dimensional digital core model into multiple sub-regions according to the spatial domain and assigning each sub-region to each computing node; each computing node independently iteratively solving the core equations of oil-water two-phase flow characteristics, surfactant convection-diffusion equations, and wettability modification coupling equations in the corresponding sub-region based on the enhanced permeation displacement model, to obtain the local pressure field, fluid saturation field, and surfactant concentration field in each sub-region; repeating the iterative solution process until the enhanced permeation displacement model converges; and integrating the local pressure field, fluid saturation field, and surfactant concentration field output by each computing node in each sub-region to obtain the distribution data of pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model.

[0010] In one possible implementation, the target parameter combination for surfactant-enhanced flooding is determined based on distribution data, including: extracting characteristic parameters reflecting the flooding effect from the distribution data; analyzing and determining the occurrence state and targeted mobilization law of the remaining oil in the three-dimensional digital core model based on the characteristic parameters, wherein the occurrence state of the remaining oil refers to its form, distribution location, and connectivity characteristics in the porous medium of rocks and fractures that have not been effectively displaced during the surfactant flooding process; the targeted mobilization law refers to the effective displacement efficiency and mechanism characteristics of the remaining oil in different occurrence states; setting parameter variables to be optimized, and configuring multiple sets of different values ​​for each parameter variable, including surfactant concentration, diffusion coefficient, adsorption amount, rock pore structure parameters of the target reservoir, reservoir wettability parameters, crude oil viscosity parameters, and inlet pressure and displacement rate in the displacement conditions; and determining the target parameter combination for surfactant-enhanced flooding based on each set of parameter variables, combined with distribution data and characteristic parameters.

[0011] Secondly, embodiments of this application provide a device for optimizing surfactant-enhanced permeation displacement parameters based on core-scale simulation, comprising: a core model construction module for selecting real core samples of the target reservoir, preprocessing the real core samples, and constructing a three-dimensional digital core model of the target reservoir; a surfactant selection module for selecting surfactants according to the reservoir characteristics of the target reservoir; an enhanced permeation displacement model construction module for iteratively solving the enhanced permeation displacement model through parallel computing to achieve dynamic simulation of the enhanced permeation displacement process at the core scale, and outputting distribution data of the pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model; and a parameter combination determination module for determining the target parameter combination for surfactant-enhanced permeation displacement based on the distribution data, wherein the target parameter combination is used to determine the parameters for surfactant-enhanced permeation displacement of the target reservoir.

[0012] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0014] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0015] The surfactant-enhanced permeation parameter optimization method provided in this application, based on core-scale simulation, constructs a three-dimensional digital core model to reconstruct the micro-nano pore-fracture structure, providing a realistic geometric carrier for locating hidden residual oil within the matrix. Then, by coupling physicochemical phenomena such as wetting modification, solute diffusion, and adsorption retention with the oil-water two-phase flow equation, it quantitatively characterizes the mechanism by which surfactants reduce interfacial tension and regulate rock wettability, clarifying how they overcome capillary forces to push unrecoverable crude oil away from the pore walls. Subsequently, by using parallel computing to efficiently output the dynamic distribution of pressure, saturation, and concentration fields, and combining parameters such as Euler coefficients and relative permeability, it analyzes the remaining oil's occurrence state and optimizes parameters such as surfactant concentration and displacement rate to ensure that surfactants efficiently reach the matrix region, directionally utilizing crude oil previously unrecoverable due to water channeling and capillary forces. This transforms previously difficult-to-displace microporous residual oil into recoverable reserves, effectively improving the crude oil recovery rate in complex reservoirs. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] Figure 1 A schematic diagram illustrating a scenario for the surfactant-enhanced permeation displacement parameter optimization method based on core-scale simulation provided in this application embodiment;

[0018] Figure 2 A schematic flowchart illustrating the surfactant-enhanced permeation displacement parameter optimization method based on core-scale simulation provided in this application embodiment;

[0019] Figure 2a A schematic diagram of the discrete velocity model provided in the embodiments of this application;

[0020] Figure 3a The digital core model of carbonate rock provided in the embodiments of this application;

[0021] Figure 3b This is a schematic diagram illustrating the effect of surfactant concentration on the distribution of oil-water two-phase fluids provided in the embodiments of this application.

[0022] Figure 3c This is a schematic diagram illustrating the effect of surfactants on solute distribution provided in the embodiments of this application;

[0023] Figure 3d This is a schematic diagram illustrating the effect of surfactant concentration on oil recovery and relative permeability curves provided in the embodiments of this application.

[0024] Figure 3e This is a schematic diagram illustrating the effect of surfactants on Euler coefficient and interfacial area provided in the embodiments of this application;

[0025] Figure 4 A schematic diagram of the structure of the surfactant-enhanced permeation displacement parameter optimization device based on core-scale simulation provided in this application embodiment;

[0026] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0027] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0029] To clearly understand the technical solution of this application, the existing technical solutions will first be described in detail. my country has abundant ultra-low permeability tight / shale oil resources, widely distributed in key basins such as Ordos, Junggar, Songliao, and Bohai Bay, which are strategic alternative energy sources to ensure energy security. These oil reservoirs generally have micro-nano-scale pores and extremely low permeability, making it difficult to achieve effective displacement through conventional water injection development. Currently, the industry mainly uses large-scale volumetric fracturing technology with horizontal wells to construct artificial fracture networks, combined with water injection huff and puff development to increase crude oil production. Among these, capillary pressure-based permeation is the core mechanism of water injection huff and puff oil production after fracturing. However, in actual development, the permeation process is affected by the displacement pressure difference, and the fracturing fractures are prone to water channeling, resulting in low crude oil utilization within the matrix. How to synergistically leverage permeation and displacement in a multi-scale matrix-fracture system and improve the efficiency of crude oil utilization in the matrix has become a key issue in improving the recovery rate of ultra-low permeability tight / shale oil reservoirs.

[0030] To address the aforementioned technical challenges, surfactants, with their amphiphilic molecular structure, have demonstrated significant application potential in the development of ultra-low permeability to tight reservoirs. Through synergistic effects such as reducing oil-water interfacial tension and modifying rock wettability, surfactant-containing fracturing fluids can optimize permeation and displacement, thereby facilitating crude oil recovery. Simultaneously, with the development of computer technology, digital core modeling, artificial intelligence image processing, and parallel computing are gradually being applied to reservoir numerical simulation, providing technical support for constructing core-scale permeation simulation methods.

[0031] Figure 1 This is a schematic diagram illustrating a scenario for the surfactant-enhanced permeation displacement parameter optimization method based on core-scale simulation provided in this application embodiment. Figure 1 As shown, the specific application scenarios of this application include: receiving device 101, processing device 102 and display device 103.

[0032] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the method for optimizing surfactant-enhanced permeation displacement parameters based on core-scale simulation. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0033] In the specific implementation process, the receiving device 101 can be an input / output interface or a communication interface, used to receive preprocessing data of real core samples of the target reservoir, reservoir characteristic data of the target reservoir, synergistic permeation characteristics data of surfactants, and initial boundary condition data required in the simulation process, so as to provide data input support for subsequent construction of three-dimensional digital core model, establishment of synergistic permeation model and iterative solution.

[0034] The processing device 102 can process real core data based on the data acquired by the receiving device 101 using image processing and modeling algorithms to construct a three-dimensional digital core model of the target reservoir; select suitable surfactant types according to reservoir characteristics; establish a surfactant-enhanced permeation model at the core scale by combining the surfactant-enhanced permeation characteristics and oil-water two-phase flow characteristics; iteratively solve the enhanced permeation model through parallel computing to realize the dynamic simulation of the enhanced permeation process at the core scale, and output the distribution data of pressure field, fluid saturation field and surfactant concentration field inside the three-dimensional digital core model; finally, determine the target parameter combination for surfactant-enhanced permeation based on the distribution data analysis.

[0035] The display device 103 can be used to display key results output by the processing device 102, including the pore structure visualization image of the three-dimensional digital core model, the iterative solution process curve of the enhanced permeation displacement model, the distribution map of the pressure field / fluid saturation field / surfactant concentration field obtained by dynamic simulation, and the specific values ​​of the final determined surfactant enhanced permeation displacement target parameter combination.

[0036] The display device can also be a touch screen, used to receive user commands while displaying the above content, so as to realize the operation interaction with the user.

[0037] Figure 2 This is a flowchart illustrating the surfactant-enhanced permeation displacement parameter optimization method based on core-scale simulation provided in this application embodiment. Figure 2 As shown, the method includes:

[0038] S201: Select real core samples from the target reservoir, preprocess the real core samples, and construct a three-dimensional digital core model of the target reservoir.

[0039] Specifically, real core samples from the target reservoir are selected, and after preprocessing the real core samples, a three-dimensional digital core model of the target reservoir is constructed, including steps Sa1~Sa5:

[0040] Sa1: Select real core samples from the target reservoir, clean and dry the real core samples to obtain pre-treated real core samples.

[0041] Specifically, the research focuses on target reservoirs, such as ultra-low permeability tight reservoirs, shale reservoirs, or carbonate reservoirs. Real core samples representative of the reservoir's characteristics are selected to ensure that the pore structure and mineral composition of the real core samples are consistent with the actual reservoir. The real core samples are first cleaned with anhydrous ethanol or other suitable cleaning agents to remove residual crude oil, impurities, and contaminants. After cleaning, the core samples are placed in an oven and dried to constant weight at a preset temperature, such as 60℃-80℃, to eliminate free water in the pores. This ensures clear visualization of the pore structure during subsequent scanning, ultimately yielding pre-treated real core samples free of impurities and water residue.

[0042] Sa2: Multi-scale scanning of pre-processed real core samples generates a series of grayscale images reflecting the pore structure distribution of the pre-processed real core samples.

[0043] Specifically, a high-resolution X-ray microfocus CT system was used to perform multi-scale high-precision imaging scans on the pre-processed real core samples. The scanning resolution was determined according to the pore size of the target oil reservoir. For example, for ultra-low permeability reservoirs with micro-nano pores, the resolution could be set to 3.0 μm / pixel or less. During the scanning process, images were continuously acquired along the core axis to generate a series of grayscale images covering the entire core sample. Different grayscale values ​​in the grayscale images correspond to different components of the core.

[0044] Sa3: Based on the correlation between each pixel and its adjacent similar pixels in a series of grayscale images, a nonlocal mean filtering algorithm is used for weighted calculation to obtain the filtered grayscale image.

[0045] Specifically, to address potential noise interference in a series of grayscale images, a nonlocal mean filtering algorithm is used for image denoising optimization. This reduces image noise while preserving details of core pores and fractures, resulting in smoother grayscale images and clearer pore structure boundaries. In the nonlocal mean filtering preprocessing, the filter value for each pixel is calculated by weighting all pixels similar to that pixel in the entire image. This results in a smoother image with more complete information retention. The calculation expression for nonlocal mean filtering is:

[0046]

[0047]

[0048] In the formula, Represents the filter value of a pixel; This represents the pixel value at pixel q; Let f represent the Euclidean distance between neighboring blocks centered at points p and q, where f is a monotonically decreasing function, and the value of f increases as the Euclidean distance increases. C(p) represents the normalization coefficient. The formula for calculating the Euclidean distance is:

[0049]

[0050] In the formula, This represents a neighborhood block centered at pixel p with a side length of 2f+1; This represents a neighborhood block centered at pixel q with a side length of 2f+1; This represents the pixel value at position j surrounding p within the neighborhood block; This represents the pixel value at position j surrounding q within the neighborhood block; This means taking the difference and square of the pixel values ​​at corresponding positions in two neighboring blocks, and then adding them all together. The larger this sum is, the less similar the two neighboring blocks are.

[0051] Sa4: Input the filtered grayscale image into the image segmentation model based on a fully convolutional neural network, and output the segmented structural image.

[0052] Specifically, the U-Net image segmentation model based on a fully convolutional neural network is used to segment the filtered grayscale image. The model has a U-shaped structure, including an encoder and a decoder. The encoder extracts features from the grayscale image through convolutional layers, batch normalization layers, activation functions, and max pooling layers, progressively compressing the image size and increasing the feature dimension, such as extracting features through convolutional kernels with 32, 64, 128, 256, and 512 channels in sequence. The decoder upsamples the feature map through deconvolutional layers and concatenates it with the feature map of the corresponding scale from the encoder to achieve feature fusion, ultimately outputting a pixel-level segmentation result with the same size as the input grayscale image. During the segmentation process, the model accurately classifies the pixels in the grayscale image into three categories: rock skeleton, pores, and cracks, generating a binary or multi-valued structural image containing only these three types of structures, clearly distinguishing the spatial locations of different components inside the rock core.

[0053] Sa5: Along the axial direction of the multi-scale scan, the segmented structural images are fused in three-dimensional space and reconstructed into a mesh to generate a three-dimensional digital core model.

[0054] Specifically, based on the axial sequence of multi-scale scanning, i.e. the length direction of the core sample, the segmented series of structural images are arranged in the order of scanning. The moving cube algorithm is used to perform three-dimensional spatial fusion on the arranged two-dimensional structural images. By extracting the spatial correspondence of pores and fractures in adjacent images, a continuous three-dimensional pore-skeleton structure is constructed. After fusion, the three-dimensional structure is reconstructed into a mesh, which is discretized into a three-dimensional mesh that meets the requirements of subsequent numerical simulation. Finally, a three-dimensional digital core model that can completely reflect the microscopic pore structure and fracture distribution of the target reservoir core is generated.

[0055] S202: Select a surfactant based on the reservoir characteristics of the target oil reservoir.

[0056] Specifically, key reservoir characteristic parameters of the target oil reservoir are obtained through core experiments and well logging data, including: rock wettability, formation water salinity, crude oil viscosity, pore structure characteristics, and formation temperature. Based on these reservoir characteristic parameters, the core performance indicators of surfactants are determined. If the reservoir is an oleophilic core (e.g., contact angle > 90°), a surfactant with wettability reversal ability should be selected, requiring it to modify the rock surface to be weakly hydrophilic; for high-salinity formations (e.g., salinity > 90°), a surfactant with wettability reversal ability should be selected. Salt-tolerant surfactants are preferred to avoid surfactant precipitation due to salt sensitivity. For high-viscosity crude oil (viscosity > 50 mPa·s), surfactants with strong emulsifying capabilities are required. Surfactants with adequate thermal stability are selected based on formation temperature; for example, surfactant concentration retention > 90% after 30 days of settling at formation temperature. Three to five candidate surfactants are initially screened. The interfacial tension between the surfactant solution and the target reservoir crude oil at different concentrations and the adsorption amount on the core surface are measured. Reservoir conditions are simulated using a sand-filled tube model to evaluate the improvement in oil recovery by the candidate surfactants. Based on the comprehensive experimental results, the surfactant type with the best performance is selected.

[0057] S203: Based on the synergistic seepage dispersing characteristics of surfactants in the porous media of the target reservoir and the two-phase flow characteristics of oil and water, a surfactant-enhanced seepage dispersing model at the core scale is established.

[0058] Specifically, the mathematical description of the synergistic physicochemical phenomena of surfactants in porous media, including wetting modification, solute diffusion, adsorption / retention, etc., is coupled with the oil-water two-phase flow equation and initial and boundary conditions to establish a mathematical model for surfactant-synergistic permeation.

[0059] Specifically, based on the synergistic seepage dispersing characteristics of surfactants in the porous media of the target reservoir and the oil-water two-phase flow characteristics, a surfactant-enhanced seepage dispersing model at the core scale is established, including steps Sb1~Sb7:

[0060] Sb1: Determine the synergistic permeation displacement characteristics of surfactants in the porous media of the target reservoir, including the modification parameters of surfactant on the wettability of rock surfaces in the porous media, the diffusion coefficient of surfactant in the fluid, and the adsorption amount and adsorption rate of surfactant on rock surfaces in the porous media.

[0061] Specifically, core samples with pore media consistent with the target reservoir were selected, and the initial and dynamic contact angles of core thin sections in surfactant solutions of different concentrations were measured using a contact angle measuring instrument under reservoir temperature and pressure conditions. Static diffusion experiments were used to detect the concentration changes of surfactant solution and formation water. Static adsorption experiments were conducted by mixing core particles with surfactant solution and measuring solution concentration at regular intervals.

[0062] Sb2: Determine the oil-water two-phase flow characteristics in the porous medium of the target reservoir. The oil-water two-phase flow characteristics include the initial oil-water interfacial tension, the oil-water viscosity ratio, and the basic relative permeability of the porous medium to oil and water.

[0063] Specifically, the initial interfacial tension between crude oil and formation water was measured using a spin drop interfacial tensiometer under reservoir formation conditions; the dynamic viscosity of crude oil and formation water was measured using a rotational viscometer; and core flow experiments were conducted to obtain basic seepage data of oil-water two-phase flow.

[0064] Sb3: Based on the characteristics of oil-water two-phase flow, the governing equations describing the flow of incompressible fluids are determined as the core equations for the characteristics of oil-water two-phase flow.

[0065] Specifically, the incompressible Navier-Stokes equations are chosen to describe the oil-water two-phase flow process within a porous medium. For simplicity, all formulas use lattice units, where lu is defined as the lattice length unit, ts as the lattice time unit, and mu as the lattice density unit. The expression for the incompressible Navier-Stokes equations is as follows:

[0066]

[0067]

[0068] In the formula, Represents the divergence operator; Represents macroscopic flow velocity, lu / ts. This indicates that the mass of a fluid is conserved during its flow. It is the partial derivative with respect to time, representing the rate of change of fluid velocity with time; This indicates the tendency of a fluid to maintain its original state of motion. The density of a fluid is expressed in mu; pressure is expressed in mu / ts. 2 ; This represents the pressure gradient, i.e., how pressure changes in space. Describes the driving effect of pressure on fluid flow; v represents dynamic viscosity. ; Describe the effect of internal friction caused by fluid viscosity on flow velocity.

[0069] Sb4: Based on the modification parameters of surfactant on the wettability of rock surface in porous media, a wettability modification sub-model is constructed by combining a pre-set adsorption model. The wettability modification sub-model characterizes the dynamic relationship between surfactant concentration and changes in the wettability of rock surface in porous media.

[0070] Specifically, the adsorption and desorption reaction model of solute on the solid surface is defined as follows:

[0071]

[0072] In the formula, It is an ionic component in water. It is the surface of a rock. These are solutes adsorbed on the surface. Surfactants adsorb onto rocks to form... ,at the same time It will also desorb and become again. and According to the Langmuir adsorption model, the adsorption rate at equilibrium is:

[0073]

[0074] In the formula, Indicates adsorption rate; Represents the equilibrium constant; This indicates the ion concentration of the surfactant.

[0075] The adsorption rate at equilibrium is:

[0076]

[0077] In the formula, k is the reaction rate constant. ; This indicates the adsorption tendency of high-concentration surfactants in solution towards unsaturated adsorption sites; This indicates the tendency of the adsorbed solute to desorb due to equilibrium requirements. When v>0, the overall behavior is adsorption; when v<0, the overall behavior is desorption; and when v=0, adsorption reaches equilibrium.

[0078] Specifically, when setting constant concentration boundary conditions Maximum adsorption rate The calculation formula is as follows:

[0079]

[0080] The normalized equilibrium adsorption rate is:

[0081]

[0082] At each time step, the local wetting angle is applied according to the following linear equation:

[0083]

[0084] In the formula, Indicates the initial contact angle. This indicates the minimum contact angle.

[0085] Specifically, based on the adsorption-desorption reaction, the reaction mechanism of surfactants on rock surfaces is described. The measured equilibrium adsorption rate is introduced, and combined with the relevant parameters of the calibrated adsorption amount, a dynamic correlation between surfactant concentration, adsorption rate, and contact angle is established. That is, as the surfactant concentration increases, the adsorption rate increases, and the contact angle changes from the initial contact angle to the minimum contact angle, ultimately forming a wettability modification sub-model to achieve quantitative characterization of the wettability of surfactant-modified rocks.

[0086] Sb5: Based on the diffusion coefficient of surfactants in fluids, a diffusion sub-model is constructed in combination with the fluid flow velocity field. The diffusion sub-model characterizes the convection-diffusion transport process of surfactants in porous media as they flow with the fluid.

[0087] Specifically, the convection-diffusion equation describing solute transport is:

[0088]

[0089] In the formula, It refers to ion concentration. ; It is the diffusion coefficient. ; This represents the rate of change of ion concentration over time. This indicates the transport of solutes as they flow through the fluid. This indicates the process by which solutes spontaneously diffuse from areas of high concentration to areas of low concentration due to uneven solute concentration.

[0090] Specifically, in the scenario of surfactant-enhanced permeation, the above convection-diffusion equation is used to simulate the migration process of surfactants in the reservoir porous medium as the fluid flows and due to their own concentration difference.

[0091] Sb6: Based on the adsorption amount and adsorption rate of surfactants on the rock surface in porous media, an adsorption retention sub-model is constructed in combination with the initial oil-water interfacial tension. The adsorption retention sub-model characterizes the quantitative correlation between the dynamic adsorption-desorption process of surfactants on the rock surface in porous media and the change of oil-water interfacial tension.

[0092] Specifically, the main mechanisms of surfactant action include adsorption, reduction of interfacial tension, and alteration of wettability. When surfactants are added to formation water, a description of the reduction of interfacial tension needs to be added during the displacement process. At each time step, the oil-water interfacial tension is set according to the surfactant concentration. Based on the Langmuir equation of state, the adsorption-retention sub-model of the surfactant in the two-phase interface region is defined as follows:

[0093]

[0094] In the formula, This indicates the oil-water interfacial tension under surface-active loading. ; Indicates the initial interfacial tension; This indicates the maximum concentration value of the surfactant. R represents the gas constant. T represents the thermodynamic temperature (K); C represents the current surfactant concentration. .

[0095] Sb7: Based on the wettability modification sub-model, the diffusion sub-model, the adsorption retention sub-model, and the core equation, a surfactant-enhanced permeation model at the core scale is obtained.

[0096] Specifically, using the incompressible Navier-Stokes equations as the core flow framework, we couple parameters of a wettability modification sub-model, a diffusion sub-model, and an adsorption-retention sub-model. The wettability modification sub-model applies a local wetting angle to output the contact angle, which in turn regulates the interaction between the fluid and the wall in the porous medium, affecting the calculation of the standard incompressible Navier-Stokes equations. The diffusion sub-model is based on the surfactant concentration field output by the convection-diffusion equations. This provides concentration inputs for the adsorption retention sub-model and the wettability modification sub-model. The interfacial tension output by the adsorption retention sub-model adjusts the interaction strength between the oil and water phases, further affecting the flow characteristics described by the incompressible Navier-Stokes equations. At the same time, initial conditions and boundary conditions are introduced, ultimately forming a core-scale surfactant-enhanced permeation model that couples flow, diffusion, adsorption, and wettability modification.

[0097] S204: Iteratively solves the enhanced permeation displacement model through parallel computing to achieve dynamic simulation of the enhanced permeation displacement process at the core scale, and outputs the distribution data of pressure field, fluid saturation field and surfactant concentration field inside the three-dimensional digital core model.

[0098] Specifically, based on the extended multiphase flow lattice Boltzmann method, the mathematical model of surfactant-enhanced displacement is numerically discretized. Given reasonable initial and boundary conditions, the Navier-Stokes equations for oil-water two-phase flow and the surfactant concentration convection-diffusion equations are iteratively solved. The accuracy of the surfactant-enhanced displacement mathematical model is verified by comparison with dynamic displacement experiments using variable-diameter capillaries. The specific solution method is as follows: At each time step, the collision and migration process of microscopic particles with adjacent particles in a regular lattice, such as the D2Q9 or D3Q19 model, is simulated first using the multiphase multiphase flow Shan-Chen lattice Boltzmann method. The Navier-Stokes equations for oil-water two-phase flow are approximated to obtain macroscopic values, such as average velocity and oil-water distribution. Then, the surfactant convection-diffusion equations are solved to obtain the solute concentration field. Finally, the program returns to the main program to calculate the next time step, iterating repeatedly until the iteration convergence condition is met.

[0099] Specifically, the enhanced permeation displacement model is iteratively solved through parallel computing to achieve dynamic simulation of the enhanced permeation displacement process at the core scale, and outputs the distribution data of pressure field, fluid saturation field and surfactant concentration field inside the three-dimensional digital core model, including steps Sc1~Sc4:

[0100] Sc1: Decompose the 3D digital core model into multiple sub-regions according to the spatial domain, and assign each sub-region to each computing node.

[0101] Specifically, based on the spatial dimensions of the three-dimensional digital core model and the number of parallel computing nodes, a spatial uniform partitioning method is adopted to divide the core's computational region into several sub-regions along the coordinate axes, ensuring that the number of grids in each sub-region is similar, thereby achieving a balanced distribution of computational load.

[0102] Sc2: Based on the enhanced permeation displacement model, each computing node independently iteratively solves the core equations of oil-water two-phase flow characteristics, surfactant convection-diffusion equations, and wettability modification coupling equations in the corresponding sub-regions, obtaining the local pressure field, fluid saturation field, and surfactant concentration field in each sub-region.

[0103] Sc3: Repeat the iterative solution process until the enhanced permeation model converges.

[0104] Specifically, the process of independently iteratively solving the core equations for the oil-water two-phase flow characteristics, the surfactant convection-diffusion equation, and the wettability modification coupling equation within the corresponding sub-region until the enhanced permeation displacement model converges includes the following steps:

[0105] Sd01: The core equations for the characteristic flow of oil and water are numerically discretized to obtain the particle distribution function, equilibrium distribution function, and discrete velocity model of each fluid component.

[0106] Specifically, the multiphase, multi-component Shan-Chen lattice Boltzmann method is used to simulate oil-water two-phase flow. The particle distribution function of each fluid component is defined as:

[0107]

[0108] In the formula, Represents discrete velocity, lu / ts; This represents the particle distribution function after migration, at position. Along discrete velocity move Distance, time from advancement Afterwards, fluid components. The particle distribution along the discrete velocity direction i; Indicates the location before migration. At time t, fluid composition The particle distribution function along direction i; Represents the collision term, where Indicates fluid composition In position The equilibrium distribution function along direction i at time t; Indicating thermodynamic equilibrium, The theoretical distribution of component particles along direction i; This indicates the non-equilibrium deviation between the current distribution and the equilibrium distribution; Indicates fluid composition The relaxation time is dimensionless. The relaxation time is related to the fluid composition. The relationship between dynamic viscosity and dynamic viscosity is as follows:

[0109]

[0110] In the formula, Represents the speed of sound in a lattice. .

[0111] The equilibrium distribution function of each fluid component is defined as follows:

[0112]

[0113] In the formula, This represents the weight coefficient for each direction, dimensionless; in the D2Q9 model, w0 = 4 / 9, w 1-4 =1 / 9, w 5-8 =1 / 36, in the D3Q19 model, w0=1 / 3, w 1-6 =1 / 18, w 7-18 =1 / 36; ρ is the macroscopic density. ; Indicates fluid composition In position The equilibrium distribution function along direction i at time t; Indicates fluid composition Macroscopic density; Represents discrete velocity; Indicates fluid composition Macroscopic speed; Represents the speed of sound in a lattice. .

[0114] Among them, fluid components The macroscopic density is defined as:

[0115]

[0116] fluid components The formula for calculating the macroscopic velocity is:

[0117]

[0118] In the formula, Indicates components Interparticle interaction forces The composite velocity is defined as:

[0119]

[0120] Components Interparticle forces include cohesive forces between fluids and surface forces between the fluid and the wall. The formula for calculating the interaction force between particles is:

[0121]

[0122] In the formula, This represents the cohesive force between fluids; This represents the surface force between the fluid and the wall. It acts on the components. The cohesive force within is defined as:

[0123]

[0124] In the formula, It represents a coefficient that controls the intensity of the interaction between different components.

[0125] Acting on components The internal surface force is defined as:

[0126]

[0127] In the formula, Indicates the action at position Fluid composition at time t The surface force on the wall, G represents the intensity coefficient of the fluid-wall interaction. Indicates position Fluid composition at time t Macroscopic density, mu / lu 3 ; The weighting coefficients represent the discrete velocity direction i; A function representing the virtual density of the wall; The virtual density of the wall, mu / lu 3 ; Indicates an indicator function, The location is a solid lattice point, s=1. The location is a fluid grid point, s=0; Represents discrete velocity; This represents the time step. The intensity of the interaction between the fluid and the wall can be adjusted using virtual density, thereby controlling the contact angle.

[0128] Specifically, the discrete velocity model adopts the DnQb model, where D represents the spatial dimension and Q represents the discrete velocity direction. Figure 2a This is a schematic diagram of a discrete velocity model provided in an embodiment of this application. Figure 2a As shown, the left figure is the D2Q9 model, and the right figure is the D3Q19 model. In the D2Q9 model, the discrete velocity is:

[0129]

[0130] In the D3Q19 model, the discrete velocity is:

[0131]

[0132] Sd02: Numerical discretization of the wettability modification sub-model yields the correction coefficients for particle interaction strength.

[0133] Specifically, for use in components With surface forces as the core, the relationship between contact angle and virtual density was experimentally calibrated. When the surfactant concentration increases and the contact angle decreases, the virtual density is increased to enhance the attraction between the fluid and the wall. When the surfactant concentration decreases and the contact angle increases, the virtual density is decreased to reduce the attraction between the fluid and the wall. The virtual density is used as a correction coefficient for the particle interaction strength and is discretized and stored in each wall grid node of the three-dimensional digital core model.

[0134] Sd03: Numerical discretization of the diffuser model yields the particle distribution function of the solute particles of the surfactant, the equilibrium distribution function of the solute particles, and the association rule of the solute particle diffusion term that matches the diffusion coefficient of the surfactant in the fluid.

[0135] Specifically, the particle distribution function, discrete velocity model, and equilibrium distribution function of the solute particles are similar to those of the single-phase flow model. Furthermore, fewer discrete velocity directions, such as D2Q5, and a simpler equilibrium distribution function can be chosen for evolution calculations. The example distribution function of the solute particles is defined as follows:

[0136]

[0137] Sd04: Numerical discretization of the adsorption retention sub-model yields the adjustment parameters for the fluid-fluid particle interaction force and the fluid-rock wall particle interaction force.

[0138] Specifically, based on the correlation between surfactant adsorption-desorption and interfacial tension and wall wettability in the adsorption retention model, the lattice Boltzmann method is used for discretization. The surfactant concentration is coupled with the oil-water interfacial tension, and then, according to the fluid cohesion formula, the change in interfacial tension is transformed into the fluid-fluid particle interaction strength coefficient. The adjustment parameters of the fluid-fluid particle interaction force are obtained by adjusting the fluid-fluid particle interaction force. At the same time, the adsorption amount of surfactant on the rock surface is calculated according to the Langmuir adsorption model, the virtual density of fluid-rock wall interaction is corrected, and the adjustment parameters of fluid-rock wall particle interaction force are obtained.

[0139] Sd05: Based on the reservoir characteristics of the target reservoir and the three-dimensional digital core model, set the initial and boundary conditions of the enhanced permeability displacement model. The initial conditions include the initial pressure, initial oil-water saturation and initial surfactant concentration within the three-dimensional digital core model. The boundary conditions include the constant surfactant concentration boundary at the inlet of the enhanced permeability displacement model and the pressure boundary at the outlet.

[0140] Specifically, based on the reservoir characteristics such as porosity and permeability of the target reservoir and the grid structure of the three-dimensional digital core model, the initial conditions are set as follows: the initial pressure of all grids in the core is the original formation pressure of the reservoir, the initial oil phase saturation is 0.8, the water phase saturation is 0.2, and the initial surfactant concentration is 0 throughout the entire region except for the inlet; the boundary conditions are: a constant surfactant concentration is applied at the inlet, the outlet is set as a pressure outlet boundary, and the core wall is set as a no-slip boundary to constrain fluid flow.

[0141] Sd06: Based on the particle distribution function of each fluid component, the equilibrium distribution function of each fluid component, the discrete velocity model, the correction coefficient of particle interaction intensity, the particle distribution function of solute particles, the equilibrium distribution function of solute particles, the correlation rule of solute particle diffusion term, the adjustment parameters of fluid-fluid particle interaction force and fluid-rock wall particle interaction force, as well as the initial conditions and boundary conditions, an iterative surfactant-enhanced permeation dispersion model is constructed.

[0142] Specifically, by integrating the particle distribution function, equilibrium distribution function, discrete velocity model, particle interaction intensity correction coefficient, solute particle distribution function, equilibrium distribution function, diffusion term association rule, and fluid-fluid and fluid-wall particle interaction force adjustment parameters, and loading initial and boundary conditions, and coupling the collision and migration processes through the lattice Boltzmann method, an iterative surfactant-enhanced permeation dispersive discrete model containing flow, diffusion, adsorption, and wettability modification is constructed.

[0143] Sd07: Based on an iterative surfactant-enhanced permeation dispersion model, the collision, recoloring, and migration processes of microscopic particles are simulated within each iteration time step. The core equations of the discretized oil-water two-phase flow characteristics are solved to obtain the macroscopic flow velocity and oil-water two-phase distribution within the three-dimensional digital core model.

[0144] Specifically, at each iteration time step, based on the iterable discrete model, the fluid particle collision process is first simulated, then the recoloring process is performed, followed by the particle migration process, and the discrete oil-water two-phase flow core equation is solved to obtain the macroscopic flow velocity vector field and oil and water phase density distribution in the core, i.e., the oil-water two-phase distribution.

[0145] Sd08: Calculate the oil-water saturation at each iteration time step based on the oil-water two-phase distribution.

[0146] Specifically, based on the oil phase density and water phase density of the oil-water two-phase distribution, the linear correlation between saturation and phase density is used to calculate the oil and water phase saturation at each point of the core grid at the current iteration time step, thereby generating a saturation field.

[0147] Sd09: Using macroscopic flow velocity as the convection driving condition, based on the particle distribution function of solute particles, the equilibrium distribution function of solute particles, and the correlation rule of solute particle diffusion term, the surfactant convection-diffusion equation corresponding to the discretized diffuser model is solved to obtain the surfactant concentration distribution in the three-dimensional digital core model.

[0148] Specifically, using the output macroscopic flow velocity as the convection driver, and based on solving the discretized convection-diffusion equation, the solute particle collision process is first executed. According to the solute particle distribution function after the collision, the current macroscopic flow velocity and solute concentration are substituted to calculate the equilibrium distribution function. Then, the solute particle migration process is executed, migrating according to the D2Q5 discrete velocity model, with boundary processing logic consistent with that of fluid particles. After the migration is completed, the surfactant concentration of each grid node is calculated. If surfactant adsorption exists in the grid, i.e., v>0, the concentration loss corresponding to the adsorption amount is deducted from the solute concentration, and finally the surfactant concentration distribution in the three-dimensional digital core model is obtained.

[0149] Sc4: Integrates the local pressure field, fluid saturation field, and surfactant concentration field of each sub-region output by each computing node to obtain the distribution data of the pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model.

[0150] Specifically, the grid data corresponding to each sub-region is extracted and transmitted via a parallel data transmission mechanism, such as MPI (Message Passing Interface) based on distributed memory. Utilizing MPI's communication function, edge data such as pressure, fluid saturation, and surfactant concentration at the edges of each sub-region are transmitted to processes in adjacent sub-regions, allowing each process to update its local edge data upon receiving neighboring data. Based on the stability criterion of the flow equation, the simulation time step for all sub-regions is uniformly calibrated to ensure that each sub-region uses the same time step. Then, the process of sub-region intra-region displacement simulation → edge data transmission → local edge data update is continuously executed in a loop, advancing the simulation step by step to gradually obtain the pressure field, fluid saturation field, and surfactant concentration field of each sub-region changing over time. The preset termination conditions of the displacement simulation are monitored in real time, such as when the simulation time reaches the target displacement duration or the rate of change of displacement efficiency falls below a set threshold. When the termination conditions are met, the pressure, fluid saturation, and surfactant concentration field data of each sub-region are aggregated to a designated process or storage unit through parallel data collection operations. According to the spatial arrangement order of the sub-regions in the 3D digital core, such as along the division axis, all the collected sub-region data are spatially stitched together to ensure the continuity of the grid data at the boundary of adjacent sub-regions. Finally, the complete distribution data of pressure field, fluid saturation field and surfactant concentration field covering the entire 3D digital core are obtained and stored in a format that can be used for 3D visualization and analysis.

[0151] S206: Based on the distribution data, determine the target parameter combination for surfactant-enhanced flooding. The target parameter combination is used to determine the parameters for surfactant-enhanced flooding of the target reservoir.

[0152] Specifically, characteristic parameters such as relative permeability, oil droplet interface area, and oil droplet Euler coefficient are introduced to analyze the microscopic occurrence state and targeted mobilization law of remaining oil. By changing the physicochemical properties of surfactants, rock pore structure, wettability, crude oil viscosity, and displacement conditions, and based on core-scale numerical simulation of surfactant-enhanced permeation, the influence of various dynamic and static factors on fluid migration patterns and recovery rate is clarified. This guides the understanding of the mechanism of surfactant-enhanced permeation to improve the recovery rate of complex media reservoirs such as ultra-low permeability-tight sandstone, shale, and carbonate rocks.

[0153] Specifically, based on the distribution data, the target parameter combination for surfactant-enhanced permeation removal is determined, including steps Se1~Se4:

[0154] Se1: Extract characteristic parameters reflecting the permeation dissipation effect from the distribution data.

[0155] Specifically, the following core characteristic parameters reflecting the seepage displacement effect are extracted from the pressure field, fluid saturation, and surfactant concentration field distribution data of the three-dimensional digital core model. First, the pressure difference data between the core inlet and outlet is read from the pressure field. Then, the flow rate and flow path length of the oil and water phases are obtained from the fluid saturation field. Based on Darcy's law, and combining the relationship between dynamic viscosity and kinematic viscosity, and the relationship between kinematic viscosity and lattice sound velocity, the absolute permeability of the core is calculated. The formula for Darcy's law is:

[0156]

[0157] In the formula, k is the absolute permeability of the core, μ is the dynamic viscosity coefficient, q is the flow rate, and L is the flow path length. This is pressure difference data. The relationship between dynamic viscosity and kinematic viscosity is:

[0158]

[0159] In the formula, μ is the dynamic viscosity coefficient. Let be the coefficient of kinematic viscosity. This is the density of water.

[0160] The relationship between the kinematic viscosity coefficient and the lattice sound velocity is as follows:

[0161]

[0162] In the formula, Let be the coefficient of kinematic viscosity. For relaxation time, Represents the speed of sound in a lattice. Indicates the time step.

[0163] Substituting the above formula into Darcy's law, we obtain the formula for calculating absolute permeability:

[0164]

[0165] Specifically, the absolute permeability is then converted to true permeability through a true permeability conversion process. Finally, the relative permeability of the oil phase and the relative permeability of the water phase are extracted separately using the relative permeability formula. The true permeability conversion formula is as follows:

[0166]

[0167] In the formula, This represents the true permeability of the core sample. For model dimensions, The actual size of the rock core. This represents the absolute permeability of the core sample. The formula for relative permeability is:

[0168]

[0169]

[0170] In the formula, It is a component relative penetration rate It is a component Effective penetration rate, The absolute permeability of the core. It is a component Volumetric flow rate, Components The viscosity.

[0171] Specifically, based on the spatial distribution of the oil phase in the fluid saturation field, the topological parameters of the oil phase are identified and statistically analyzed, including: the number of isolated fluid components, the number of throats connected to the network of oil droplets, and the number of closed voids in the fluid components. These parameters are then substituted into the Euler coefficients to calculate the Euler coefficients of the oil droplets, which are used to characterize the connectivity of the remaining oil. The Euler coefficients are defined as follows:

[0172]

[0173] In the formula, It is the Euler coefficient. It is the number of isolated fluid components. It refers to the number of throats connected to the network of oil droplets. It is the number of closed pores in a fluid component.

[0174] Specifically, an image recognition algorithm is used to couple the analysis of the fluid saturation field and the surfactant concentration field to locate the interface between the oil and water phases, calculate the total area of ​​the interface region, and obtain the oil droplet interface area. This parameter directly reflects the degree of contact between the remaining oil and the displacing fluid. From the time series data of the pressure field, the fluctuation amplitude of the pressure difference at different iteration time steps is calculated, such as the ratio of the maximum fluctuation value to the average pressure drop, as an indicator of the stability of the displacement pressure drop. The initial oil phase saturation and the oil phase saturation at the end of the simulation are read from the fluid saturation field, and the oil recovery rate is calculated using the following formula, where the oil recovery rate is the core indicator for evaluating the drainage effect. The formula is:

[0175]

[0176] In the formula, E represents the oil recovery rate. Indicates the initial oil phase saturation. This indicates the oil phase saturation at the end of the simulation.

[0177] Se2: Based on the characteristic parameters, the occurrence state and targeted mobilization law of the remaining oil in the three-dimensional digital core model are analyzed and determined. The occurrence state of the remaining oil is the form, distribution location and connectivity characteristics of the remaining oil in the porous medium, rock and fractures that are not effectively displaced during the surfactant infiltration process. The targeted mobilization law is the effective displacement efficiency and action mechanism characteristics of the remaining oil in different occurrence states.

[0178] Specifically, negative Euler coefficients indicate that the remaining oil exists in a continuous network within the pores connecting the fracture and the matrix, while positive Euler coefficients indicate that the remaining oil is retained as isolated droplets in micropores or oil films on rock walls. Combined with the spatial distribution data of the fluid saturation field, the specific locations of the remaining oil in different occurrence forms can be determined. Continuous network-like remaining oil, due to its relatively high oil phase permeability, is easily driven by the displacement pressure difference to achieve efficient utilization, while isolated droplets, due to their low oil phase permeability, require surfactant diffusion to achieve contact and stripping. At the same time, the degree of contact between the remaining oil and the displacement fluid is quantified by the oil droplet interface area, clarifying the mechanism by which surfactants reduce interfacial tension and affect oil film stripping and droplet dispersion.

[0179] Se3: Set the parameters to be optimized and configure multiple sets of different values ​​for each parameter. The parameters include the concentration, diffusion coefficient, and adsorption amount of surfactants, the rock pore structure parameters, reservoir wettability parameters, crude oil viscosity parameters of the target reservoir, and the inlet pressure and displacement rate in the displacement conditions.

[0180] Specifically, the parameters to be optimized are defined, and multiple sets of values ​​are configured for each parameter: the surfactant concentration is set to three normalized concentrations of 0.1, 0.2, and 0.3, and the diffusion coefficient is set to [value missing] based on the logic of the correlation between the model and the actual parameters. , , The adsorption capacity was set to 0.1, 0.3, and 0.5 mg / g rock based on the Langmuir adsorption model; the porosity of the target reservoir rock was set to 18%, 20.9%, and 23%, and the permeability was set to... , , Reservoir wettability parameters are expressed using contact angles, set at 80° (weakly oleophilic), 100° (oleophilic), and 120° (strongly oleophilic); crude oil viscosity is set at 8, 12, and 16 mPa·s; the inlet pressure in the displacement conditions is calculated using units of reference pressure field, set at 0.1, 0.2, and 0.3 mu / ts. 2 Displacement rate set to , , .

[0181] Se4: Based on the parameter variables of each group, combined with the distribution data and characteristic parameters, determine the target parameter combination for surfactant-enhanced permeation removal.

[0182] Specifically, for each set of parameter variables, combined with the distribution data and characteristic parameters of the pressure field, fluid saturation field, and concentration field, firstly, the oil recovery rate under each set of parameters is calculated based on the fluid saturation field, the stability of the displacement pressure drop is evaluated based on the pressure field fluctuation data, and the surfactant sweep efficiency is calculated through the concentration field coverage. Secondly, by comparing the evaluation indicators of all parameter combinations, the candidate combination with the highest oil recovery rate, stable pressure drop, and optimal sweep efficiency is selected. Finally, the candidate combination is adapted and adjusted according to the actual reservoir characteristics of the target oil reservoir, and the target parameter combination for surfactant-enhanced permeation is finally determined.

[0183] In summary, by constructing a three-dimensional digital core model to reconstruct the micro-nano pore-fracture structure, a realistic geometric carrier is provided for locating hidden residual oil within the matrix. Furthermore, by coupling physicochemical phenomena such as wetting modification, solute diffusion, and adsorption retention with the oil-water two-phase flow equation, the mechanism by which surfactants reduce interfacial tension and regulate rock wettability is quantitatively characterized, clarifying how they overcome capillary forces to push unrecoverable crude oil away from the pore walls. Subsequently, by using parallel computing to efficiently output the dynamic distribution of pressure, saturation, and concentration fields, and combining parameters such as Euler coefficients and relative permeability, the residual oil occurrence state is analyzed, and parameters such as surfactant concentration and displacement rate are optimized to ensure efficient surfactant penetration into the matrix region, enabling targeted utilization of crude oil previously unrecoverable due to water channeling and capillary forces. This transforms previously difficult-to-displace microporous residual oil into recoverable reserves, effectively improving the utilization rate of crude oil in complex reservoirs.

[0184] In another embodiment provided in this application, a low-permeability carbonate rock core from the Middle East is selected as the research object. X-ray microfocus CT scanning of the core is performed at a resolution of 3.0 μm / pixel. Based on a fully convolutional neural network deep learning algorithm, a three-dimensional visualized digital core model is constructed. The digital core voxel size is 200×200×200 μm. 3 With a porosity of 20.9%, the permeability was calculated to be 9.988 × 10⁻³ μm using lattice Boltzmann single-phase flow simulation. 2 It is basically consistent with the gas permeability measurement. Figure 3a The digital core model of carbonate rock provided in the embodiments of this application, such as Figure 3a As shown, the left image represents the grayscale image of the unit cell, and the right image represents the binary image obtained after segmentation correction.

[0185] Figure 3b This is a schematic diagram illustrating the effect of surfactant concentration on the distribution of oil-water two-phase fluids, as provided in an embodiment of this application. Figure 3b As shown, the normalized concentration C is compared. ls =0.1、C ls =0.2 and C ls The effect of surfactant concentration of 0.3 on the distribution of oil-water two-phase fluid was investigated. Results showed that when the surfactant concentration decreased from 0.3 to 0.2, water adhered more closely to the rock wall during transport, and the surfactant spread to different regions of the porous medium, which was beneficial for stripping oil films and oil droplets from micropores. However, when the surfactant concentration continued to decrease, the distribution of the oil-water two-phase fluid did not change significantly.

[0186] Figure 3c This is a schematic diagram illustrating the effect of surfactants on solute distribution provided in the embodiments of this application. Figure 3c As shown, concentration has little effect on solute distribution. As displacement proceeds, a large amount of solute accumulates at the inlet. Appropriately reducing the concentration can weaken this local enrichment phenomenon.

[0187] Figure 3d This is a schematic diagram illustrating the effect of surfactant concentration on oil recovery and relative permeability curves provided in the embodiments of this application. Figure 3d As shown, the left figure is the recovery rate curve. When the ion concentration decreases from 0.3 to 0.2, the recovery rate increases significantly, but when the ion concentration decreases from 0.2 to 0.1, the increase in recovery rate is not significant. For carbonate cores in a highly oil-wet state, the wetting regulation ability of surfactants is limited. The right figure is the relative permeability curve. Under high oil saturation, the oil and water phases are less affected by the surfactant concentration. As the injected water increases, the micro-regulatory effect of surfactants strengthens, and as the surfactant concentration decreases, the permeability of the oil phase decreases, while the relative permeability of the water phase changes only slightly.

[0188] Figure 3e This diagram illustrates the effect of surfactants on the Euler coefficient and interfacial area, as provided in the embodiments of this application. Figure 3e As shown, the left figure is the Euler coefficient variation curve. With increasing water saturation, the Euler coefficient of the oil phase initially decreases and then increases. When the surfactant concentration is highest, the oil phase is partially distributed as a thin film on the rock wall, and partially dispersed in the core under the influence of displacement and wetting, resulting in the oil / water interface area reaching its maximum, poor connectivity, and thus the Euler coefficient also reaching its maximum. The right figure is the interface area variation curve. When the surfactant concentration decreases, the oil / rock interface area A... os Decrease the water / rock interface area A ws The increase indicates that water is gradually stripping crude oil from the rock surface, and the oil / water interface area A... ow Increased surfactant concentration leads to enhanced relative permeability of the aqueous phase, ultimately resulting in increased oil recovery. When the surfactant concentration decreases to 0.1%, the increase in oil recovery is relatively small. Analysis suggests that in porous media such as carbonate rocks, the oil phase fluid tends to fill narrow pores, resulting in a tight connection between the oil and water phases. Therefore, the effect of wetting regulation on the oil-water interface area is relatively small, ultimately leading to a smaller change in the relative permeability of the oil and water phases. When the surfactant concentration changes, the occurrence morphology of the wetting phase affects the oil recovery.

[0189] Figure 4 This is a schematic diagram of the structure of the surfactant-enhanced permeation displacement parameter optimization device based on core-scale simulation provided in this application embodiment, as shown below. Figure 4 As shown, the surfactant-enhanced permeation displacement parameter optimization device based on core-scale simulation provided in this embodiment includes: a core model construction module 401, a surfactant selection module 402, an enhanced permeation displacement model construction module 403, an enhanced permeation displacement model solving module 404, and a parameter combination determination module 405.

[0190] The core model construction module 401 is used to select real core samples of the target reservoir, preprocess the real core samples, and construct a three-dimensional digital core model of the target reservoir.

[0191] The surfactant selection module 402 is used to select surfactants based on the reservoir characteristics of the target oil reservoir.

[0192] The enhanced permeation model construction module 403 is used to establish a surfactant-enhanced permeation model at the core scale based on the enhanced permeation characteristics of surfactants in the porous media of the target reservoir and the flow characteristics of oil-water two-phase flow.

[0193] The enhanced permeation displacement model solution module 404 is used to iteratively solve the enhanced permeation displacement model through parallel computing, realize the dynamic simulation of the enhanced permeation displacement process at the core scale, and output the distribution data of pressure field, fluid saturation field and surfactant concentration field inside the three-dimensional digital core model.

[0194] The parameter combination determination module 405 is used to determine the target parameter combination for surfactant-enhanced flooding based on the distribution data. The target parameter combination is used to determine the parameters for surfactant-enhanced flooding of the target reservoir.

[0195] In one possible implementation, the core model construction module 401 is specifically used to select real core samples from the target reservoir, clean and dry the real core samples to obtain pre-processed real core samples; perform multi-scale scanning on the pre-processed real core samples to generate a series of grayscale images reflecting the pore structure distribution of the pre-processed real core samples; perform weighted calculations using a non-local mean filtering algorithm based on the correlation between each pixel and its adjacent similar pixels in the series of grayscale images to obtain filtered grayscale images; input the filtered grayscale images into an image segmentation model based on a fully convolutional neural network to output segmented structural images; and perform three-dimensional spatial fusion and mesh reconstruction on the segmented structural images along the axial direction of the multi-scale scanning to generate a three-dimensional digital core model.

[0196] In one possible implementation, the enhanced permeation dispersing model construction module 403 is specifically used to determine the enhanced permeation dispersing characteristics of surfactants in the porous media of the target reservoir. These enhanced permeation dispersing characteristics include the modification parameters of surfactant on the wettability of rock surfaces in the porous media, the diffusion coefficient of surfactants in the fluid, and the adsorption amount and adsorption rate of surfactants on the rock surfaces in the porous media. It also determines the oil-water two-phase flow characteristics in the porous media of the target reservoir, including the initial oil-water interfacial tension, the oil-water viscosity ratio, and the basic relative permeability of the porous media to oil and water. Based on the oil-water two-phase flow characteristics, the governing equations describing the flow of incompressible fluids are determined as the core equations for the oil-water two-phase flow characteristics. Finally, based on the modification parameters of surfactant on the wettability of rock surfaces in the porous media, combined with preset adsorption parameters… A wettability modification sub-model was constructed to characterize the dynamic correlation between surfactant concentration and changes in the wettability of rock surfaces in porous media. A diffusion sub-model was constructed based on the surfactant's diffusion coefficient in the fluid, combined with the fluid flow velocity field. This sub-model characterizes the convective-diffusion transport process of surfactants in porous media. An adsorption retention sub-model was constructed based on the surfactant's adsorption amount and rate on the rock surface in porous media, combined with the initial oil-water interfacial tension. This sub-model characterizes the quantitative correlation between the dynamic adsorption-desorption process of surfactants on the rock surface in porous media and changes in oil-water interfacial tension. Based on the wettability modification sub-model, diffusion sub-model, adsorption retention sub-model, and core equations, a surfactant-enhanced permeation model at the core scale was obtained.

[0197] In one possible implementation, the enhanced permeation displacement model solving module 404 is specifically used to decompose the three-dimensional digital core model into multiple sub-regions according to the spatial domain, and assign each sub-region to each computing node; based on the enhanced permeation displacement model, each computing node independently and iteratively solves the core equations of oil-water two-phase flow characteristics, surfactant convection-diffusion equations, and wettability modification coupling equations in the corresponding sub-regions to obtain the local pressure field, fluid saturation field, and surfactant concentration field in each sub-region; the iterative solution process is repeated until the enhanced permeation displacement model converges; the local pressure field, fluid saturation field, and surfactant concentration field output by each computing node in each sub-region are integrated to obtain the distribution data of the pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model.

[0198] In one possible implementation, the parameter combination determination module 405 is specifically used to extract characteristic parameters reflecting the permeation displacement effect from the distributed data; based on the characteristic parameters, analyze and determine the occurrence state and targeted mobilization law of the remaining oil in the three-dimensional digital core model, wherein the occurrence state of the remaining oil refers to the existence form, distribution location and connectivity characteristics of the remaining oil in the porous medium of rocks and fractures that have not been effectively displaced during the surfactant permeation process; the targeted mobilization law refers to the effective displacement efficiency and mechanism characteristics of the remaining oil in different occurrence states; set the parameter variables to be optimized, and configure multiple sets of different values ​​for each parameter variable, including the surfactant concentration, diffusion coefficient, adsorption amount, rock pore structure parameters of the target reservoir, reservoir wettability parameters, crude oil viscosity parameters, and inlet pressure and displacement rate in the displacement conditions; based on each set of parameter variables, combined with the distributed data and characteristic parameters, determine the target parameter combination for surfactant-enhanced permeation displacement.

[0199] The surfactant-enhanced permeation parameter optimization device based on core-scale simulation provided in this embodiment can execute the method provided in the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0200] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. For example... Figure 5 As shown, the computer device provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the computer device further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0201] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0202] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0203] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0204] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0205] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0206] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0207] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0208] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0209] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0210] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0211] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0212] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0213] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0214] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0215] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for optimizing surfactant-enhanced permeation displacement parameters based on core-scale simulation, characterized in that, include: Select real core samples from the target reservoir, preprocess the real core samples, and then construct a three-dimensional digital core model of the target reservoir. Based on the reservoir characteristics of the target oil reservoir, a surfactant is selected; Based on the enhanced permeation characteristics of the surfactant in the porous media of the target reservoir and the oil-water two-phase flow characteristics, a surfactant-enhanced permeation model at the core scale is established. The enhanced permeation displacement model is solved iteratively through parallel computing to realize the dynamic simulation of the enhanced permeation displacement process at the core scale, and output the distribution data of pressure field, fluid saturation field and surfactant concentration field inside the three-dimensional digital core model. Based on the distribution data, the target parameter combination for surfactant-enhanced drainage is determined, and the target parameter combination is used to determine the parameters for surfactant-enhanced drainage of the target reservoir. The step of establishing a surfactant-enhanced permeation model at the core scale based on the synergistic permeation characteristics of the surfactant in the porous medium of the target reservoir and the oil-water two-phase flow characteristics includes: The enhanced permeation and drainage characteristics of the surfactant in the porous media of the target reservoir were determined, wherein the enhanced permeation and drainage characteristics included the modification parameters of the wettability of the rock surface in the porous media by the surfactant, the diffusion coefficient of the surfactant in the fluid, and the adsorption amount and adsorption rate of the surfactant on the rock surface in the porous media. The oil-water two-phase flow characteristics in the porous medium of the target oil reservoir were determined. The oil-water two-phase flow characteristics include the initial oil-water interfacial tension, the oil-water viscosity ratio, and the basic relative permeability of the porous medium to oil and water. Based on the aforementioned oil-water two-phase flow characteristics, the governing equations describing the flow of incompressible fluids are determined as the core equations for the oil-water two-phase flow characteristics. Based on the modification parameters of the surfactant on the wettability of the rock surface in the porous medium, a wettability modification sub-model is constructed in combination with a preset adsorption model. The wettability modification sub-model characterizes the dynamic correlation between the concentration of the surfactant and the change in the wettability of the rock surface in the porous medium. Based on the diffusion coefficient of the surfactant in the fluid, a diffusion sub-model is constructed in combination with the fluid flow velocity field. The diffusion sub-model characterizes the convective-diffusion transport process of the surfactant in the porous medium as it flows with the fluid. Based on the adsorption amount and adsorption rate of the surfactant on the rock surface in the porous medium, an adsorption retention sub-model is constructed in combination with the initial oil-water interfacial tension. The adsorption retention sub-model characterizes the quantitative correlation between the dynamic adsorption-desorption process of the surfactant on the rock surface in the porous medium and the change of oil-water interfacial tension. Based on the wettability modification sub-model, the diffusion sub-model, the adsorption retention sub-model, and the core equation, the surfactant-enhanced permeation displacement model at the core scale is obtained.

2. The method according to claim 1, characterized in that, The process of selecting real core samples from the target reservoir, preprocessing the real core samples, and constructing a three-dimensional digital core model of the target reservoir includes: Select real core samples from the target reservoir, and clean and dry the real core samples to obtain pretreated real core samples; Multi-scale scanning is performed on the pre-processed real core sample to generate a series of grayscale images reflecting the pore structure distribution of the pre-processed real core sample. Based on the correlation between each pixel and its adjacent similar pixels in the series of grayscale images, a nonlocal mean filtering algorithm is used to perform weighted calculations to obtain the filtered grayscale image. The filtered grayscale image is input into an image segmentation model based on a fully convolutional neural network, and the segmented structural image is output. Along the axial direction of the multi-scale scan, the segmented structural image is fused in three dimensions and reconstructed into a mesh to generate the three-dimensional digital core model.

3. The method according to claim 1, characterized in that, The enhanced permeation displacement model is iteratively solved through parallel computing to achieve dynamic simulation of the enhanced permeation displacement process at the core scale, and outputs the distribution data of pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model, including: The three-dimensional digital core model is decomposed into multiple sub-regions according to the spatial domain, and each sub-region is assigned to each computing node; Based on the enhanced permeation displacement model, each computing node independently and iteratively solves the core equations of oil-water two-phase flow characteristics, surfactant convection-diffusion equations, and wettability modification coupling equations in the corresponding sub-regions, thereby obtaining the local pressure field, fluid saturation field, and surfactant concentration field in each sub-region. Repeat the iterative solution process until the enhanced permeation displacement model converges; By integrating the local pressure field, fluid saturation field, and surfactant concentration field output by each computing node in each sub-region, the distribution data of the pressure field, fluid saturation field, and surfactant concentration field inside the three-dimensional digital core model are obtained.

4. The method according to claim 1, characterized in that, The step of determining the target parameter combination for surfactant-enhanced permeation removal based on the distribution data includes: From the distribution data, extract the characteristic parameters that reflect the seepage removal effect; Based on the aforementioned characteristic parameters, the occurrence state and targeted mobilization law of the remaining oil in the three-dimensional digital core model are analyzed and determined. The occurrence state of the remaining oil refers to its form, distribution location, and connectivity characteristics in the rocks and fractures that were not effectively displaced during the surfactant infiltration process. The targeted mobilization law refers to the effective displacement efficiency and mechanism characteristics of the remaining oil in different occurrence states. Set the parameter variables to be optimized and configure multiple sets of different values ​​for each parameter variable. The parameter variables include the concentration, diffusion coefficient, and adsorption amount of the surfactant, the rock pore structure parameters, reservoir wettability parameters, and crude oil viscosity parameters of the target reservoir, as well as the inlet pressure and displacement rate in the displacement conditions. Based on the parameter variables described in each group, and in combination with the distribution data and the characteristic parameters, the target parameter combination for enhancing the surfactant's penetration-reducing effect is determined.

5. A device for optimizing surfactant-enhanced permeation displacement parameters based on core-scale simulation, characterized in that, include: The core model construction module is used to select real core samples of the target reservoir, preprocess the real core samples, and construct a three-dimensional digital core model of the target reservoir. The surfactant selection module is used to select surfactants based on the reservoir characteristics of the target oil reservoir. The enhanced permeation model construction module is used to establish a surfactant-enhanced permeation model at the core scale based on the enhanced permeation characteristics of the surfactant in the porous medium of the target reservoir and the oil-water two-phase flow characteristics. The enhanced permeation displacement model solving module is used to iteratively solve the enhanced permeation displacement model through parallel computing, realize the dynamic simulation of the enhanced permeation displacement process at the core scale, and output the distribution data of pressure field, fluid saturation field and surfactant concentration field inside the three-dimensional digital core model. The parameter combination determination module is used to determine the target parameter combination for surfactant-enhanced flooding based on the distribution data. The target parameter combination is used to determine the parameters for surfactant-enhanced flooding of the target reservoir. The enhanced permeation displacement model construction module is specifically used to determine the enhanced permeation displacement characteristics of the surfactant in the porous media of the target reservoir. These enhanced permeation displacement characteristics include the modification parameters of the surfactant on the wettability of the rock surface in the porous media, the diffusion coefficient of the surfactant in the fluid, and the adsorption amount and adsorption rate of the surfactant on the rock surface in the porous media. It also determines the oil-water two-phase flow characteristics in the porous media of the target reservoir, including the initial oil-water interfacial tension, the oil-water viscosity ratio, and the basic relative permeability of the porous media to oil and water. Based on these oil-water two-phase flow characteristics, the governing equations describing the flow of incompressible fluids are determined as the core equations for these flow characteristics. Finally, based on the modification parameters of the surfactant on the wettability of the rock surface in the porous media, a wettability modification model is constructed using a preset adsorption model. A wettability modification sub-model is constructed, which characterizes the dynamic correlation between the concentration of the surfactant and the change in wettability of the rock surface in the porous medium. A diffusion sub-model is constructed based on the diffusion coefficient of the surfactant in the fluid, combined with the fluid flow velocity field. This diffusion sub-model characterizes the convective-diffusion transport process of the surfactant in the porous medium with the fluid flow. An adsorption retention sub-model is constructed based on the adsorption amount and adsorption rate of the surfactant on the rock surface in the porous medium, combined with the initial oil-water interfacial tension. This adsorption retention sub-model characterizes the quantitative correlation between the dynamic adsorption-desorption process of the surfactant on the rock surface in the porous medium and the change in oil-water interfacial tension. Based on the wettability modification sub-model, the diffusion sub-model, the adsorption retention sub-model, and the core equation, a surfactant-enhanced permeation model at the core scale is obtained.

6. The apparatus according to claim 5, characterized in that, The core model construction module is specifically used to select real core samples from the target reservoir, clean and dry the real core samples to obtain pre-processed real core samples. The preprocessed real core sample is scanned at multiple scales to generate a series of grayscale images reflecting the pore structure distribution of the preprocessed real core sample. Based on the correlation between each pixel and its adjacent similar pixels in the series of grayscale images, a nonlocal mean filtering algorithm is used for weighted calculation to obtain a filtered grayscale image. The filtered grayscale image is input into an image segmentation model based on a fully convolutional neural network to output a segmented structural image. Along the axial direction of the multi-scale scan, the segmented structural image is fused in three dimensions and reconstructed into a mesh to generate the three-dimensional digital core model.

7. A computer device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 4.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 4.

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