Numerical simulation method, system and equipment for low-permeability reservoir water-oil displacement process

Abstract

The invention discloses a numerical simulation method, system and equipment for the water-oil displacement process of a low-permeability reservoir, and relates to the technical field of oil-gas field development engineering and reservoir numerical simulation, the method comprises the steps that a coupling flow model of oil-water two-phase flow is constructed, and the coupling flow model comprises a common diffusion term and an independent diffusion term; wherein the common diffusion term is used for describing collaborative migration of the oil phase and the water phase as a whole under the potential field gradient, and the independent diffusion term is used for describing independent migration of each phase fluid under intra-phase gradient driving; and dynamically adjusting the weight proportion of the common diffusion term and the independent diffusion term in the model according to the flow condition so as to realize simulation of different seepage states. The method solves the problem of inaccurate simulation under the low permeability condition.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development engineering and reservoir numerical simulation technology, specifically to a numerical simulation method, system and equipment for water-drive oil production in low-permeability reservoirs. Background Technology

[0002] With the continued growth of global energy demand and the increasing depletion of conventional oil and gas resources, the exploration and development of low-permeability oil and gas reservoirs has become an important strategic direction for maintaining energy security. Low-permeability reservoirs typically refer to reservoirs with permeability below 50 millidarcy. Their geological characteristics include narrow pore throats, complex pore structures, and poor connectivity, resulting in extremely high resistance to fluid flow and significantly different seepage patterns compared to conventional reservoirs. Water injection development (i.e., waterflooding) is a key technology for improving oil recovery in such reservoirs. However, the displacement process exhibits unique physical characteristics: due to the small displacement pressure gradient and extremely low fluid velocity, the convection effect (i.e., overall flow driven by pressure) in traditional seepage theory is significantly weakened, while the influence of diffusion-like phenomena such as molecular diffusion, capillary adsorption, and concentration gradient-driven flow becomes relatively prominent.

[0003] Currently, commercial numerical simulation software widely used in industry is largely based on convection-diffusion coupling equations dominated by the convection term. Under low-permeability conditions, this model framework has inherent flaws, leading to problems such as biases in mass conservation, distorted predictions of the displacement front, and overly optimistic assessments of final recovery rates, severely impacting the scientific and economic feasibility of development scheme design. In recent years, the academic community has proposed some improvement approaches for low-permeability flow problems, such as using fractional derivative models to describe non-Darcy flows or introducing more complex fluid-structure interaction equations. However, these methods are often computationally expensive, have difficult-to-obtain physical parameters, and fail to fundamentally reconstruct a flow mathematical model framework suitable for low-convection conditions.

[0004] In summary, traditional numerical simulation methods based on the "convection-diffusion" framework suffer from physical distortion and computational instability. The computational models are inaccurate in representing the dual mechanisms of convection-diffusion at low flow velocities, making it difficult to accurately describe flow processes with low convection and strong diffusion under low permeability conditions. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and to propose a numerical simulation method, system and equipment for water-driven oil displacement processes in low-permeability reservoirs, so as to solve the problem of inaccurate simulation under low-permeability conditions.

[0006] A numerical simulation method for water-driven oil recovery in low-permeability reservoirs includes: Obtain reservoir property parameters, fluid physical property parameters, and initial conditions of the target oil reservoir; Based on reservoir property parameters, fluid physical property parameters, and initial conditions, a coupled flow model of oil and water phases is constructed according to the law of conservation of mass and the constitutive relation of generalized diffusion flux. The governing equations of the coupled flow model include common diffusion terms and individual diffusion terms. The common diffusion term is used to simulate the cooperative migration of the oil and water phases under the influence of a common potential gradient, to construct the common diffusion flux of the water and oil phases. The individual diffusion term is used to describe the migration behavior of each phase fluid independently of the common motion, driven by the concentration gradient or chemical potential gradient within the phase, to construct the individual diffusion flux of the water and oil phases respectively. The common diffusion flux of the water and oil phases is coupled with the individual diffusion fluxes of the water and oil phases, and an oil phase partition coefficient function is introduced to allocate the contribution of the common diffusion flux between the two phases, thus establishing the coupled flow model of oil and water phases. The reservoir state field, including the pressure field and saturation field, is obtained by solving the coupled flow model. Based on the reservoir state field, a proportional function characterizing the local flow mechanism is calculated. The weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model is dynamically adjusted according to the proportional function. The reservoir state field and model weight ratio are updated iteratively by calculation, and the simulation results of the evolution of reservoir pressure and saturation over time and space are output.

[0007] Furthermore, the flux of the common diffusion term is expressed as: ; in, Let x be the co-diffusion tensor, and x be the spatial location. t For time, Water saturation; For the total pressure gradient, This is a generalized diffusion caused by uneven rock pore size.

[0008] Furthermore, the separate diffusion term includes aqueous phase separate diffusion flux and oil phase separate diffusion flux, respectively expressed as: ; in, For aqueous phase diffusion flux alone, For oil phase diffusion flux alone, and These are the phase diffusion coefficients for the aqueous phase and the oil phase, respectively. and These represent the saturation levels of the aqueous and oil phases, respectively. and These represent the concentrations of the aqueous phase and the oil phase, respectively. and These represent the concentration gradients of the aqueous and oil phases, respectively.

[0009] Furthermore, the simulation results of reservoir pressure and saturation obtained through iterative calculation include the following steps: Starting from the initial conditions, the coupled flow model is solved iteratively in time sequence; for the current time step, the coupled flow model is solved based on the calculation results of the previous time step to obtain the pressure field and saturation field of the current time step. The initial conditions refer to the reservoir state field defined at the start of the simulation, including the initial pressure field and the initial saturation field. Based on the pressure field and saturation field at the current time step, the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model is dynamically adjusted according to the value of the proportional function. The coupled flow model with updated weight ratios is used for the calculation of the next time step, and the steps of solving the model based on the results of the previous time step, calculating the proportional function, and dynamically adjusting the weight ratios are repeated until the preset simulation termination conditions are met, and the simulation results of reservoir pressure and saturation are output.

[0010] Furthermore, based on the reservoir state field, a proportional function characterizing the local flow mechanism is calculated, and the proportional function is expressed as: ; in, For the co-diffusion tensor, It is the water saturation level. For the total pressure gradient, For rock porosity, A value set to avoid division by zero.

[0011] Furthermore, the step of dynamically adjusting the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model based on the numerical value of the proportional function includes the following steps: When the value of the scaling function is greater than the first set threshold, the effective modulus of the co-diffusion tensor is increased, so that the model behavior tends to be dominated by co-diffusion. When the value of the scaling function is less than the second set threshold, the effective modulus of the co-diffusion tensor is reduced, so that the model behavior tends to be dominated by individual diffusion. When the value of the scaling function is between the first set threshold and the second set threshold, the effective modulus of the co-diffusion tensor is continuously adjusted by a smoothing function.

[0012] Furthermore, based on reservoir property parameters, fluid property parameters, and initial conditions, and in accordance with the law of conservation of mass and the constitutive relation of generalized diffusion flux, a coupled flow model of the oil and water two phases is constructed. The governing equations of this coupled flow model are expressed as follows: ; ; in, For common diffusion flux, and For the individual diffusion fluxes of the aqueous and oil phases, and These represent the saturation levels of the aqueous and oil phases, respectively. φ For rock porosity, The density of the oil phase is... The density of the oil phase is... The water phase distribution coefficient function is the water saturation level. The function, : Oil phase distribution coefficient function, satisfying , The time partial derivative operator characterizes the rate of change of phase mass within the pores with time. For the source and sink of the aqueous phase, For the source and sink of the oil phase.

[0013] Furthermore, the process of iteratively calculating from the initial conditions in chronological order, and solving the coupled flow model based on the results of the previous time step for the current time step, specifically involves discretizing the coupled flow model using the fully implicit finite volume method to form a set of nonlinear equations about pressure and saturation, and solving the set of equations using the Newton-Raphson iterative method, dynamically adjusting the step size of the next time step according to the convergence of the current time step.

[0014] This invention also includes a numerical simulation system for waterflooding processes in low-permeability reservoirs, comprising: The acquisition module is used to acquire reservoir property parameters, fluid physical property parameters, and initial conditions of the target reservoir; A construction module is used to construct a coupled flow model of oil and water phases based on reservoir property parameters, fluid property parameters, and initial conditions, according to the law of conservation of mass and the constitutive relation of generalized diffusion flux. The governing equations of the coupled flow model include common diffusion terms and individual diffusion terms. The common diffusion term is used to simulate the cooperative migration of the oil and water phases under the influence of a common potential gradient, to construct the common diffusion flux of the water and oil phases. The individual diffusion terms are used to describe the migration behavior of each phase fluid independently of the common motion, driven by the concentration gradient or chemical potential gradient within the phase, to construct the individual diffusion flux of the water and oil phases respectively. The common diffusion flux of the water and oil phases is coupled with the individual diffusion fluxes of the water and oil phases, and an oil phase partition coefficient function is introduced to allocate the contribution of the common diffusion flux between the two phases, thus establishing the coupled flow model of oil and water phases. The simulation module is used to solve the coupled flow model to obtain a reservoir state field that includes a pressure field and a saturation field; calculate a proportional function characterizing the local flow mechanism based on the reservoir state field; and dynamically adjust the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model according to the proportional function. The simulation module is used to iteratively update the reservoir state field and model weight ratio through calculation, and output the simulation results of the evolution of reservoir pressure and saturation over time and space.

[0015] The present invention also includes a numerical simulation computer device for water-drive oil displacement process in low-permeability reservoirs, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the numerical simulation method for water-drive oil displacement process in low-permeability reservoirs.

[0016] The present invention also includes a readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, are used to perform the steps of the numerical simulation method for the water-drive process in low-permeability reservoirs.

[0017] This invention provides a numerical simulation method for waterflooding processes in low-permeability reservoirs, which has the following advantages: This invention describes oil-water two-phase flow within a generalized diffusion framework. It uses a common diffusion term to physically represent convection-like effects and a separate diffusion term to characterize diffusion-like behavior. This approach inherently better reflects the real flow characteristics of low-convection, high-diffusion reservoirs, overcoming the physical distortion caused by the failure of the strong convection assumption in traditional models. Secondly, regarding model adaptability, an adaptive mechanism that dynamically adjusts the weight ratio of the two types of diffusion terms based on flow conditions achieves a seamless transition from high-permeability, high-velocity regions (dominated by common diffusion) to low-permeability, low-velocity regions (dominated by separate diffusion). This allows the same model to cover the simulation needs of the entire seepage stage, avoiding the discontinuities and error accumulation caused by model switching in traditional methods. At the numerical computation level, replacing the traditional weakened convection term with a diffusion-type term effectively alleviates numerical singularity and oscillation problems under low-velocity conditions, significantly improving computational stability and convergence. This invention can more accurately characterize the microscopic transport mechanisms such as capillary adsorption and interphase mass transfer in low-permeability reservoirs, and achieve more realistic predictions of the evolution of the displacement front, pressure field and saturation field. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the numerical simulation method for water-driven oil recovery in low-permeability reservoirs in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] This invention proposes a numerical calculation method called the "Diffusion Ratio Controlled Coupled Model." Its core idea is to abandon the traditional approach of modeling "convection" and "diffusion" as distinct physical processes, and instead unify all flow phenomena within a generalized "diffusion" framework. Specifically, this method constructs a coupled flow model that includes "co-diffusion" and "individual diffusion," and dynamically controls the contribution ratio of the two to physically and accurately simulate the entire process of waterflooding under low-permeability conditions.

[0021] like Figure 1 As shown, a numerical simulation method for waterflooding in low-permeability reservoirs includes the following steps: S1. Model initialization: Input the initial conditions of the target reservoir, reservoir property parameters, fluid property parameters, and boundary and source / sink conditions; the initial conditions include at least the initial pressure field and the initial saturation field.

[0022] S2. Constructing and solving the coupled flow model: Based on the law of conservation of mass and the constitutive relation of generalized diffusion flux, a coupled flow model of oil and water phases is constructed. This coupled flow model includes a common diffusion term for simulating the cooperative transport of the two phases and a separate diffusion term for simulating the independent transport of each phase. Starting from this initial condition, iterative calculations are performed in time sequence. For the current time step, the coupled flow model is solved based on the results of the previous time step to obtain the pressure field and saturation field of the current time step.

[0023] S3. Dynamically adjust model parameters: Based on the pressure field and saturation field of the current time step, calculate a proportional function that characterizes the relative strength of the flow mechanism, and dynamically adjust the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model according to the value of the proportional function.

[0024] S4. Iterative simulation: The coupled flow model with updated weight ratios is used for the calculation of the next time step, and steps S2 to S4 are repeated to obtain the evolution results of reservoir pressure and saturation over time and space.

[0025] Initial conditions refer to the definition of the state of the entire reservoir system at the start of the simulation (t=0). This includes at least: Initial pressure field: formation pressure distribution data at each spatial point within the reservoir at the start of the simulation; Initial saturation field: oil phase saturation or water phase saturation distribution data at each spatial point within the reservoir at the start of the simulation.

[0026] Reservoir property parameters: These are spatially distributed parameters that describe the inherent physical properties of the reservoir rocks. They mainly include: Porosity field: a parameter field representing the proportion of pore volume in the rock; Absolute permeability field: a parameter field representing the rock's ability to allow fluids to pass through.

[0027] Fluid physical properties parameters refer to parameters that describe the physicochemical characteristics of the oil and water two-phase fluids within an oil reservoir. These mainly include, but are not limited to, the densities of the oil and water phases, the viscosity of the oil and water phases, the interfacial tension between the oil and water phases, and the fundamental parameters that constitute the capillary coupling coefficient function.

[0028] Boundary and source / sink conditions: These refer to the mathematical conditions that define the exchange of mass and energy between the computational domain and the external environment. Specifically, they include: boundary conditions, such as defining the outer boundary as a closed boundary with no flow, or a isobaric boundary.

[0029] Source-sink term: mainly refers to oil and water wells, whose conditions can be defined as constant production, constant flowing pressure, or associated through well model. Injection wells are the source, and production wells are the sink.

[0030] Co-diffusion term: This is a physical term used to equivalently and macroscopically describe the cooperative transport phenomenon of oil and water phases as a whole system driven by a common potential field such as pressure gradient and capillary force. Its flux is proportional to the total potential gradient but in the opposite direction. This "diffusion" is generalized, and its macroscopic effect is equivalent to the "convection" effect in traditional models, but it provides a more accurate physical description at low flow rates.

[0031] Individual diffusion term: Used to describe the transport behavior of fluid phases independently of the aforementioned common motion, driven by internal chemical potential gradients or concentration gradients. This includes typical diffusion-like phenomena such as classical molecular diffusion, dissolution mass transfer, and microscopic dispersion.

[0032] The proportionality function is a dimensionless or scalar function with specific dimensions, used to quantitatively characterize the relative strength of the co-diffusion (convective-like) driving force and the individual diffusion (diffusion-like) driving force at a specific point in the computational domain at a specific time. Its specific expression is defined in the dependent claims, and its value determines the tendency of the model's behavior.

[0033] Dynamic weighting adjustment: This refers to automatically and continuously changing the contribution of the common diffusion term and the individual diffusion term to the total flow flux calculation based on the real-time calculation results of the proportional function. Specifically, this is achieved by adjusting the effective modulus (i.e., its numerical value) of the common diffusion tensor. When the proportional function value is large, the contribution of the common diffusion term is enhanced, making the model closer to simulating convection-dominated flow; when the proportional function value is small, the contribution of the individual diffusion term is enhanced, making the model closer to simulating diffusion-dominated flow.

[0034] The input basis for model building and the steps of the simulation process: The input to the model as a whole is the initial conditions, and the solution process requires the result of the previous step.

[0035] The simulation of this invention is a time-progressive, iterative, transient process. Its inputs and the computational basis for each step are as follows:

[0036] Initial conditions: This is the starting point of the simulation and represents the essential inputs that must be provided. These mainly include:

[0037] The initial pressure field of the entire reservoir grid system. The initial saturation field of the entire reservoir grid system. Reservoir property fields, such as porosity ( ), absolute permeability (used to calculate initial mobility or as the basis for constructing the common diffusion coefficient).

[0038] Boundary conditions and source / sink terms: These define the rules for the model's interaction with the external world and are essential inputs. They include:

[0039] Injection well conditions: typically constant flow rate or constant bottom hole pressure, with a specified water phase injection rate.

[0040] Production well conditions: typically a constant bottom hole pressure or constant fluid volume, and a specified total production rate or flowing pressure.

[0041] External boundary conditions: usually set as closed boundary (flux is zero) or isobaric boundary.

[0042] The results of the previous time step: After completing the calculation of the nth time step, the obtained pressure field and saturation field will serve as the basis for calculating the initial guesses and nonlinear coefficients of the (n+1)th time step. This is the core feature of time-progressive solution.

[0043] Correspondence between steps and technical features: The coefficient calculation step embodies the feature of "diffusion ratio control": in this step, based on the currently guessed saturation and pressure gradient, the ratio function is calculated and the effective value of the co-diffusion coefficient and the phase distribution coefficient are dynamically adjusted.

[0044] The discretization and linearization steps are the core application of the "fully implicit finite volume method," which involves all variables... Processing at the new time step n+1 and the current iteration layer k ensures the robustness of the method and its stability over large time steps, making it particularly suitable for physical processes with slow changes in low percolation.

[0045] The iterative steps ensure that the nonlinear equations are solved with high accuracy within a single time step. Adaptive time step size is a key optimization technique for addressing the numerical challenges of low-permeability problems.

[0046] Based on the above inventive concept, the present invention proposes the following embodiments: 1. Theoretical model construction.

[0047] Assuming two-phase flow of oil and water in a porous medium, the governing equations of this model are established based on the generalized continuity equation and flux constitutive relation.

[0048] Co-diffusion term: Used to simulate the cooperative transport phenomenon of a two-phase fluid as a whole system under the influence of potential field gradients such as pressure and capillary force. Macroscopically, it is equivalent to a "convection-like" effect. Define the co-diffusion flux. for:

[0049] ; in, For the co-diffusion tensor, it depends not only on spatial location and time It is the water saturation level. The strong function reflects the nonlinear variation of the flow coupling strength. For the total pressure gradient, This is the capillary coupling coefficient function. (Term) The introduction of this is to characterize rock porosity ( The generalized diffusion effect caused by heterogeneity. This flux acts on both the aqueous and oil phases, but in opposite directions (consistent with the principle of action and reaction), thus fundamentally ensuring the conservation of total mass within the computational domain.

[0050] Individual diffusion term: Used to describe the transport behavior of fluid phases independently of their common motion, driven by concentration or chemical potential gradients within those phases; i.e., typical diffusion-like phenomena. The individual diffusion fluxes for the aqueous and oil phases are as follows:

[0051] ; in, and The phase diffusion coefficient accounts for the influence of temperature on the concentration of components within the phase. This parameter characterizes physicochemical processes such as dissolution, mass transfer, and microscopic dispersion.

[0052] Coupled flow equations: By superimposing the two flux terms above, we obtain the modified two-phase flow governing equations: ; ; in, For common diffusion flux, and For the individual diffusion fluxes of the aqueous and oil phases, and These represent the saturation levels of the aqueous and oil phases, respectively. φ For rock porosity, Represents phase density,q Source and sink items. Key parameters. The water phase distribution coefficient function is the water saturation level. The function, : Oil phase distribution coefficient function, satisfying The co-diffusion flux is used to characterize the distribution ratio between two phases. It is directly related to the relative permeability curve and serves as a bridge connecting the new model with traditional physical concepts.

[0053] 2. Core regulation mechanism: dynamic adaptive diffusion ratio.

[0054] The essence of this invention lies in the dynamic control of the relative weights of "co-diffusion" and "isolated diffusion" to simulate different flow states from pure diffusion to strong convection.

[0055] Establish a proportional function: Define a local fine-grained index: ; in To avoid small amounts that are divided by zero. It characterizes the relative strength of the common diffusion driving force and the individual diffusion driving force.

[0056] Dynamic coupling strategy: through a proportional function And the saturation field, dynamically adjusting the effective modulus of the co-diffusion tensor: In areas with high permeability or high flow rate, the system automatically enhances [its effectiveness]. This makes the model behavior approximate traditional convection models dominated by co-diffusion (convective-like).

[0057] In areas with low permeability or low flow rate, the system automatically weakens. This causes the model to degenerate into a fine-grained descriptive model dominated by individual diffusion (diffusion-like).

[0058] In the transition zone, the model achieves a continuous and natural transition between the two mechanisms through a smoothing function, thus seamlessly covering the entire seepage stage.

[0059] Parameter determination: The co-diffusion tensor and phase distribution coefficients can be obtained by joint inversion fitting of core displacement experimental data and historical production data, ensuring the physical objectivity and field applicability of the model.

[0060] 3. Numerical solution strategy.

[0061] A fully implicit finite volume method was employed for spatial and temporal discretization. To handle strong nonlinearity, the Newton-Raphson iterative method was introduced to solve the pressure-saturation equations at each time step. To address the numerical stiffness introduced by low permeability, an adaptive time step technique was used, and semi-implicit processing was employed during coefficient updates to improve stability. The entire solution process achieved efficient and high-resolution simulation of complex physical phenomena in low-permeability flow regions while ensuring the accuracy of mass conservation.

[0062] First, by constructing a coupled flow model that includes both "common diffusion terms" and "individual diffusion terms," ​​the traditionally separate "convection" and "diffusion" mechanisms are unified under a generalized diffusion framework. Next, by establishing a dynamic adaptive diffusion ratio mechanism based on flow conditions, the weight ratios of the two terms are adjusted in real time, enabling the model to smoothly switch the dominant mechanism according to local seepage conditions (high permeability / low permeability, high velocity / low velocity). Finally, the coupled model is numerically solved using a fully implicit finite volume method combined with an adaptive time step technique, achieving stable and efficient computation.

[0063] Based on the above inventive concept, this invention proposes a numerical simulation system for waterflooding processes in low-permeability reservoirs, comprising: The acquisition module is used to acquire reservoir property parameters, fluid property parameters, and initial conditions of the target oil reservoir.

[0064] A construction module is used to build a coupled flow model of oil and water phases based on reservoir property parameters, fluid property parameters, and initial conditions, according to the law of conservation of mass and the constitutive relation of generalized diffusion flux. The governing equations of the coupled flow model include common diffusion terms and individual diffusion terms. The common diffusion term is used to simulate the cooperative migration of oil and water phases under the action of a common potential field gradient to construct the common diffusion flux of the water phase and the oil phase. The individual diffusion term is used to describe the migration behavior of each phase fluid independently of the common motion and driven by the concentration gradient or chemical potential gradient within the phase to construct the individual diffusion flux of the water phase and the oil phase, respectively. The common diffusion flux of the water phase and the oil phase are coupled with the individual diffusion flux of the water phase and the oil phase, and an oil phase partition coefficient function is introduced to allocate the contribution of the common diffusion flux between the two phases to establish the coupled flow model of oil and water phases.

[0065] The simulation module is used to solve the reservoir state field, which includes the pressure field and the saturation field, based on the coupled flow model. Based on the reservoir state field, a proportional function characterizing the local flow mechanism is calculated. The weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model is dynamically adjusted according to the proportional function.

[0066] The simulation module is used to iteratively update the reservoir state field and model weight ratio through calculation, and output the simulation results of the evolution of reservoir pressure and saturation over time and space.

[0067] The present invention also proposes a numerical simulation computer device for water-drive oil displacement process in low-permeability reservoirs, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a numerical simulation method for water-drive oil displacement process in low-permeability reservoirs.

[0068] The present invention also proposes a readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, are steps of a numerical simulation method for a water-drive process in a low-permeability reservoir.

[0069] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A numerical simulation method for waterflooding processes in low-permeability reservoirs, characterized in that, Includes the following steps: Obtain reservoir property parameters, fluid physical property parameters, and initial conditions of the target oil reservoir; Based on reservoir property parameters, fluid physical property parameters, and initial conditions, a coupled flow model of oil and water phases is constructed according to the law of conservation of mass and the constitutive relation of generalized diffusion flux. The governing equations of the coupled flow model include common diffusion terms and individual diffusion terms. The common diffusion term is used to simulate the cooperative migration of the oil and water phases under the influence of a common potential gradient, to construct the common diffusion flux of the water and oil phases. The individual diffusion term is used to describe the migration behavior of each phase fluid independently of the common motion, driven by the concentration gradient or chemical potential gradient within the phase, to construct the individual diffusion flux of the water and oil phases respectively. The common diffusion flux of the water and oil phases is coupled with the individual diffusion fluxes of the water and oil phases, and an oil phase partition coefficient function is introduced to allocate the contribution of the common diffusion flux between the two phases, thus establishing the coupled flow model of oil and water phases. The reservoir state field, including the pressure field and saturation field, is obtained by solving the coupled flow model. Based on the reservoir state field, a proportional function characterizing the local flow mechanism is calculated. The weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model is dynamically adjusted according to the proportional function. The reservoir state field and model weight ratio are updated iteratively by calculation, and the simulation results of the evolution of reservoir pressure and saturation over time and space are output.

2. The method according to claim 1, characterized in that, The flux of the common diffusion term is expressed as: ； in, Let x be the co-diffusion tensor, and x be the spatial location. t For time, Water saturation; For the total pressure gradient, This is a generalized diffusion caused by uneven rock pore size.

3. The method according to claim 1, characterized in that, The separate diffusion term includes aqueous phase separate diffusion flux and oil phase separate diffusion flux, respectively expressed as: ； in, For aqueous phase diffusion flux alone, For oil phase diffusion flux alone, and These are the phase diffusion coefficients for the aqueous phase and the oil phase, respectively. and These represent the saturation levels of the aqueous and oil phases, respectively. and These represent the concentrations of the aqueous phase and the oil phase, respectively. and These represent the concentration gradients of the aqueous and oil phases, respectively.

4. The method according to claim 1, characterized in that, The simulation results of reservoir pressure and saturation obtained through iterative calculation include the following steps: Starting from the initial conditions, the coupled flow model is solved iteratively in time sequence; for the current time step, the coupled flow model is solved based on the calculation results of the previous time step to obtain the pressure field and saturation field of the current time step. The initial conditions refer to the reservoir state field defined at the start of the simulation, including the initial pressure field and the initial saturation field. Based on the pressure field and saturation field at the current time step, the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model is dynamically adjusted according to the value of the proportional function. The coupled flow model with updated weight ratios is used for the calculation of the next time step, and the steps of solving the model based on the results of the previous time step, calculating the proportional function, and dynamically adjusting the weight ratios are repeated until the preset simulation termination conditions are met, and the simulation results of reservoir pressure and saturation are output.

5. The method according to claim 1, characterized in that, Based on the reservoir state field, a proportional function characterizing the local flow mechanism is calculated, and the proportional function is expressed as: ； in, For the co-diffusion tensor, It is the water saturation level. For the total pressure gradient, For rock porosity, A value set to avoid division by zero.

6. The method according to claim 1, characterized in that, The method of dynamically adjusting the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model based on the numerical value of the proportional function includes the following steps: When the value of the scaling function is greater than the first set threshold, the effective modulus of the co-diffusion tensor is increased, so that the model behavior tends to be dominated by co-diffusion. When the value of the scaling function is less than the second set threshold, the effective modulus of the co-diffusion tensor is reduced, so that the model behavior tends to be dominated by individual diffusion. When the value of the scaling function is between the first set threshold and the second set threshold, the effective modulus of the co-diffusion tensor is continuously adjusted by a smoothing function.

7. The method according to claim 1, characterized in that, Based on reservoir property parameters, fluid physical property parameters, and initial conditions, and in accordance with the law of conservation of mass and the constitutive relation of generalized diffusion flux, a coupled flow model of oil and water phases is constructed. The governing equations of this coupled flow model are expressed as follows: ； ； in, For common diffusion flux, and For the individual diffusion fluxes of the aqueous and oil phases, and These represent the saturation levels of the aqueous and oil phases, respectively. φ For rock porosity, The density of the oil phase is... The density of the oil phase is... The water phase distribution coefficient function is the water saturation level. The function, : Oil phase distribution coefficient function, satisfying , The time partial derivative operator characterizes the rate of change of phase mass within the pores with time. For the source and sink of the aqueous phase, For the source and sink of the oil phase.

8. The method according to claim 4, characterized in that, Starting from the initial conditions, the calculation is iterated in chronological order. For the current time step, the coupled flow model is solved based on the results of the previous time step. Specifically, the coupled flow model is discretized using the fully implicit finite volume method to form a set of nonlinear equations about pressure and saturation. The Newton-Raphson iterative method is then used to solve the set of equations. The step size of the next time step is dynamically adjusted according to the convergence of the current time step.

9. A numerical simulation system for waterflooding processes in low-permeability reservoirs, characterized in that, include: The acquisition module is used to acquire reservoir property parameters, fluid physical property parameters, and initial conditions of the target reservoir; A construction module is used to construct a coupled flow model of oil and water phases based on reservoir property parameters, fluid property parameters, and initial conditions, according to the law of conservation of mass and the constitutive relation of generalized diffusion flux. The governing equations of the coupled flow model include common diffusion terms and individual diffusion terms. The common diffusion term is used to simulate the cooperative migration of the oil and water phases under the influence of a common potential gradient, to construct the common diffusion flux of the water and oil phases. The individual diffusion terms are used to describe the migration behavior of each phase fluid independently of the common motion, driven by the concentration gradient or chemical potential gradient within the phase, to construct the individual diffusion flux of the water and oil phases respectively. The common diffusion flux of the water and oil phases is coupled with the individual diffusion fluxes of the water and oil phases, and an oil phase partition coefficient function is introduced to allocate the contribution of the common diffusion flux between the two phases, thus establishing the coupled flow model of oil and water phases. The simulation module is used to solve the coupled flow model to obtain a reservoir state field that includes a pressure field and a saturation field; calculate a proportional function characterizing the local flow mechanism based on the reservoir state field; and dynamically adjust the weight ratio of the common diffusion term and the individual diffusion term in the coupled flow model according to the proportional function. The simulation module is used to iteratively update the reservoir state field and model weight ratio through calculation, and output the simulation results of the evolution of reservoir pressure and saturation over time and space.

10. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 8.

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