A multi-mechanism coupled nanofluid physical and chemical seepage modeling and oil displacement dynamic optimization method

Abstract

This invention discloses a multi-mechanism coupled physicochemical seepage modeling and dynamic optimization method for oil displacement using nanofluids. The invention replaces the traditional Darcy equation with the Darcy-NS equation to achieve multi-scale flow characterization, systematically coupling physicochemical mechanisms such as nanoparticle interface effects, convection-diffusion adsorption, porosity-permeability dynamic changes, and fluid viscosity response. Dedicated seepage transport models are constructed for three systems: typical nanofluids, active nanofluids, and self-growing nanofluids. Based on the constructed models, core-scale dynamic simulations of oil displacement are conducted to obtain dynamic parameters and optimize injection strategies. This invention can accurately simulate the oil displacement behavior of nanofluids, achieve quantitative optimization of key parameters such as injection timing and slug design, significantly improve oil recovery, and is applicable to unconventional reservoirs with high temperature, high salinity, strong heterogeneity, and significant multi-scale flow characteristics, providing theoretical basis and technical support for the application of nanofluid oil displacement technology.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, specifically relating to a numerical simulation method for nanofluid flooding, and in particular a multi-mechanism coupled nanofluid physicochemical flow modeling and dynamic optimization method for flooding. Background Technology

[0002] In unconventional oil reservoir development, reservoirs exhibit strong heterogeneity, multi-scale pore structures, and complex multiphase flow characteristics. Traditional oil displacement simulation methods, based on macroscopic Darcy's law, treat the fluid as a continuous medium and simplify key parameters such as capillary force and interfacial tension to constants. These methods struggle to accurately describe the microscopic physicochemical effects of nanofluids, such as the interfacial behavior and dynamic adsorption of nanoparticles. stagnation, convection Diffusion and fluid viscosity response, etc.

[0003] Current technologies lack systematic mathematical models that can integrate the equations of state for nanoparticles, multiphase flow, porosity dynamics, and fluid property changes. Furthermore, there is a lack of targeted mathematical descriptions and modeling frameworks for the physicochemical seepage mechanisms of different types of nanofluids. This leads to significant discrepancies between numerical simulation results and actual oil displacement processes, making it difficult to effectively guide slug design and injection timing optimization in nanofluid-assisted oil displacement. Therefore, there is an urgent need to develop a clear, multi-scale, and optimizable method for nanofluid seepage modeling and dynamic simulation.

[0004] Meanwhile, there is a lack of multi-scale simulations from microscopic mechanisms to core scales, meaning a lack of simulation methods to guide discontinuous variable-cycle displacement strategies. This makes the application of nanofluid flooding technology in unconventional reservoirs lack scientific support, limiting the improvement of reservoir development efficiency, and necessitates overcoming existing technological bottlenecks. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-mechanism coupled nanofluid physicochemical flow modeling and oil displacement dynamic optimization method, which can accurately simulate the oil displacement process of different nanosystems, dynamically optimize displacement parameters, and improve the recovery rate of unconventional reservoirs.

[0006] This invention uses the Darcy-NS equation to replace the original Darcy flow to characterize the flow of nanofluids in porous media, achieving multi-scale simulation; considering the interface effect of nanoparticles, as well as the changes in pore permeation and phase permeation curves caused by the convection diffusion and adsorption retention of nanoparticles, a basic model of typical nanofluid transport is established; the similarities and differences between typical nanofluids, active nanofluids and self-growing nanofluids are preliminarily clarified, and the uniqueness of different systems is characterized by additional equations.

[0007] Specifically, the multi-mechanism coupled nanofluid physicochemical seepage modeling and oil displacement dynamic optimization method provided by the present invention includes the following steps:

[0008] A physicochemical flow model for nanofluid flow in porous media is established. The physicochemical flow model is coupled with the mass conservation equation, momentum conservation equation, water phase equation, oil phase equation, interfacial force equation, permeability equation and viscosity equation of nanoparticles after oil-water interaction. Based on the aforementioned physicochemical percolation fundamental model, specific percolation mathematical models for different nanofluid systems are constructed. The specific percolation mathematical models include at least one model for typical nanofluid systems, active nanofluid systems, and self-growing nanofluid systems. Using the aforementioned proprietary seepage mathematical model, the nanofluid oil displacement process at the core scale was dynamically simulated to obtain dynamic parameters for oil displacement. Based on the aforementioned oil displacement dynamic parameters, the injection strategy for nanofluid-driven oil displacement is optimized.

[0009] Preferably, when establishing the physicochemical seepage basic model, the Darcy-NS equation is used instead of the traditional Darcy equation to describe the flow of nanofluids in porous media, realizing multi-scale simulation from the micropore scale to the macro core scale.

[0010] Preferably, the momentum conservation equation in the physicochemical seepage fundamental model is the Darcy-NS equation, which has the following form:

[0011] in, Porosity For density, For speed, For pressure, For viscosity, F C For interface force.

[0012] Preferably, the physicochemical percolation model for the flow of the nanofluid in the porous medium is as follows: Conservation of mass:

[0013] Conservation of momentum:

[0014] Water phase equation:

[0015] Oil phase equation:

[0016] Interfacial force equations:

[0017] Permeability equation:

[0018] Viscosity of nanoparticles after oil-water interaction:

[0019] Porosity For penetration rate, For density, For speed, For pressure, For viscosity, For interface force, For phase saturation, Where is the diffusion coefficient. 1. 2 represents the coefficient of the exponential adsorption equation. For interfacial tension, The oil-water contact angle, The equivalent orifice throat radius, For the nanoparticle blockage fraction, To reduce the penetration rate index, 0 represents the initial penetration rate. Permeability after clogging; subscript , , , , These represent oil, water, mixed liquid, nano-liquid, and rock solid phase, respectively.

[0020] Preferably, when constructing the specific percolation mathematical model of the typical nanofluid system, it includes at least establishing the equation of state of the nanoparticles and constructing its percolation transport model in combination with a multiphase and multicomponent model. The percolation transport model of the typical nanofluid system includes the convection-diffusion-adsorption equation, the pore percolation change equation, and the phase percolation change equation. Convection-diffusion adsorption equation:

[0021] Pore ​​permeability variation equation:

[0022] Equation of phase permeation change:

[0023] x For distance; u For speed; C i For nanoparticles in i Concentration within the range; S w Water phase saturation; t For time; D iFor nanoparticles in i The diffusion coefficient of the interval; R i For nanoparticles in i Adsorption parameters within the range; φ Porosity; φ 0 represents the initial porosity; K For penetration rate; K 0 represents the initial penetration rate; K f The permeability after clogging; f For blocking scores; K ’ rwjp The relative permeability of the aqueous phase; K rwj The aqueous phase permeability without nano-adsorption; K ’ rwj The aqueous phase permeability when the nanoparticles are fully adsorbed; K ’ rojp This refers to the relative permeability of the oil phase. K roj The oil phase permeability without nano-adsorption; K ’ roj The aqueous phase permeability when the nanoparticles are fully adsorbed; S v Specific surface area of ​​pores; S This represents the area of ​​adsorption by the nanoparticles.

[0024] Unlike nanosystems, active nanofluids, in addition to possessing the basic transport characteristics of nanoparticles, also adsorb at the oil-water-solid interface, thereby altering the viscosity of the displacing system, causing retention and blockage, convection and diffusion (dispersion), and changing key parameters such as porosity and permeability. Therefore, when constructing a dedicated permeation mathematical model for the active nanofluid system, it is necessary to at least establish the equation of state for the active nanofluid and combine it with a multiphase and multicomponent model to construct its permeation transport model. The permeation transport model for the active nanofluid system includes reaction kinetic equations, fluid viscosity change equations, convection-diffusion-adsorption equations, porosity and permeability change equations, and phase permeability change equations. Reaction kinetic equation:

[0025] Equation for fluid viscosity change:

[0026] Convection-diffusion adsorption equation:

[0027] Pore ​​permeability variation equation:

[0028] Equation of phase permeation change:

[0029] k For reaction rate constants; A Pre-exponential factors; E Activation energy; T For temperature; μ oeff For effective emulsion viscosity; S w Water saturation; μ o Crude oil viscosity; R ad This refers to the amount of adsorption loss in the emulsion.

[0030] Since the nanoparticles in the self-grown nanosystem increase over time, thereby improving its injectability and effective plugging properties, and thus changing key parameters such as adsorption blockage, convection diffusion, and pore permeability changes, the construction of the dedicated permeation mathematical model for the self-grown nanofluid system should at least include establishing the equation of state for the self-grown nanofluid and constructing its permeation transport model in combination with a multiphase multicomponent model. The permeation transport model of the self-grown nanofluid system includes the growth rate equation, the fluid viscosity change equation, the convection diffusion adsorption equation, the pore permeability change equation, and the phase permeability change equation. Growth rate equation:

[0031] Equation for fluid viscosity change:

[0032] Convection-diffusion adsorption equation:

[0033] Pore ​​permeability variation equation:

[0034] Equation of phase permeation change:

[0035] k For reaction rate constants; A Pre-exponential factors; E Activation energy; T For temperature; μ l The viscosity of the system; S w Water saturation; R i1 The adsorption parameters are for self-grown nanoparticles.

[0036] Preferably, the dynamic parameters for oil displacement include at least one of oil recovery rate, oil-water saturation distribution, and pressure distribution; the optimized nanofluid oil displacement injection strategy includes at least one of optimized displacement rate, nanofluid mass fraction, and injection slug.

[0037] Preferably, in the step of dynamically simulating the nanofluid oil displacement process at the core scale using the dedicated seepage mathematical model, the core permeability difference is further considered to study the influence of displacement rate, nanofluid mass fraction, and rock porosity and permeability parameters on the recovery rate.

[0038] The present invention has the following beneficial technical effects: Multi-mechanism coupling, high simulation accuracy: This invention overcomes the limitations of traditional macroscopic Darcy's law, which simplifies key parameters such as capillary force and interfacial tension to constants, and systematically constructs a multi-scale, multi-physics field coupled seepage mathematical model. This model comprehensively considers the interfacial effects of nanoparticles and convection. diffusion The key physicochemical processes, such as adsorption mechanism, dynamic changes in pore permeability, and fluid viscosity response, can accurately simulate the oil displacement behavior of active nanofluids in porous media, solving the problem of the lack of systematic mathematical description in existing technologies.

[0039] Multi-scale integration and wide applicability: This invention uses the Darcy-NS equation instead of the traditional Darcy equation to characterize the flow of nanofluids in porous media, achieving multi-scale simulation from the microscopic pore scale to the macroscopic core scale. Simultaneously, for three different systems—typical nanofluids, active nanofluids, and self-growing nanofluids—the differences and similarities in their mechanisms are clarified. Furthermore, a dedicated seepage transport model is constructed through additional equations (such as reaction kinetics equations, growth rate equations, and viscosity change equations), solving the problem of the lack of a targeted modeling framework in existing technologies. This model is flexibly applicable to various nano-enhanced oil recovery systems.

[0040] Dynamic simulation optimization with strong engineering guidance: Based on the constructed physicochemical seepage mathematical model, this invention can conduct dynamic simulations of the nanofluid flooding process at the core scale. Through simulation, key parameters such as oil-water saturation distribution and pressure distribution during the displacement dynamic process can be obtained, and the effects of displacement rate, nanofluid mass fraction, and rock porosity and permeability parameters on oil recovery can be studied. Based on this, injection strategies such as injection timing and slug design for nanofluid flooding can be optimized, providing reliable technical support for the scientific formulation and dynamic adjustment of nanofluid flooding schemes.

[0041] Enhancing oil recovery and supporting the development of unconventional reservoirs: This invention is particularly suitable for unconventional reservoirs characterized by high temperature and salinity, strong heterogeneity, and significant multi-scale flow characteristics. Through specific core-scale simulation examples, the method of this invention, using a waterflooding process to a cumulative recovery rate of 38% (90% water cut), followed by the injection of 0.3PV nanofluid and subsequent waterflooding, achieves a cumulative recovery rate of up to 75%, increasing the recovery rate by 37%. These results demonstrate that this invention can effectively guide the nanofluid flooding process, significantly improving crude oil recovery and providing strong technical support for the efficient development of unconventional reservoirs. Attached Figure Description

[0042] Figure 1 This is a graph showing the viscosity variation equation of the active nanofluid.

[0043] Figure 2 These are mathematical models of physicochemical percolation in different nanosystems.

[0044] Figure 3 This is a dynamic simulation diagram of nanofluid flooding at the core scale.

[0045] Figure 4 These are core model diagrams of porosity (top) and permeability (bottom).

[0046] Figure 5 This is a distribution diagram of oil and water saturation during the dynamic simulation of displacement. From left to right, it shows the distribution of oil (top) / water (bottom) saturation during the dynamic simulation of displacement.

[0047] Figure 6 This is a pressure distribution diagram of the displacement dynamic simulation process. Detailed Implementation

[0048] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0049] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0050] This invention provides a multi-mechanism coupled nanofluid physicochemical flow modeling and oil displacement dynamic optimization method: First, this invention constructs a multi-mechanism coupled physicochemical flow fundamental model. This model overcomes the limitations of the traditional Darcy's law, employing the Darcy-NS equation instead to achieve multi-scale simulation from the microscopic pore scale to the macroscopic core scale. The fundamental model systematically couples the mass conservation equation, momentum conservation equation, aqueous phase equation, oil phase equation, interfacial force equation, permeability equation, and viscosity equation of nanoparticles after oil-water interaction, comprehensively considering the interfacial effects and convection of nanoparticles. diffusion Key physicochemical processes include adsorption mechanisms, dynamic changes in pore permeability, and fluid viscosity response.

[0051] Secondly, considering the differences between different types of nanofluids, this invention constructs specific percolation mathematical models for three systems—typical nanofluids, active nanofluids, and self-growing nanofluids—based on the aforementioned basic model. Among them: For typical nanofluid systems, their equations of state are established, and a seepage transport model including convection-diffusion-adsorption equations, pore-permeability change equations, and phase permeability change equations is constructed. For active nanofluid systems, their equation of state is established, and a seepage transport model is constructed that includes reaction kinetics equation, fluid viscosity change equation, convection-diffusion-adsorption equation, pore-permeability change equation, and phase permeability change equation. For self-growing nanofluid systems, their equation of state is established, and a seepage transport model is constructed that includes the growth rate equation, fluid viscosity change equation, convection-diffusion-adsorption equation, pore-permeability change equation, and phase permeability change equation.

[0052] Finally, based on the dedicated seepage mathematical models of different nanofluid systems constructed, this invention conducts dynamic simulations of the nanofluid flooding process at the core scale, obtains dynamic parameters of oil displacement including recovery rate, oil-water saturation distribution and pressure distribution, and optimizes the injection strategy of nanofluid flooding, including displacement rate, nanofluid mass fraction and injection slug, etc.

[0053] Example 1: Establishment and Solution of Darcy-NS Flow Equations The traditional Darcy equation is insufficient to accurately describe the flow details of nanofluids in complex porous structures. Therefore, this invention employs the Darcy-NS equation instead of the traditional Darcy equation. According to Newton's second law, the seepage of fluid elements in rock is influenced by the combined effects of the net force of the surrounding fluid, gravity, viscous force, and capillary force.

[0054] Force analysis of a single-phase seepage fluid (ignoring gravity) in a micro-element, taking the x-direction as an example, yields the following flow equation:

[0055] Its three-dimensional general formula is:

[0056] Introducing the mass derivative yields the Darcy-NS flow equation:

[0057] By combining the mass conservation equation and the Navier-Darcy flow equation, we obtain the momentum conservation equation for a single-phase fluid:

[0058] This equation, coupled with the subsequent multiphase flow equation, forms the basis of the multiscale simulation of this invention.

[0059] Example 2: Establishment and parameter fitting of the viscosity variation equation for active nanofluids A laboratory-made nanofluid A with a viscosity of 0.1 wt% was used. The crude oil viscosity was 20.5 mPa·s at 50℃ and 3.4 mPa·s at 120℃. The experimental temperature was 120℃, the stirrer speed was 1000 r·min⁻¹, the stirring time was 30 min, and the total volume of the oil-water mixture was 30 ml. The water content during emulsification with nanofluid A was set to 10%, 30%, 40%, 50%, 60%, 70%, 80%, and 85%, respectively. Before stirring, the oil-water mixture was preheated at 120℃ for 30 min, and the emulsion viscosity was measured immediately after stirring.

[0060] Experimental results show (see) Figure 1 When the water content is below 45% and 55%, respectively, the nanofluid self-assembles at the oil-water interface to form a thickening emulsion, and the emulsion viscosity is positively correlated with the water content. When the water content is above 45% and 55%, the relative strength of the oil-water interface film decreases, the free water phase increases, and the emulsion viscosity is negatively correlated with the water content.

[0061] Based on the above experimental data, the relationship between the viscosity and water content of the active nanofluid emulsion was established using a nonlinear regression method as follows:

[0062] In the formula, μ e S represents the emulsion viscosity, expressed in mPa·s. w The values ​​represent the water content, expressed as %; a and b are equation coefficients, which are dimensionless. For nanofluid emulsion A, when the water content is less than or equal to 0.55%, a = 5.66 and b = 4.84; when the water content is greater than 0.55%, a = 1192621.5 and b = -17.44771.

[0063] This viscosity-water content variation equation provides a quantitative basis for determining the optimal injection timing of active nanofluids.

[0064] Example 3: Derivation and Analytical Solution of the Convection-Diffusion Adsorption Equation The classic convection-diffusion model is as follows:

[0065] When nanoscale systems flow in porous media, adsorption occurs. Considering the adsorption term:

[0066] Nanoscale systems exhibit exponential adsorption, inherently possessing the following properties:

[0067]

[0068] When C = 0.5C0, V corresponds to C as V0.5 The above formula can be rewritten as:

[0069] When x=L (one-dimensional linear flow), the analytical solution for convection-diffusion of the active nanofluid is obtained:

[0070] This analytical solution provides a rapid method for predicting the transport behavior of nanofluids in porous media.

[0071] Example 4: Construction of Mathematical Models for Different Nanofluid Systems First, we need to clarify the similarities and differences among typical nanofluids, active nanofluids, and self-growing nanofluids. Typical nanofluids mainly exhibit interfacial effects, convection diffusion, and adsorption retention of nanoparticles; active nanofluids, in addition to these, also exhibit the characteristic of interfacial adsorption altering the viscosity of the displaced system; self-growing nanofluids, on the other hand, have the characteristic of nanoparticles growing with increasing reaction time, thereby improving injectability and plugging properties.

[0072] For the three systems mentioned above, seepage transport models were constructed respectively (see...). Figure 2 ): Typical nanofluid systems include convection-diffusion-adsorption equations, pore-permeability change equations, and phase permeability change equations. Active nanofluid systems include reaction kinetic equations, fluid viscosity change equations, convection-diffusion-adsorption equations, pore-permeability change equations, and phase permeability change equations; Self-growing nanofluid systems include growth rate equations, fluid viscosity change equations, convection-diffusion-adsorption equations, pore-permeability change equations, and phase permeability change equations.

[0073] The three types of dedicated models mentioned above can be selected according to the type of nanofluid used in the actual application, thus achieving a precise description of the oil displacement process of different nanosystems.

[0074] Example 5: Dynamic Simulation and Optimization of Core-Scale Nanofluid Enhanced Oil Displacement This embodiment uses the constructed nanofluid physicochemical seepage mathematical model to conduct dynamic simulation of oil displacement at the core scale and verify the effectiveness of the method of the present invention.

[0075] 1. Model parameter settings The core model measures 1.8cm × 5.0cm (diameter × length) and employs a heterogeneous permeability field. The core permeabilities are 20mD, 50mD, and 100mD, with a porosity of 16.0%. The model is as follows: Figure 3 and Figure 4As shown. The simulation temperature was 120℃, the original oil saturation was 80%, the aqueous phase viscosity was 0.31 mPa·s, and the viscosity of nanofluid A emulsion met the viscosity-water content relationship established in Example 2. The core model mesh generation and porosity / permeability parameter distribution are shown below. Figure 4 As shown.

[0076] 2. Displacement Scheme and Simulation Results The displacement scheme is designed as follows: first, water flooding is carried out to a water content of 90%, then 0.3 PV of active nanofluid A is injected, and finally subsequent water flooding is carried out to a water content of 98%.

[0077] Simulation results are as follows Figure 5 and Figure 6 As shown: Figure 5 The changes in saturation distribution of the oil and water phases during the dynamic simulation of displacement were demonstrated, and the initiation and displacement effects of nanofluid injection on residual oil could be observed intuitively. Figure 6 The study demonstrates the pressure distribution changes during the displacement dynamic simulation process, reflecting the flow resistance characteristics of nanofluids in porous media.

[0078] 3. Recovery rate analysis Simulation results show that: When the water content reaches 90%, the cumulative recovery rate reaches 38%. When 0.3PV nanofluid A was used and subsequent waterflooding was performed to a water cut of 98%, the cumulative recovery rate reached 75%. Compared with pure water flooding, nanofluid flooding increases oil recovery by 37%.

[0079] The simulation results verify that the physicochemical seepage mathematical model constructed in this invention can effectively predict the oil displacement dynamics of nanofluids, quantitatively assess its potential to enhance oil recovery, and provide a scientific basis for optimizing nanofluid oil displacement injection strategies.

[0080] Example 6: Injection Strategy Optimization Based on the simulation platform of Example 5, the effects of different displacement parameters on oil recovery were further investigated. Multiple simulations were conducted to compare different displacement rates (0.1 m / d, 0.3 m / d, 0.5 m / d), nanofluid mass fractions (0.05 wt%, 0.1 wt%, 0.2 wt%), and injection slug sizes (0.2 PV, 0.3 PV, 0.5 PV).

[0081] Simulation results show that: For the core model and nanofluid system in this embodiment, the optimal displacement rate is 0.3 m / d; The recovery rate increased most when the nanofluid mass fraction was 0.1 wt%, and further increasing the concentration had limited contribution to improving the recovery rate. The optimal cost-effectiveness is achieved when the injected slug size is 0.3 PV, and the recovery rate increases with further increases in slug size tend to slow down.

[0082] Through the above optimization analysis, the optimal injection strategy for specific reservoir conditions and nanofluid systems can be determined, achieving the best overall balance between economic benefits and enhanced oil recovery.

[0083] In summary, this invention constructs a multi-mechanism coupled physicochemical flow mathematical model and establishes dedicated flow transport models for different nanofluid systems. This enables accurate dynamic simulation and injection strategy optimization of the nanofluid flooding process at the core scale, significantly improving simulation accuracy and engineering practicality. It provides reliable technical support for the application of nanofluid flooding technology in unconventional reservoirs.

Claims

1. A multi-mechanism coupled nanofluid physicochemical flow modeling and oil displacement dynamic optimization method, comprising the following steps: A physicochemical flow model for nanofluid flow in porous media is established. The physicochemical flow model is coupled with the mass conservation equation, momentum conservation equation, water phase equation, oil phase equation, interfacial force equation, permeability equation and viscosity equation of nanoparticles after oil-water interaction. Based on the aforementioned physicochemical percolation fundamental model, specific percolation mathematical models for different nanofluid systems are constructed. The specific percolation mathematical models include at least one model for typical nanofluid systems, active nanofluid systems, and self-growing nanofluid systems. Using the aforementioned proprietary seepage mathematical model, the nanofluid oil displacement process at the core scale was dynamically simulated to obtain dynamic parameters for oil displacement. Based on the aforementioned oil displacement dynamic parameters, the injection strategy for nanofluid-driven oil displacement is optimized.

2. The method according to claim 1, characterized in that: When establishing the physicochemical seepage basic model, the Darcy-NS equation is used instead of the traditional Darcy equation to describe the flow of nanofluids in porous media, realizing multi-scale simulation from the micropore scale to the macro core scale.

3. The method according to claim 1 or 2, characterized in that: The momentum conservation equation in the physicochemical seepage fundamental model is the Darcy-NS equation, which takes the form: in, Porosity For density, For speed, For pressure, For viscosity, F C For interface force.

4. The method according to any one of claims 1-3, characterized in that: The physicochemical percolation model for the flow of nanofluids in porous media is as follows: Conservation of mass: Conservation of momentum: Water phase equation: Oil phase equation: Interfacial force equations: Permeability equation: Viscosity of nanoparticles after oil-water interaction: Porosity For penetration rate, For density, For speed, For pressure, For viscosity, For interface force, For phase saturation, Where is the diffusion coefficient.

1. 2 represents the coefficient of the exponential adsorption equation. For interfacial tension, The oil-water contact angle, The equivalent orifice throat radius, For the nanoparticle blockage fraction, To reduce the penetration rate index, 0 represents the initial penetration rate. Permeability after clogging; subscript , , , , These represent oil, water, mixed liquid, nano-liquid, and rock solid phase, respectively.

5. The method according to any one of claims 1-4, characterized in that: When constructing the specific percolation mathematical model of the typical nanofluid system, it includes at least establishing the equation of state of the nanoparticles and constructing its percolation transport model in combination with the multiphase multicomponent model. The percolation transport model of the typical nanofluid system includes the convection-diffusion-adsorption equation, the pore percolation change equation, and the phase percolation change equation. Convection-diffusion adsorption equation: Pore ​​permeability variation equation: Equation of phase permeation change: x For distance; u For speed; C i For nanoparticles in i Concentration within the range; S w Water phase saturation; t For time; D i For nanoparticles in i The diffusion coefficient of the interval; R i For nanoparticles in i Adsorption parameters within the range; φ Porosity; φ 0 represents the initial porosity; K For penetration rate; K 0 represents the initial penetration rate; K f The permeability after clogging; f For blocking scores; K ’ rwjp The relative permeability of the aqueous phase; K rwj The aqueous phase permeability without nano-adsorption; K ’ rwj The aqueous phase permeability when the nanoparticles are fully adsorbed; K ’ rojp This refers to the relative permeability of the oil phase. K roj The oil phase permeability without nano-adsorption; K ’ roj The aqueous phase permeability when the nanoparticles are fully adsorbed; S v Specific surface area of ​​pores; S This represents the area of ​​adsorption by the nanoparticles.

6. The method according to any one of claims 1-4, characterized in that: When constructing the specific percolation mathematical model of the active nanofluid system, it includes at least establishing the equation of state of the active nanofluid and constructing its percolation transport model in combination with the multiphase and multicomponent model. The percolation transport model of the active nanofluid system includes reaction kinetic equation, fluid viscosity change equation, convection-diffusion-adsorption equation, pore percolation change equation and phase percolation change equation. Reaction kinetic equation: Equation for fluid viscosity change: Convection-diffusion adsorption equation: Pore ​​permeability variation equation: Equation of phase permeation change: k For reaction rate constants; A Pre-exponential factors; E Activation energy; T For temperature; μ oeff For effective emulsion viscosity; S w Water saturation; μ o Crude oil viscosity; R ad This refers to the amount of adsorption loss in the emulsion.

7. The method according to any one of claims 1-4, characterized in that: When constructing the dedicated percolation mathematical model of the self-growing nanofluid system, it includes at least establishing the state equation of the self-growing nanofluid and constructing its percolation transport model in combination with the multiphase multicomponent model. The percolation transport model of the self-growing nanofluid system includes the growth rate equation, the fluid viscosity change equation, the convection-diffusion-adsorption equation, the pore percolation change equation, and the phase percolation change equation. Growth rate equation: Equation for fluid viscosity change: Convection-diffusion adsorption equation: Pore ​​permeability variation equation: Equation of phase permeation change: k For reaction rate constants; A Pre-exponential factors; E Activation energy; T For temperature; μ l The viscosity of the system; S w Water saturation; R i1 The adsorption parameters are for self-grown nanoparticles.

8. The method according to any one of claims 1-7, characterized in that: The oil displacement dynamic parameters include at least one of recovery rate, oil-water saturation distribution, and pressure distribution; the optimized nanofluid oil displacement injection strategy includes at least one of displacement rate, nanofluid mass fraction, and injection slug.

9. The method according to any one of claims 1-8, characterized in that: In the step of dynamically simulating the nanofluid oil displacement process at the core scale using the aforementioned proprietary seepage mathematical model, the influence of core permeability difference, displacement rate, nanofluid mass fraction, and rock porosity and permeability parameters on oil recovery is further considered.

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