A method for analyzing the mobility of original formation water in gas reservoirs

Abstract

This invention discloses a method for analyzing the mobility of original formation water in gas reservoirs, comprising the following steps: S1, acquiring target well and target layer image data, and constructing a three-dimensional multi-pore structure model; S2, restoring the original formation conditions to the three-dimensional multi-pore structure model; S3, establishing a gas-water two-phase displacement mathematical model based on the lattice Boltzmann method, and coupling the two to obtain a coupled model; S4, determining the original state of the gas reservoir, setting evolutionary solution conditions, and performing evolutionary calculations on the coupled model based on the evolution equation to obtain a three-dimensional multi-pore structure model containing both gas and water phases; S5, performing multi-angle slice analysis, classifying and analyzing the mobility of the water phase, and obtaining the distribution of mobile water. This method does not require closed coring operations and avoids subsequent high-cost experiments such as nuclear magnetic resonance and core displacement, effectively reducing research costs, providing accurate and reliable prediction results, greatly shortening the testing and analysis cycle, and realizing the evaluation of formation water mobility under the original conditions of gas reservoirs.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir development, and in particular to a method for analyzing the mobility of original formation water in gas reservoirs. Background Technology

[0002] During the formation and development of oil and gas fields, water in the reservoir will migrate with the injection or production of oil and gas. Due to differences in reservoir properties and displacement forces, some water in the core is mobile while some is immobile. The fluid occurrence state and saturation in the reservoir are necessary indicators for formulating gas reservoir development plans. In particular, before a gas reservoir is put into development, the mobility of the original formation water and the original water saturation are crucial for calculating gas reservoir reserves and well location.

[0003] Current conventional research methods typically involve obtaining core samples from the target formation during drilling and then performing nuclear magnetic resonance (NMR) and displacement experiments to analyze the mobility of the original formation water and calculate water saturation. To ensure the fluid remains in its reservoir-like state, coring must be conducted under closed conditions. However, closed coring requires the insertion of specialized coring tools into the wellbore, significantly increasing costs and delaying the drilling cycle. The complexity of these tools also increases operational risks. Furthermore, after closed coring, NMR and core displacement experiments require sophisticated equipment and lack repeatability, further increasing costs. In displacement experiments, the experiment becomes difficult to perform when water saturation drops to a certain level. Therefore, a low-cost method for analyzing the mobility of original formation water is currently lacking. Summary of the Invention

[0004] The purpose of this invention is to overcome the problems of high cost, low efficiency and poor repeatability caused by the use of molecular methods such as nuclear magnetic resonance and core displacement to analyze the mobility of original formation water in gas reservoirs, and to provide a method for analyzing the mobility of original formation water in gas reservoirs.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] A method for analyzing the mobility of original formation water in a gas reservoir includes the following steps:

[0007] S1. Obtain target layer image data of the target well, wherein the target layer image includes image data of rock core and / or thin film made from rock cuttings of the target layer; and construct a three-dimensional multi-pore structure model based on the target layer image data using mathematical algorithms.

[0008] S2. Restore the original formation conditions to the three-dimensional multi-pore structure model. Restoring the original formation conditions involves stress correction of the pore skeleton of the three-dimensional pore structure model.

[0009] S3. Establish a gas-water two-phase displacement mathematical model based on the lattice Boltzmann method, and couple the gas-water two-phase displacement mathematical model with the three-dimensional multi-pore structure model restored in step S2, including simplifying the pore skeleton boundary inside the three-dimensional multi-pore structure model to obtain the coupled model.

[0010] S4. Determine the original state of the gas reservoir and set the evolution solution conditions. Based on the evolution equation of the gas-water two-phase displacement mathematical model, perform evolution calculation on the coupled model to simulate the gas-water displacement process and obtain a three-dimensional multi-pore structure model containing gas and water phases.

[0011] S5. Perform multi-angle slice analysis on the three-dimensional multi-pore structure model of the gas-water two-phase system, classify and analyze the mobility of the water phase, obtain the distribution of mobile water in the original strata of the gas reservoir, and realize the analysis of the mobility of water in the original strata of the gas reservoir.

[0012] The technical solution of this invention is a low-cost evaluation model and analysis method for the mobility of original formation water in gas reservoirs. This invention requires first establishing a three-dimensional multi-pore structure physical model. The established pore structure model can characterize the actual pore throat structure and scale characteristics of the reservoir. A gas-water two-phase displacement mathematical model is established based on the lattice Boltzmann method. Through processing and numerical solution of the coupled model, a visual simulation of the gas-water displacement process during reservoir formation can be achieved, ultimately obtaining the gas-water two-phase distribution characteristics under the original conditions of the gas reservoir, reconstructing the fluid distribution under the original conditions, and enabling qualitative and quantitative analysis of the distribution and proportion characteristics of the residual water phase after displacement. The closed-loop coring procedure using this invention ensures the preservation of the original fluid conditions, greatly reducing experimental costs and potential engineering risks. It also effectively solves the problems of high cost, low efficiency, and poor repeatability of experiments such as nuclear magnetic resonance and core displacement, reducing research costs while improving efficiency and accuracy, completing the evaluation of the mobility of original formation water in gas reservoirs, and thus achieving a qualitative and quantitative understanding of the mobility of formation water under the original conditions of gas reservoirs.

[0013] Furthermore, a three-dimensional multi-pore structure model is constructed based on the target layer image data using mathematical algorithms, including: processing the target layer core image data using the Otsu method to construct a three-dimensional multi-pore structure model.

[0014] Furthermore, the correction equation for stress correction of the pore skeleton of the three-dimensional pore structure model is as follows:

[0015]

[0016] In the formula: E is the Young's modulus of the rock. Poisson's ratio, , The tangential and axial stress components are given, where a represents the abstract inner diameter of the pore and b represents the abstract outer diameter of the pore. Rock mechanics parameters, such as Young's modulus, are obtained from triaxial stress experiments.

[0017] Furthermore, the evolution equation is:

[0018]

[0019] In the formula Let be the gas density distribution function along the i-th direction at grid point x in the lattice space at time t. The relaxation time is calculated by considering the effect of gas density. L is the feature length of the lattice space. For Knudsen numbers, The gas correction factor can be derived from... Find the value of b, where b is a simplified coefficient. Let x be the equilibrium distribution function of the x-grid point along the i-direction at time t. It represents the interaction potential between the gas and water phases.

[0020] Furthermore, the interaction potential between the gas and water phases can be characterized by a potential function, calculated as follows:

[0021] (3)

[0022] In the formula G For the interaction strength, It is a pseudo-potential density function;

[0023] The pseudopotential density function and surface tension expressions are as follows:

[0024] (4)

[0025] (5)

[0026] In the formula Let be the initial pseudopotential density function. For the initial density, The macroscopic density of the fluid; For the speed of sound in a grid, This is the upper limit of liquid phase density. This is the lower limit of gas phase density.

[0027] Furthermore, the simplification process for the pore skeleton boundary inside the three-dimensional multi-pore structure model includes: straightening the irregular geometric boundary conditions inside the three-dimensional multi-pore structure model.

[0028] Furthermore, determining the evolutionary solution conditions includes determining the initial parameters of the model, the convergence conditions, and the maximum number of iterations.

[0029] Furthermore, the convergence condition is set as a high-precision convergence criterion, which involves fluid flow within micro- and nano-scale pores; therefore, the velocity convergence error number is set to 10. -7 As shown in the following formula:

[0030]

[0031] In the formula, ux, uy, and uz are the velocities of the fluid in the x, y, and z directions, respectively.

[0032] The maximum number of iterations should be matched with the convergence condition setting. Based on experience, the maximum number of iterations can be set to 100,000.

[0033] Furthermore, the mobility of the aqueous phase is classified into three categories according to the attachment location of the aqueous phase: blind end or corner water mass, surface water film, and throat water column.

[0034] Furthermore, the analysis method also includes:

[0035] S6. The original water saturation of the target gas reservoir is calculated based on the distribution of movable water. The formula for calculating the water saturation is as follows:

[0036] (6)

[0037] In the formula S w V represents the water saturation level. wi V represents the volume of the aqueous mesh. bi Let n be the volume of the rock skeleton mesh, and n be the number of meshes for different media.

[0038] Another aspect of the present invention provides an electronic device comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-described method for analyzing the mobility of original formation water in a gas reservoir.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 1. This invention proposes a low-cost method for analyzing the movable water in the original formation of gas reservoirs. The method proposed in this invention does not require closed coring operations and avoids subsequent high-cost experiments such as nuclear magnetic resonance and core displacement. Overall, this invention effectively reduces research costs, provides accurate and reliable prediction results, greatly shortens the testing and analysis cycle, realizes the evaluation of formation water mobility under the original conditions of gas reservoirs, and provides qualitative and quantitative understanding of formation water mobility under the original conditions of gas reservoirs. It is particularly suitable for analyzing the distribution of movable water in the original formation of carbonate gas reservoirs.

[0041] 2. This invention achieves full-process visualization in model numerical simulation, and the simulated data is closer to the real state. Finally, the water saturation under the original formation conditions is obtained, which provides support for the submission of gas reservoir reserves and the deployment of development well locations.

[0042] 3. The analytical method of this invention focuses on the key parameters that must be obtained in the field of gas reservoir exploration and development. It can effectively serve the field of oil and gas exploration and development, has strong practicality, broad application prospects, and great promotional value. Attached image description:

[0043] Figure 1 This is a flowchart illustrating the method for analyzing movable water in the original strata of a gas reservoir.

[0044] Figure 2 The images show scanning electron microscope (SEM) core and thin section images of a carbonate gas reservoir.

[0045] Figure 3 The images show scanning electron microscope (SEM) core and thin section images of a carbonate gas reservoir.

[0046] Figure 4 Results of scanning electron microscopy and thin-section image processing and three-dimensional multi-pore structure reconstruction;

[0047] Figure 5 A schematic diagram of the "curved to straight" boundary simplification method;

[0048] Figure 6 Numerical programming solution flow for three-dimensional multiporous structures using the LBM joint method;

[0049] Figure 7 Schematic diagram of gas-driven water dynamic simulation

[0050] Figure 8 Schematic diagram of residual aqueous phase classification and evaluation model; Detailed Implementation

[0051] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0052] Example 1

[0053] This embodiment analyzes the distribution of movable water in the original strata of a carbonate gas reservoir in the Sichuan Basin, such as... Figure 1 As shown, it includes the following steps:

[0054] S1. Obtain target layer image data of the target well. The target layer image includes image data of core and / or thin sections made from rock cuttings of the target layer. Based on the target layer image data, construct a three-dimensional multi-pore structure model using mathematical algorithms. The three-dimensional multi-pore structure model includes the pore structure characteristics of the target layer reservoir.

[0055] Specifically, the steps of step S1 are as follows:

[0056] S11. Obtain image data of the target layer core and / or image data of thin sections cast from target layer cuttings. The target layer core or cuttings are taken from the target well, which is an exploited gas reservoir. The target layer core is collected in the target well using a core sampler. The core image data is obtained by examining the target layer core through a scanning electron microscope, selecting images with multiple pores and microfractures as the target layer core images. Figure 2 As shown; the thin section images of the cast body are obtained by imaging the thin sections of the cast body made from rock fragments of the target layer using a microscope, such as... Figure 3 As shown.

[0057] S12. The target layer image described in step S1 is processed using the Otsu method to obtain the pore structure feature parameters represented by the 0-1 matrix in the image.

[0058] S13. Using the pore structure characteristic parameters obtained in S12, a four-parameter stochastic growth model (QSGS) is introduced. Based on the correlation between two spatial particles as a constraint, a three-dimensional multi-pore structure model with the same statistical characteristics as the actual pore structure of the reservoir is reconstructed. The actual pore structure of the reservoir is obtained statistically from the core image obtained in S11, and the statistical characteristics are characterized by the Pearson correlation coefficient between the two points. The construction process of the three-dimensional multi-pore structure model is as follows: Figure 4 As shown.

[0059] S2. Restore the original formation conditions to the three-dimensional multi-pore structure model. Restoring the original formation conditions involves stress correction of the pore skeleton of the three-dimensional pore structure model.

[0060] The pore structure under original formation pressure can be obtained by combining the three-dimensional multipore structure model obtained in S1 with triaxial stress experimental results. Based on the triaxial stress experimental results, the pore throat structure is modified considering the original formation pressure conditions. In the stress correction, the relationship between the displacement of the skeleton boundary particles and the stress is first derived. The derivation process assumes that the strain of the pores is entirely elastic strain, following the generalized Hooke's law, resulting in the corrected equation, as follows:

[0061] (1)

[0062] In the formula, E is the Young's modulus of the target layer of rock. Poisson's ratio, , The tangential and axial stress components are given, where a is the abstract inner diameter of the pores inside the core, b is the abstract outer diameter of the pores, and r is the radial displacement. The Young's modulus and Poisson's ratio of the target layer rock were obtained from triaxial stress experiments on the S1 core, and the experimental results are shown in Table 1. The formation stress was set to the original formation pressure of 90 MPa.

[0063] Table 1. Triaxial stress test results of core samples from the target layer

[0064]

[0065] S3. Establish a gas-water two-phase displacement mathematical model based on the lattice Boltzmann method, and couple the gas-water two-phase displacement mathematical model with the three-dimensional multi-porous structure model restored in step S2, including simplifying the pore skeleton boundary inside the three-dimensional multi-porous structure model to obtain the coupled model.

[0066] In this invention, a three-dimensional multi-porous structure model is first established. By solving this model, the gas-water interaction process during gas reservoir formation is reproduced within the model, enabling real-time observation of parameters such as gas-water two-phase flow regime and saturation. Using the Shan-Chen multiphase flow model in the lattice Boltzmann method, a gas-water two-phase displacement mathematical model suitable for the aforementioned three-dimensional multi-porous structure is established. Considering gas-water two-phase seepage, an evolution equation using a multi-relaxation time collision operator is adopted, as shown in the following equation:

[0067] (2)

[0068] In the formula Let be the gas density distribution function along the i-th direction at grid point x in the lattice space at time t. The relaxation time is calculated by considering the effect of gas density. L is the feature length of the lattice space. For Knudsen numbers, The gas correction factor can be derived from... To find the value of b, where b is a simplified coefficient, we can use the formula b = 2πd. 3 Calculations are performed using / 3m. for t time x Gridline i The equilibrium distribution function of the direction. It represents the interaction potential between the gas and water phases.

[0069] The interaction potential between the gas and water phases can be characterized by a potential function, and the calculation formula is as follows:

[0070] (3)

[0071] In the formula G For the interaction strength, This is the pseudo-potential density function.

[0072] The pseudopotential density function is expressed as follows:

[0073] (4)

[0074] In the formula Let be the initial pseudopotential density function. For the initial density, The macroscopic density of the fluid.

[0075] The surface tension of the gas and water phases can be characterized by formula (5). By applying formula (5), the interaction between the two phases can be adjusted, thereby adjusting the displacement process.

[0076] (5)

[0077] In the formula For the speed of sound in a grid, This is the upper limit of liquid phase density. This is the lower limit of gas phase density.

[0078] Solving the evolution equation requires obtaining macroscopic parameters, including equilibrium velocity, pressure, and density. Density can be calculated from equilibrium velocity and pressure. The influence of two-phase forces on the gas-water two-phase flow needs to be considered. The expression for equilibrium velocity is as follows:

[0079] (6)

[0080] Pressure can be calculated using the gas-liquid two-phase equation of state in lattice space:

[0081] (7)

[0082] Before numerically solving the three-dimensional multi-porous structure model, the internal boundary conditions of the model are simplified. Because the geometric boundaries of the pore throats within the three-dimensional multi-porous structure are irregular, they greatly increase the difficulty of numerically solving the LBM model. Therefore, the key to combining two methods is to simplify the internal boundary conditions of the three-dimensional multi-porous structure. Specifically, the irregular geometric boundary conditions are "curved to straight," where curved boundaries are placed in the numerical grid and abstracted as the line connecting the center points of two adjacent grids, such as... Figure 5 As shown.

[0083] S4. Based on the original state of the gas reservoir, set the evolution solution conditions, perform evolution calculations on the coupled model based on the evolution equation of the gas-water two-phase displacement mathematical model, simulate the gas-water displacement process, and obtain a three-dimensional multi-pore structure model containing gas and water phases.

[0084] The evolutionary solution conditions include the initial parameters of the model, the convergence condition, and the maximum number of iterations. The maximum number of iterations is set to 100,000. The convergence condition is:

[0085]

[0086] In the formula, ux, uy, and uz are the velocities of the fluid in the x, y, and z directions, respectively.

[0087] The hydrodynamic conditions during the formation of crude oil and gas reservoirs are simulated using a three-dimensional multi-porous structure to address gas-driven water. The specific initial parameters for solving the model are shown in Table 1. To ensure model solvability, pressure boundary conditions are used in this method, assuming a rigid water drive throughout the process. Specifically, the initial pressure conditions are obtained from high-pressure mercury intrusion experiments; that is, the displacement pressure corresponding to 50% mercury intrusion is selected as the gas drive pressure during the reservoir formation process.

[0088] Table 1 Initial parameters of the model

[0089]

[0090] After setting initial pressure conditions for the model, a small transformation Δ is added to the variables in the evolution equation, and then evolution calculations are performed according to... Figure 6 The program is run according to the steps shown. Based on the above formulas, the equilibrium velocity, density, and pressure in the macroscopic parameters are obtained. The entire process requires calculating and storing the macroscopic parameters of the gas and water phases at a unit time step, successfully reconstructing the gas-driven water process of gas reservoir formation, and then outputting a complete image by combining the three-dimensional multi-pore structure. The gas and water distribution under different iteration steps is as follows: Figure 7 As shown.

[0091] S5. Perform multi-angle slice analysis on the three-dimensional multi-pore structure of the gas-water two-phase system, classify the mobility of the water phase, and obtain the distribution of movable water in the original strata of the gas reservoir.

[0092] Since this method evaluates the original formation water, it mainly analyzes the distribution of residual water phases in the three-dimensional multi-porous structure. After the simulation calculation, the movable water has been completely displaced from the three-dimensional multi-porous structure, and what remains inside is residual water. The residual water inside the three-dimensional multi-porous structure is considered to be the original formation water after the gas reservoir formation. This step processes the gas and water two-phase density distribution image obtained in step S3. A certain angle is selected to slice and observe the three-dimensional image. For the multi-angle slice images, a classification and evaluation model based on the attachment position of the water phase in the image is established for the residual water phase, such as... Figure 8 As shown, the mobility of water phase is divided into three categories according to the attachment location of the water phase: blind end or corner water mass, surface water film, and throat water column, so as to realize the analysis of the mobility of the original formation water in the gas reservoir.

[0093] In some embodiments, after obtaining the original formation movable water distribution of the gas reservoir, the water saturation can be calculated. The specific steps are as follows:

[0094] S6. The original water saturation of the target gas reservoir is calculated based on the distribution of movable water. The formula for calculating the water saturation is as follows:

[0095] (6)

[0096] In the formula S w V represents the water saturation level. wi V represents the volume of the aqueous mesh. bi Let n be the volume of the rock skeleton mesh, and n be the number of meshes for different media.

[0097] The water saturation can be calculated using the different media mesh volumes in the model, based on the definition of water saturation. In this embodiment, the calculation results are: blind end and corner water mass saturation is 21.6%, surface water film saturation is 14.3%, throat water column saturation is 11.8%, and total water saturation is 47.7%. This differs from the 51.3% water saturation obtained from nuclear magnetic resonance experiments. The deviation between the two methods is [missing information]. The small deviation indicates the accuracy of this method.

[0098] As can be seen from the examples, the original formation water mobility analysis method proposed in this invention can avoid closed coring and excessive large-scale laboratory experiments, while realizing the classification, qualitative and quantitative evaluation of original formation water in oil and gas reservoirs. This invention focuses on key parameters essential for gas reservoir exploration and development, and can be directly applied to obtaining key indicators such as water saturation distribution and reserves. The method proposed in this invention can effectively reduce various research costs and potential engineering risks while accurately obtaining key parameters, and has a very broad prospect for promotion in the field of gas reservoir exploration and development.

[0099] Example 2

[0100] This embodiment provides an electronic device, which includes: a memory storing executable instructions; and a processor that runs the executable instructions in the memory to implement the above-described method for analyzing the mobility of original formation water in a gas reservoir.

[0101] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0102] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0103] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0104] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the mobility of original formation water in a gas reservoir, characterized in that, Includes the following steps: S1. Obtain target layer image data of the target well, wherein the target layer image includes image data of rock core and / or thin film made from rock cuttings of the target layer; and construct a three-dimensional multi-pore structure model based on the target layer image data using mathematical algorithms. S2. Restore the original formation conditions to the three-dimensional multi-pore structure model. Restoring the original formation conditions involves stress correction of the pore skeleton of the three-dimensional pore structure model. Derive the relationship between the displacement and stress of the boundary particles of the skeleton. In the derivation process, the strain of the pores is assumed to be entirely elastic strain, following the generalized Hooke's law. The corrected equation is as follows: In the formula, E is the Young's modulus of the rock. Poisson's ratio, , Tangential and axial stress components, where a is the abstract inner diameter of the pore, b is the abstract outer diameter of the pore, and r is the radial displacement; S3, Shan in the lattice Boltzmann method Chen's multiphase flow model establishes a gas-water two-phase displacement mathematical model, and couples the gas-water two-phase displacement mathematical model with the three-dimensional multi-pore structure model restored in step S2. This includes simplifying the pore skeleton boundary inside the three-dimensional multi-pore structure model. The simplification process includes straightening the irregular geometric boundary conditions inside the three-dimensional multi-pore structure model to obtain the coupled model. S4. Determine the original state of the gas reservoir and set the evolution solution conditions. Based on the evolution equation of the gas-water two-phase displacement mathematical model, perform evolution calculation on the coupled model. Use the evolution equation of the multi-relaxation time collision operator to solve the evolution equation to obtain macroscopic parameters, including equilibrium velocity, pressure and density. Simulate the gas-water displacement process to obtain a three-dimensional multi-pore structure model of gas-water two phases. S5. Perform multi-angle slice analysis on the three-dimensional multi-pore structure model of gas-water two phases, classify the mobility of water phase, and classify the mobility of water phase into three categories according to the attachment position of water phase: blind end or corner water mass, surface water film, and throat water column, to obtain the distribution of movable water in the original strata of gas reservoir. S6. The original water saturation of the target gas reservoir is calculated based on the distribution of movable water. The formula for calculating the water saturation is as follows: In the formula S w V represents the water saturation level. wi V represents the volume of the aqueous mesh. bi Let n be the volume of the rock skeleton mesh, and n be the number of meshes for different media.

2. The method for analyzing the mobility of original formation water in a gas reservoir according to claim 1, characterized in that, The method of constructing a three-dimensional multi-pore structure model based on the target layer image data using mathematical algorithms includes: processing the target layer core image data using the Otsu method to construct a three-dimensional multi-pore structure model.

3. The method for analyzing the mobility of original formation water in a gas reservoir according to claim 1, characterized in that, The evolution equation is: In the formula Let be the gas density distribution function along the i-th direction at grid point x in the lattice space at time t. The relaxation time is calculated by considering the effect of gas density. L is the feature length of the lattice space. For Knudsen numbers, The gas correction factor can be derived from... Find the value of b, where b is a simplified coefficient. Let x be the equilibrium distribution function of the x-grid point along the i-direction at time t. It represents the interaction potential between the gas and water phases.

4. The method for analyzing the mobility of original formation water in a gas reservoir according to claim 3, characterized in that, The conditions for solving the evolution equation include determining the initial parameters of the numerical model, the convergence conditions, and the number of iterations.

5. The method for analyzing the mobility of original formation water in a gas reservoir according to claim 4, characterized in that, The maximum number of iterations is 100,000, and the convergence condition is: In the formula , , These represent the velocities of the fluid in the x, y, and z directions, respectively.

6. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes executable instructions in memory to implement the method for analyzing the mobility of original formation water in a gas reservoir as described in any one of claims 1-5.

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