Component natural gas pore network stress coupling simulation method, system and medium

Abstract

The invention discloses a component natural gas pore network stress coupling simulation method and system and a medium. Relates to the technical field of natural gas reservoir model simulation. Through a multi-component diffusion flow coupling model considering effective aperture change, real-time change of pore radius and permeability along with reservoir stress and seepage phenomena of pore change along with stress and flow coupling of multi-component gas in a pore scale are displayed, and meanwhile, aiming at the multi-component diffusion flow coupling model considering effective aperture change, the permeability of the multi-component gas is improved. A special coupling simulation method is designed, a multi-component gas pore scale seepage model is established, discretization processing and model solving are carried out on the multi-component gas pore scale seepage model in combination with throat characteristic parameters, and dynamic coupling simulation of component natural gas pore network stress is achieved. The change of the relative permeability of the fluid under different stress conditions is shown, and the adaptability of a multi-component diffusion flow coupling model is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of natural gas reservoir model simulation, and particularly relates to a component natural gas pore network stress coupling simulation method, system and medium. BACKGROUND

[0002] The development of unconventional natural gas reservoirs faces many challenges, mainly due to the heterogeneity of the rock and the complexity of the pore structure. The components of natural gas inside these reservoirs not only have a wide variety of types, but also have large differences in physical properties, which makes the micro-flow mechanism extremely complex. These factors together make the development of unconventional natural gas reservoirs more difficult, thus resulting in a low recovery rate. Therefore, in order to effectively improve the development efficiency of unconventional natural gas and provide sufficient protection for the country's energy security, researchers have gradually turned to the tool of pore network model (PNM) to in-depth study the flowability and transport characteristics of natural gas inside unconventional natural gas reservoir rocks.

[0003] The design concept of the pore network model is to simplify the complex pore structure of the rock into a network structure composed of pore bodies and throats. Such a model is between the micro-nano digital core and the meter-level Darcy model in scale, and has both interpretability and high computational efficiency, so it is widely used in micro-scale seepage mechanism research. The traditional pore network model usually uses spherical pores and circular throats as ideal geometric assumptions, and regards the gas in the natural gas reservoir as a single component. Although this simplification can capture some macroscopic seepage phenomena, it is difficult to reflect some key micro-physical phenomena.

[0004] Actual core observation shows that the shape of the throat section often presents a variety of complex shapes such as triangle, square, oval and the like. These different geometric shapes have an important influence on the fluid wetting boundary condition and the effective flow area; the wettability of the fluid is an important factor affecting the flow characteristics, and the wettability is closely related to the geometric structure of the pore. Therefore, when studying the flowability and transmission characteristics of natural gas, the influence of the pore geometric structure on the fluid behavior cannot be ignored; secondly, with the development of unconventional natural gas reservoirs accompanied by the increase of effective stress, the pore radius will also shrink; this phenomenon is very common in actual production, but the traditional pore network model usually fails to consider this dynamic change; the relationship between the pore radius and the permeability is complex, and often decays in an exponential or logarithmic manner, which cannot be fully reflected in the classical model, resulting in a large deviation between the actual prediction and the actual situation. Finally, the composition of the underground natural gas is not single, mainly composed of methane, ethane, propane, carbon dioxide and nitrogen and other natural gas components. These different components show significant behavioral differences in the micro-flow processes such as adsorption, diffusion and slip. If only relying on the classical pore network model of single-component gas and fixed geometric structure, it is difficult to accurately capture the flow characteristics of each component in the actual gas reservoir. SUMMARY

[0005] The technical problem to be solved by the present application is that the classical pore network model, the coupling simulation process and the solving process cannot reflect the relationship between the effective stress, the permeability and the pore radius, resulting in a large deviation between the simulation and the actual situation, and it is difficult to capture the flow characteristics of each component in the actual gas reservoir; the present application aims to provide a component natural gas pore network stress coupling simulation method, system and medium, which shows the real-time changes of the pore radius and the permeability with the reservoir stress, and the seepage phenomenon of the pore coupled with the multi-component gas flow at the pore scale with the stress change, and designs a special coupling simulation method for the multi-component diffusion flow coupling model with the effective pore size change, realizes the dynamic coupling simulation of the component natural gas pore network stress by establishing the multi-component gas pore scale seepage model, combining the multi-component gas pore scale seepage model with the throat characteristic parameters for discretization processing and model solving, and shows the relative permeability change of the fluid under different stress conditions, and enhances the adaptability of the multi-component diffusion flow coupling model.

[0006] The present application is realized by the following technical scheme:

[0007] The present application provides a component natural gas pore network stress coupling simulation method, comprising:

[0008] A multi-component diffusion flow coupling model considering the effective pore size change is constructed, and a multi-component gas pore scale seepage model is determined based on the law of conservation of mass;

[0009] Under the multi-component gas pore-scale seepage model, the throat characteristic parameters connecting any two pore structures are determined; the throat characteristic parameters include: the total transport flux of the component gases, the effective radius of the throat, the conductivity of the component gases, and the tortuosity of the throat.

[0010] The multi-component gas pore-scale seepage model is discretized based on the throat characteristic parameters.

[0011] The discretized multi-component gas pore-scale seepage model was solved to obtain the pore pressure and the concentration of component gases in the pores.

[0012] A further optimized scheme is that the multi-component diffusion flow coupling model with varying effective pore size includes:

[0013]

[0014] in, V represents the time derivative; p σ represents pore volume; eff Indicates effective stress; φ j Porosity; C i q represents the concentration of component gas i; i,total N represents the total adsorption amount of component gas i; i This represents the total transport flux of component gas i.

[0015] A further optimized scheme is as follows: the multi-component gas pore-scale seepage model is determined based on the law of conservation of mass, including the following method:

[0016] For any pore structure j in a multi-component diffusion flow coupling model considering changes in effective pore size, the seepage model considering stress variation is:

[0017]

[0018] in, V represents the time derivative; p,j σ represents the volume of pore structure j; eff Indicates effective stress; φ j Indicates porosity; C i,j q represents the concentration of component gas i in pore structure j; total,i,j Ω represents the total adsorption amount of component gas i in pore structure j; k represents the pore structure adjacent to pore structure j; j N represents the set of all pore structures adjacent to pore structure j; i,jk Q represents the total transport flux of component gas i between pore structure j and all adjacent pore structures; i,jFor source and sink terms, it represents a source or sink associated with component gas i in pore structure j.

[0019] A further optimization scheme involves determining the throat characteristic parameters connecting any two pore structures under a multi-component gas pore-scale seepage model; including the following method:

[0020] In the throat connecting pore structure j and pore structure k, the total transport flux N of component gas i is... i,jk for:

[0021] N i,jk =-τ i,jk (p j -p k )-ω i,jk (C i,j -C i,k )

[0022] Where r represents the radius; p j and p k C represents the pressure in pore structure j and pore structure k, respectively; i,j and C i,k τ represents the concentration of component gas i in pore structure j and pore structure k, respectively; i,jk Represents the transmission coefficient; ω i,jk Indicates the diffusion coefficient;

[0023] The effective radius r of the throat connecting pore structure j and pore structure k eff,jk for:

[0024]

[0025] Where, σ eff,jk This represents the effective stress applied outside the throat connecting pore structure j and pore structure k; q i,total,jk q represents the total adsorption amount of component gas i in the throat connecting pore structure j and pore structure k; m,i,jk This represents the maximum adsorption capacity of component gas i in the throat connecting pore structure j and pore structure k; d i The characteristic constant of component gas i is represented; nm represents the total number of mixed components in a single-phase gas.

[0026] In the throats of pore structures j and k, the conductivity g of component gas i i,jk for:

[0027]

[0028] Among them, A jk W represents the cross-sectional area of ​​the throat connecting pore structure j and pore structure k. jkσ represents the pore structure correction factor for the throat connecting pore structure j and pore structure k. eff,0,jk μ represents the initial effective stress applied outside the throat connecting pore structure j and pore structure k. jk The viscosity of component gas i in the throat connecting pore structure j and pore structure k; the tortuosity of the throat connecting pore structure j and pore structure k. for:

[0029]

[0030] in, f(W) represents the average porosity of pore structures j and k; jk ) represents the shape correction function for the throat section connecting pore structure j and pore structure k.

[0031] A further optimization scheme involves discretizing the multi-component gas pore-scale seepage model based on the throat characteristic parameters; including the following method:

[0032] Substituting the throat characteristic parameters into the multi-component gas pore-scale seepage model, we obtain the dynamic expression of the multi-component gas pore-scale seepage model.

[0033] The dynamic expression of the multi-component gas pore-scale seepage model is discretized in time based on the explicit Euler method to obtain the time-discrete form;

[0034] The pressure and concentration variables in the dynamic expression of the multi-component gas pore-scale seepage model based on the central difference method are spatially discretized to obtain the spatial variable discrete form.

[0035] The dynamic expression of the multi-component gas pore-scale seepage model is represented by time-discrete form and spatial variable-discrete form, resulting in the discretized multi-component gas pore-scale seepage model.

[0036] A further optimization scheme is that the multi-component gas pore-scale seepage model after discretization and processing includes:

[0037]

[0038] Where k represents the pore structure adjacent to pore structure j; This represents the set of all pore structures adjacent to pore structure j; n represents the current time step; n-1 represents the previous time step; This represents the transmission coefficient at the current time step; This represents the diffusion coefficient at the current time step; This represents the pressure in pore structure j at the current time step; This represents the pressure of pore structure k at the current time step; Indicates the source and sink terms at the current time step; This represents the concentration of component gas i in pore structure j at the current time step; This represents the concentration of component gas i in pore structure k at the current time step; This represents the volume of pore structure j at the current time step; This represents the porosity of pore structure j; σ represents the volume of pore structure j at the previous time step; eff Indicates effective stress; This represents the total adsorption amount of component gas i in pore structure j at the current time step; This represents the total adsorption amount of component gas i in pore structure j at the previous time step; This represents the concentration of component gas i in pore structure j; Indicates the time step.

[0039] A further optimization scheme involves solving the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of the component gases in the pores; including the following methods:

[0040] The discretized multi-component gas pore-scale seepage model is written as a matrix form B containing the relationship between pore pressure and concentration in the pores, and the coefficient matrix A is obtained.

[0041] The coefficient matrix A is decomposed into a lower triangular matrix L and an upper triangular matrix U based on the LU decomposition method, and the matrix form B is reconstructed by introducing an intermediate variable ζ.

[0042] First, the intermediate variable ζ is solved using the forward substitution method and the reconstructed matrix form B. Then, the pore pressure matrix p and the concentration matrix C of the component gases in the pores are solved using the backward substitution method.

[0043] The pore pressure and the concentration of the component gas in the pores are obtained from the pore pressure matrix p and the concentration matrix C of the component gas in the pores.

[0044] A further optimization involves employing an adaptive time step during the solution process for the discretized multi-component gas pore-scale seepage model.

[0045]

[0046] Where, Δt n+1 Indicates the step size of the next time step; Δt n The step size represents the current time step; θ total Indicates the allowable error value; θ max This represents the maximum error calculated in the current simulation.

[0047] This solution also provides a stress coupling simulation system for component natural gas pore networks, used to implement the aforementioned stress coupling simulation method for component natural gas pore networks; the system includes:

[0048] The model building module is used to construct a multi-component diffusion flow coupling model that considers the variation of effective pore size, and to determine a multi-component gas pore-scale seepage model based on the law of conservation of mass.

[0049] The parameter calculation module is used to determine the throat characteristic parameters connecting any two pore structures under a multi-component gas pore-scale seepage model. The throat characteristic parameters include: the total transport flux of the component gases, the effective radius of the throat, the conductivity of the component gases, and the tortuosity of the throat.

[0050] The discretization module is used to discretize the multi-component gas pore-scale seepage model by combining the throat characteristic parameters;

[0051] The solver module is used to solve the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of component gases in the pores.

[0052] This solution also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, can implement a stress coupling simulation method for a component natural gas pore network as described above.

[0053] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0054] 1. This invention provides a method, system, and medium for simulating stress coupling in a component natural gas pore network. By considering a multi-component diffusion-flow coupling model with varying effective pore size, it demonstrates the real-time changes in pore radius and permeability with reservoir stress, as well as the seepage phenomenon resulting from the coupling of pore size with stress and multi-component gas flow at the pore scale. Furthermore, a specialized coupling simulation method is designed for the multi-component diffusion-flow coupling model with varying effective pore size. By establishing a multi-component gas pore-scale seepage model and combining throat characteristic parameters to discretize and solve the model, dynamic coupling simulation of stress in the component natural gas pore network is achieved. This demonstrates the relative permeability changes of the fluid under different stress conditions, enhancing the adaptability of the multi-component diffusion-flow coupling model.

[0055] 2. The present invention provides a method, system and medium for stress coupling simulation of pore networks of component natural gas, which analyzes multi-component natural gas in pore networks. Compared with single-component models, the simulation of multi-component gases provides a perspective on the interaction between different component gases and more accurately describes the flow characteristics of the gas.

[0056] 3. The present invention provides a stress coupling simulation method, system and medium for component natural gas pore networks. The discretization technique can flexibly handle complex geometries and boundary conditions, thus improving the adaptability of the solution.

[0057] 4. The present invention provides a method, system and medium for stress coupling simulation of component natural gas pore network. In the process of solving the multi-component gas pore-scale seepage model, the numerical algorithm of LU decomposition is combined to improve the convergence speed, reduce the computational cost and obtain stable solutions more effectively.

[0058] 5. The present invention provides a method, system and medium for stress coupling simulation of component natural gas pore networks, which uses an adaptive time step to balance convergence and computational efficiency. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0060] Figure 1 This is a schematic diagram of the process for simulating stress coupling in the pore network of component natural gas.

[0061] Figure 2 This is a schematic diagram of the structure of a stress coupling simulation system for the pore network of component natural gas.

[0062] Figure 3 This is a schematic diagram of the core model;

[0063] Figure 4 This is a schematic diagram of the flow of multi-component gas in rocks with complex porous structures.

[0064] Figure 5 This is a schematic diagram illustrating the competition between CO2 and methane in rocks with complex porous structures. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0066] The classical pore network model, coupled simulation process, and solution process cannot reflect the relationship between effective stress, permeability, and pore radius, resulting in a large deviation between the simulation and the actual situation, making it difficult to capture the flow characteristics of each component in the actual gas reservoir; in view of this, this solution provides the following embodiments to solve the above technical problems.

[0067] Example 1

[0068] This embodiment provides a method for simulating stress coupling in the pore network of component natural gas, such as... Figure 1 As shown, it includes:

[0069] Step 1: Construct a multi-component diffusion flow coupling model that considers the variation of effective pore size, and determine the multi-component gas pore-scale seepage model based on the law of mass conservation.

[0070] Multi-component diffusion flow coupling models with varying effective pore size include:

[0071]

[0072] in, V represents the time derivative; p σ represents pore volume; eff Indicates effective stress; φ j Porosity; C i q represents the concentration of component gas i; i,total N represents the total adsorption amount of component gas i; i This represents the total transport flux of component gas i.

[0073] A multi-component gas pore-scale seepage model was determined based on the law of conservation of mass, including the following methods:

[0074] For any pore structure j in a multi-component diffusion flow coupling model considering changes in effective pore size, the seepage model considering stress variation is:

[0075]

[0076] in, V represents the time derivative; p,j σ represents the volume of pore structure j; eff Indicates effective stress; φ j Indicates porosity; C i,j q represents the concentration of component gas i in pore structure j; total,i,j Ω represents the total adsorption amount of component gas i in pore structure j; k represents the pore structure adjacent to pore structure j; j N represents the set of all pore structures adjacent to pore structure j; i,jk Q represents the total transport flux of component gas i between pore structure j and all adjacent pore structures;i,j For source and sink terms, it represents a source or sink associated with component gas i in pore structure j.

[0077] By considering the variation in effective pore size in a multi-component diffusion-flow coupling model, and introducing a more complex pore structure network model and multi-component gas flow characteristics, a more accurate diffusion-flow coupling model is constructed. This model can demonstrate the real-time changes in pore radius and permeability with reservoir stress, as well as the seepage phenomenon caused by the coupling of pore size with stress changes and multi-component gas flow at the pore scale. It more accurately reflects the microscopic flow mechanism of actual reservoirs and improves the understanding of unconventional natural gas reservoirs.

[0078] Step two: Under the multi-component gas pore-scale seepage model, determine the throat characteristic parameters connecting any two pore structures; the throat characteristic parameters include: the total transport flux of the component gases, the effective radius of the throat, the conductivity of the component gases, and the tortuosity of the throat; this step specifically includes the following methods:

[0079] In the throat connecting pore structure j and pore structure k, the total transport flux N of component gas i is... i,jk for:

[0080] N i,jk =-τ i,jk (p j -p k )-ω i,jk (C i,j -C i,k );

[0081] Where r represents the radius; p j and p k C represents the pressure in pore structure j and pore structure k, respectively; i,j and C i,k τ represents the concentration of component gas i in pore structure j and pore structure k, respectively; i,jk Represents the transmission coefficient; ω i,jk Indicates the diffusion coefficient;

[0082] Diffusion coefficient ω i,jk The calculation method is as follows:

[0083]

[0084] Among them, M i,jk f(W) represents the amount of substance of component i in the throat connecting pore structure j and pore structure k; jk ) is the shape correction function for the throat connecting pore structure j and pore structure k; The tortuosity of the throat connecting pore structure j and pore structure k;

[0085] Tortuosity of the throat connecting pore structure j and pore structure k for:

[0086]

[0087] in, f(W) represents the average porosity of pore structures j and k; jk ) represents the shape correction function for the throat section connecting pore structure j and pore structure k.

[0088] Transmission coefficient τ i,jk The effects of complex pore throat structures and stress were taken into account. The specific calculation method is as follows:

[0089]

[0090] Where, μ jk μ represents the gas viscosity in the throat connecting pore structure j and pore structure k. i,jk This represents the gas viscosity of component gas i in the throat connecting pore structure j and pore structure k. This represents the average pressure in the throat connecting pore structure j and pore structure k; x i,jk L represents the molar composition of component i in the gas within the throat connecting pore structure j and pore structure k. jk The length of the throat connecting pore structure j and pore structure k is indicated by r. eff,jk The effective radius of the throat connecting pore structure j and pore structure k is represented.

[0091] The effective radius r of the throat connecting pore structure j and pore structure k eff,jk for:

[0092]

[0093] Where, σ eff,jk This represents the effective stress applied outside the throat connecting pore structure j and pore structure k; q i,total,jk q represents the total adsorption amount of component gas i in the throat connecting pore structure j and pore structure k; m,i,jk This represents the maximum adsorption capacity of component gas i in the throat connecting pore structure j and pore structure k; d i The characteristic constant of component gas i is represented; nm represents the total number of mixed components in a single-phase gas.

[0094] In the throats of pore structures j and k, the conductivity g of component gas i i,jk for:

[0095]

[0096] Among them, A jkW represents the cross-sectional area of ​​the throat connecting pore structure j and pore structure k. jk σ represents the pore structure correction factor for the throat connecting pore structure j and pore structure k. eff,0,jk μ represents the initial effective stress applied outside the throat connecting pore structure j and pore structure k. jk This represents the gas viscosity of component gas i in the throat connecting pore structure j and pore structure k.

[0097]

[0098] Among them, A jk The shape characteristic function representing the throat cross section connecting pore structure j and pore structure k; W represents the curve function around the throat cross-sectional shape connecting pore structure j and pore structure k; cir The porosity correction factor represents the equivalent circle with the minimum distance from the center of the polygon to the side length;

[0099] Step 3: Discretize the multi-component gas pore-scale seepage model by combining throat characteristic parameters; this step specifically includes the following methods:

[0100] S31, Substituting the throat characteristic parameters into the multi-component gas pore-scale seepage model, we obtain the dynamic expression of the multi-component gas pore-scale seepage model:

[0101]

[0102] S32, Based on the explicit Euler method, the dynamic expression of the multi-component gas pore-scale seepage model is discretized over time to obtain the time-discrete form:

[0103]

[0104] Where n represents the current time step and n-1 represents the previous time step;

[0105] S33, based on the central difference method, spatial variables of pressure and concentration variables in the dynamic expression of a multi-component gas pore-scale seepage model are discretized to obtain the spatial variable discretization form:

[0106]

[0107] S34 uses time-discrete and spatial variable-discrete forms to represent the dynamic expression of the multi-component gas pore-scale seepage model, resulting in the discretized multi-component gas pore-scale seepage model.

[0108] Based on the time-discrete form and the space-variable discretization form, we have:

[0109]

[0110] The final discretized multi-component gas pore-scale seepage model is as follows:

[0111]

[0112] Where k represents the pore structure adjacent to pore structure j; Ω j This represents the set of all pore structures adjacent to pore structure j; n represents the current time step; n-1 represents the previous time step; This represents the transmission coefficient at the current time step; This represents the diffusion coefficient at the current time step; This represents the pressure in pore structure j at the current time step; This represents the pressure of pore structure k at the current time step; Indicates the source and sink terms at the current time step; This represents the concentration of component gas i in pore structure j at the current time step; This represents the concentration of component gas i in pore structure k at the current time step; φ represents the volume of pore structure j at the current time step; j This represents the porosity of pore structure j; σ represents the volume of pore structure j at the previous time step; eff Indicates effective stress; This represents the total adsorption amount of component gas i in pore structure j at the current time step; This represents the total adsorption amount of component gas i in pore structure j at the previous time step; C i,j Δt represents the concentration of component gas i in pore structure j; Δt represents the time step.

[0113] Step four involves solving the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of the component gases in the pores. This step specifically includes the following methods:

[0114] S41, the discretized multi-component gas pore-scale seepage model is written as a matrix form B containing the relationship between pore pressure and concentration in the pores, and the coefficient matrix A of the matrix is ​​obtained.

[0115] The specific matrix form B is:

[0116] Where A is a 2V×2V coefficient matrix, containing the relationship between pressure p and concentration C; the elements of vector b represent sink terms, and vector b is represented as: b=[b1,b2,b3,…b V ,b V+1 …b 2V ]T b V This represents the sink term of the Vth pore;

[0117] S42, based on the LU decomposition method, the coefficient matrix A is decomposed into a lower triangular matrix L and an upper triangular matrix U, resulting in A = L·U, and the intermediate variable ζ is introduced to reconstruct the matrix form B as: Lζ = b;

[0118] S43, first solve the intermediate variable ζ based on the forward substitution method and the reconstructed matrix form B, and then solve the pore pressure matrix p and the concentration matrix C of the component gas in the pores based on the backward substitution method.

[0119] Solve Solve for ζ using the forward substitution method. For each i... The pore pressure matrix p and the concentration matrix C of the component gases in the pores are solved using the backward substitution method:

[0120] S44. The pore pressure and the concentration of the component gas in the pore are obtained based on the pore pressure matrix p and the concentration matrix C of the component gas in the pore.

[0121] For the pore pressure and the concentration of the component gas in the pores, the final solution is:

[0122]

[0123] Finally, vectors p and C are calculated:

[0124]

[0125] In solving the discretized multi-component gas pore-scale seepage model, an adaptive time step is adopted:

[0126]

[0127] Where, Δt n+1 Indicates the step size of the next time step; Δt n The step size represents the current time step; θ total Indicates the allowable error value; θ max This represents the maximum error calculated in the current simulation. It is an adjustment factor to ensure that the change in the time step does not become too large; it is set to 1.2 and... The smallest of the two values.

[0128] The coupled simulation process involves iteratively solving a coupled stress-sensitive multi-component gas pore-scale seepage model. It requires updating the effective stress and pore throat size based on the initial pore structure pressure vector field (matrix) and the concentration fields (matrix) of different gas components. This includes calculating the adsorption amount and effective pore size of each gas component, updating the conductivity, and iteratively solving the pore pressure field and concentration fields of different gas components at the pore nodes for the next time step. Convergence is checked, and if correct, a new iterative simulation is performed until the set termination conditions are met (setting a certain component saturation level or reaching the preset total simulation time). Through this process, a complete coupled simulation workflow for the stress of the component natural gas pore network within the complex pore structure of unconventional natural gas reservoirs can be established.

[0129] Example 2

[0130] This embodiment provides a stress coupling simulation system for component natural gas pore networks, used to implement the aforementioned stress coupling simulation method for component natural gas pore networks; as follows: Figure 2 As shown, the system includes:

[0131] The model building module is used to construct a multi-component diffusion flow coupling model that considers the variation of effective pore size, and to determine a multi-component gas pore-scale seepage model based on the law of conservation of mass.

[0132] The parameter calculation module is used to determine the throat characteristic parameters connecting any two pore structures under a multi-component gas pore-scale seepage model. The throat characteristic parameters include: the total transport flux of the component gases, the effective radius of the throat, the conductivity of the component gases, and the tortuosity of the throat.

[0133] The discretization module is used to discretize the multi-component gas pore-scale seepage model by combining the throat characteristic parameters;

[0134] The solver module is used to solve the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of component gases in the pores.

[0135] Example 3

[0136] This embodiment provides a computer-readable medium storing a computer program, which, when executed by a processor, can implement a stress coupling simulation method for component natural gas pore networks as described in Embodiment 1; specifically, the following steps are performed:

[0137] Step 1: Construct a multi-component diffusion flow coupling model that considers the variation of effective pore size, and determine the multi-component gas pore-scale seepage model based on the law of mass conservation.

[0138] Step 2: Under the multi-component gas pore-scale seepage model, determine the throat characteristic parameters connecting any two pore structures; the throat characteristic parameters include: the total transport flux of the component gas, the effective radius of the throat, the conductivity of the component gas, and the tortuosity of the throat.

[0139] Step 3: Discretize the multi-component gas pore-scale seepage model based on the throat characteristic parameters.

[0140] Step four: Solve the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of component gases in the pores.

[0141] Example 4

[0142] This embodiment applies the method of the present invention to perform stress coupling simulation of a multi-component natural gas pore network. First, a network as shown in the invention is constructed. Figure 3 The core model shown is a 100×100×200 pore node unit. The pore radius distribution follows a log-normal distribution with a mean of 10 nm and a standard deviation of 0.5 nm. Simultaneously, the throat radius also follows a log-normal distribution with a mean of 5 nm and a standard deviation of 0.3 nm. The shape factor distribution of the pore structure is 40% triangular, 30% square, and 30% circular. The average coordination number is 4.5. Regarding reservoir and fluid parameters, under the conditions of an initial confining pressure of 45 MPa, a pore pressure of 30 MPa, an effective network stress of 15 MPa, a reservoir temperature of 80 °C, and a confining pressure of 45 MPa, the stress sensitivity coefficients α and β are set to 0.03 and β to 0.02. The gas composition includes 85% methane (CH4), 8% ethane (C2H6), 4% propane (C3H8), 2% carbon dioxide (CO2), and 1% nitrogen (N2).

[0143] In the stress coupling simulation of multi-component natural gas pore network, the initial assumption is that the initial state of the pore network model is a complete vacuum. Multi-component natural gas enters from the left end at a constant speed and flows out of the model to the outlet under the combined effects of pressure difference, diffusion and adsorption. In the simulation program, the entire outlet pressure of the model is set to a constant value. Except for the inlet and outlet ends, the outer boundary pressure of the model is set to a constant value, and the fluid cannot pass through.

[0144] After realizing the visualization of fluid flow through computer language, the flow simulation of multi-component natural gas in unconventional natural gas reservoirs in porous media with complex pore structures was realized. The simulation results show that as the production pressure drops to 5 MPa, the effective stress increases to 40 MPa, and the throat radius shrinks by an average of 48% according to the exponential model, resulting in a decrease in permeability from 120 mD to 65 mD.

[0145] The flow process of multi-component gas in rocks with complex porous structures, such as Figure 4As shown, different colors represent different concentration distributions of different gas components, with the blue area representing regions not invaded by the gas. The different color differences clearly reflect the variations in the morphology and distribution of different components during the flow process. Simulation results show that when pore throat structure and stress sensitivity are both incorporated, the methane breakthrough time is delayed by approximately 12 hours compared to the traditional model, while carbon dioxide, due to its high adsorption capacity, is essentially adsorbed by the rock within the first 30 days, verifying the ability to characterize the competitive adsorption-transport synergistic mechanism. Furthermore, Figure 5 This invention describes the competitive process between CO2 and methane in rocks with complex pore structures. After CO2 injection, strong adsorption and reduced fluid density jointly promote the rapid advancement of the CO2 front and its migration towards low-pressure areas. CO2 quickly occupies large pores and forces methane to transfer into the microporous system, thereby enhancing oil recovery. Traditional single-component models cannot characterize this process in pore network models. However, the multi-component natural gas pore network stress coupling simulation method provided by this invention accurately recreates the staged changes in CO2 front advancement, methane desorption-readsorption, and effective permeability in porous media with complex pore structures, and can also provide quantitative basis for optimizing the CO2 injection-production ratio.

[0146] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulating stress coupling in a component natural gas pore network, characterized in that, include: A multi-component diffusion flow coupling model considering the variation of effective pore size was constructed, and a multi-component gas pore-scale seepage model was determined based on the law of mass conservation. Under a multi-component gas pore-scale seepage model, the throat characteristic parameters connecting any two pore structures were determined; The throat characteristic parameters include: total transport flux of component gases, effective throat radius, conductivity of component gases, and throat tortuosity. The multi-component gas pore-scale seepage model is discretized based on the throat characteristic parameters. The discretized multi-component gas pore-scale seepage model was solved to obtain the pore pressure and the concentration of component gases in the pores.

2. The method for stress coupling simulation of component natural gas pore networks according to claim 1, characterized in that, The multi-component diffusion flow coupling model with varying effective pore size includes: in, V represents the time derivative; p σ represents pore volume; eff Indicates effective stress; φ j Porosity; C i q represents the concentration of component gas i; i,total N represents the total adsorption amount of component gas i; i This represents the total transport flux of component gas i.

3. The method for stress coupling simulation of component natural gas pore networks according to claim 2, characterized in that, The method for determining a multi-component gas pore-scale seepage model based on the law of conservation of mass includes: For any pore structure j in a multi-component diffusion flow coupling model considering changes in effective pore size, the seepage model considering stress variation is: in, V represents the time derivative; p,j σ represents the volume of pore structure j; eff Indicates effective stress; φ j Indicates porosity; C i,j q represents the concentration of component gas i in pore structure j; total,i,j Ω represents the total adsorption amount of component gas i in pore structure j; k represents the pore structure adjacent to pore structure j; j N represents the set of all pore structures adjacent to pore structure j; i,jk Q represents the total transport flux of component gas i between pore structure j and all adjacent pore structures; i,j For source and sink terms, it represents a source or sink associated with component gas i in pore structure j.

4. The method for stress coupling simulation of component natural gas pore networks according to claim 1, characterized in that, The throat characteristic parameters connecting any two pore structures are determined under the multi-component gas pore-scale seepage model. Including methods: In the throat connecting pore structure j and pore structure k, the total transport flux N of component gas i is... i,jk for: N i,jk =-t i,jk (p j -p k )-oh i,jk (C i,j -C i,k ) Where r represents the radius; p j and p k C represents the pressure in pore structure j and pore structure k, respectively; i,j and C i,k τ represents the concentration of component gas i in pore structure j and pore structure k, respectively; i,jk Represents the transmission coefficient; ω i,jk Indicates the diffusion coefficient; The effective radius r of the throat connecting pore structure j and pore structure k eff,jk for: Where, σ eff,jk This represents the effective stress applied outside the throat connecting pore structure j and pore structure k; q i,total,jk q represents the total adsorption amount of component gas i in the throat connecting pore structure j and pore structure k; m,i,jk This represents the maximum adsorption capacity of component gas i in the throat connecting pore structure j and pore structure k; d i The characteristic constant of component gas i is represented; nm represents the total number of mixed components in a single-phase gas. In the throats of pore structures j and k, the conductivity g of component gas i i,jk for: Among them, A jk W represents the cross-sectional area of ​​the throat connecting pore structure j and pore structure k. jk σ represents the pore structure correction factor for the throat connecting pore structure j and pore structure k. eff,0,jk μ represents the initial effective stress applied outside the throat connecting pore structure j and pore structure k. jk This represents the gas viscosity of component gas i in the throat connecting pore structure j and pore structure k. Tortuosity of the throat connecting pore structure j and pore structure k for: in, f(W) represents the average porosity of pore structures j and k; jk ) represents the shape correction function for the throat section connecting pore structure j and pore structure k.

5. The method for stress coupling simulation of component natural gas pore networks according to claim 1, characterized in that, The multi-component gas pore-scale seepage model is discretized by combining the throat characteristic parameters. Including methods: Substituting the throat characteristic parameters into the multi-component gas pore-scale seepage model, we obtain the dynamic expression of the multi-component gas pore-scale seepage model. The dynamic expression of the multi-component gas pore-scale seepage model is discretized in time based on the explicit Euler method to obtain the time-discrete form; The pressure and concentration variables in the dynamic expression of the multi-component gas pore-scale seepage model based on the central difference method are spatially discretized to obtain the spatial variable discrete form. The dynamic expression of the multi-component gas pore-scale seepage model is represented by time-discrete form and spatial variable-discrete form, resulting in the discretized multi-component gas pore-scale seepage model.

6. The method for stress coupling simulation of component natural gas pore networks according to claim 5, characterized in that, The discretized multi-component gas pore-scale seepage model includes: Where k represents the pore structure adjacent to pore structure j; Ω j This represents the set of all pore structures adjacent to pore structure j; n represents the current time step; n-1 represents the previous time step; This represents the transmission coefficient at the current time step; This represents the diffusion coefficient at the current time step; This represents the pressure in pore structure j at the current time step; This represents the pressure of pore structure k at the current time step; Indicates the source and sink terms at the current time step; This represents the concentration of component gas i in pore structure j at the current time step; This represents the concentration of component gas i in pore structure k at the current time step; φ represents the volume of pore structure j at the current time step; j This represents the porosity of pore structure j; σ represents the volume of pore structure j at the previous time step; eff Indicates effective stress; This represents the total adsorption amount of component gas i in pore structure j at the current time step; This represents the total adsorption amount of component gas i in pore structure j at the previous time step; C i,j Δt represents the concentration of component gas i in pore structure j; Δt represents the time step.

7. The method for stress coupling simulation of component natural gas pore networks according to claim 6, characterized in that, The method involves solving the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of the component gases in the pores; including: The discretized multi-component gas pore-scale seepage model is written as a matrix form B containing the relationship between pore pressure and concentration in the pores, and the coefficient matrix A is obtained. The coefficient matrix A is decomposed into a lower triangular matrix L and an upper triangular matrix U based on the LU decomposition method, and the matrix form B is reconstructed by introducing an intermediate variable ζ. First, the intermediate variable ζ is solved using the forward substitution method and the reconstructed matrix form B. Then, the pore pressure matrix p and the concentration matrix C of the component gases in the pores are solved using the backward substitution method. The pore pressure and the concentration of the component gas in the pores are obtained from the pore pressure matrix p and the concentration matrix C of the component gas in the pores.

8. The method for stress coupling simulation of component natural gas pore networks according to claim 7, characterized in that, In solving the discretized multi-component gas pore-scale seepage model, an adaptive time step is adopted: Where, Δt n+1 Indicates the step size of the next time step; Δt n This indicates the step size of the current time step; Indicates the allowable error value; This represents the maximum error calculated in the current simulation.

9. A stress coupling simulation system for component natural gas pore networks, characterized in that, A system for implementing the stress coupling simulation method for component natural gas pore networks according to any one of claims 1-8; the system comprises: The model building module is used to construct a multi-component diffusion flow coupling model that considers the variation of effective pore size, and to determine a multi-component gas pore-scale seepage model based on the law of conservation of mass. The parameter calculation module is used to determine the throat characteristic parameters connecting any two pore structures under a multi-component gas pore-scale seepage model. The throat characteristic parameters include: the total transport flux of the component gases, the effective radius of the throat, the conductivity of the component gases, and the tortuosity of the throat. The discretization module is used to discretize the multi-component gas pore-scale seepage model by combining the throat characteristic parameters; The solver module is used to solve the discretized multi-component gas pore-scale seepage model to obtain the pore pressure and the concentration of component gases in the pores.

10. A computer-readable medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, can implement a stress coupling simulation method for component natural gas pore networks as described in any one of claims 1-8.

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