Method for calculating microcosmic residual oil non-continuous evolution cross-scale parameters of middle-high permeability reservoir

Abstract

The application discloses a kind of middle-high permeability reservoir microcosmic residual oil non-continuous evolution cross-scale parameter calculation methods, and it is related to oil and gas field development technical field.The application is first according to the core sample of target layer section in middle-high permeability reservoir, constructs three-dimensional digital core model and determines macroscopic percolation equation, carries out microcosmic two-phase flow full dynamic displacement simulation using three-dimensional digital core model, obtains global water saturation in real time during simulation process and carries out topological snapshot automatic acquisition microcosmic oil-water interface space configuration to obtain displacement snapshot, based on interface natural relaxation static force balance method, extracts capillary pressure inside three-dimensional digital core model to construct capillary pressure data set, based on no capillary force steady percolation method, effective permeability is extracted to construct effective permeability data set, and the cross-scale percolation constitutive parameter curve containing microcosmic residual oil non-continuous occurrence information is obtained by fitting and mapped to reservoir numerical simulator, the accurate acquisition of microscale physical information in reservoir macroscopic numerical simulation process is realized.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, specifically to a method for calculating cross-scale parameters of discontinuous evolution of microscopic residual oil in medium- and high-permeability reservoirs. Background Technology

[0002] After long-term water injection development, most medium-to-high permeability sandstone reservoirs have entered the ultra-high water-cut stage (i.e., overall water cut > 90%), and the microscopic occurrence state of the remaining oil underground has undergone a fundamental change. Unlike low-permeability-tight reservoirs where crude oil is mainly bound by capillary forces as a continuous phase, medium-to-high permeability reservoirs, due to their larger pore throat radius and higher water drive front advance velocity, experience more intense viscous-capillary force competition between injected water and crude oil. This leads to frequent dynamic processes such as interface instability, snap-off effects at the throat, oil filament breakage, and droplet capture. As a result, crude oil gradually transforms from an early continuous oil phase into a discontinuous form dispersed in the water-bearing pore space, including isolated oil droplets, oil films, blind-end residual oil, and corner residual oil. Therefore, the existence of such a large number of discontinuous occurrences in medium- and high-permeability reservoirs has created a unique challenge in characterizing seepage parameters: on the one hand, the formation of discontinuous oil phases has a strong path dependence, and its spatial topology is not only dependent on the current water saturation, but also closely related to the entire process of interface evolution in the displacement history; on the other hand, the migration of isolated oil droplets carried by water flow, the additional resistance generated when passing through the throat (i.e., the Jamin effect), and the re-coagulation of oil droplets make the viscous shear contribution and the interfacial capillary force contribution in the macroscopic seepage resistance mixed and difficult to distinguish.

[0003] How to extract constitutive parameters that truly reflect the aforementioned microscopic physical mechanisms from the microscopic pore structure of typical core samples of a specific reservoir segment, clearly define the effective permeability and intrinsic capillary pressure that are independently decoupled from viscous forces and capillary forces in a physical sense, and map them as constitutive inputs to the macroscopic seepage equation into the reservoir so that macroscopic numerical simulation can include physical information at the microscopic scale, has become a core technical challenge in the fine numerical simulation of medium- and high-permeability reservoirs during the ultra-high water cut period.

[0004] Meanwhile, in practical applications, the following limitations have been found in existing technologies for numerical simulation of medium-to-high permeability reservoirs: First, the parameters obtained by the core displacement experiment method are volume-averaged results at the core scale, which cannot reveal the dynamic evolution of the fluid interface topology at the pore scale.

[0005] Second, the quasi-static method based on the pore network model calculates the filling state pore by pore and throat by throat based on the intrusion percolation assumption. Its quasi-static assumption ignores the fluid inertia and interface dynamics effects.

[0006] Third, although the direct numerical simulation method based on pore scale in computational fluid dynamics can reproduce the above transient process with high fidelity in three-dimensional real pore space, there are systematic defects when upgrading the simulation results to macroscopic seepage parameters. This results in the output equivalent permeability being mixed with the contribution of capillary resistance, the equivalent capillary pressure containing the viscous pressure drop component, and the physical meaning of the seepage parameters being unclear.

[0007] Fourth, existing technologies cannot truly reflect the discontinuous occurrence information of microscopic residual oil in macroscopic seepage parameters.

[0008] Therefore, it is urgent to propose a cross-scale parameter calculation method for the discontinuous evolution of microscopic residual oil in medium- and high-permeability reservoirs, to solve the problem of the lack of systematic engineering methods for the discontinuous occurrence of residual oil in medium- and high-permeability reservoirs during the ultra-high water cut period, and to realize the true reconstruction of the complete evolution history of residual oil in medium- and high-permeability reservoirs from continuous phase to discontinuous phase. Summary of the Invention

[0009] This invention aims to solve the above-mentioned problems and proposes a cross-scale parameter calculation method for the discontinuous evolution of microscopic residual oil in medium- and high-permeability reservoirs. By combining the full dynamic capture of the complete evolution history of residual oil from continuous to discontinuous phase, the extraction of effective permeability and intrinsic capillary pressure based on the physical mechanism decoupling method, and the construction and mapping of constitutive parameter curves containing microscopic information, this invention solves the problem that the large amount of residual oil in the ultra-high water-cut period of medium- and high-permeability reservoirs exists in a discontinuous form, making it difficult to accurately characterize the seepage parameters. This provides a basis for guiding the accurate reconstruction of the discontinuous evolution of microscopic residual oil in medium- and high-permeability reservoirs.

[0010] The present invention adopts the following technical solution: A method for calculating cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs includes the following steps: Step 1: Select the target section in the medium-to-high permeability reservoir, obtain core samples from the target section, and construct a three-dimensional digital core model; Step 2: Upgrade the oil-water two-phase micro-seepage equation of the three-dimensional digital core model across scales to obtain the macro-seepage equation of the three-dimensional digital core model, which is used to obtain micro-seepage information. Step 3: Use a three-dimensional digital core model to perform full dynamic displacement simulation of micro two-phase flow, obtain the micro dynamic process of oil and water phases inside the three-dimensional digital core model, obtain the global water saturation in real time during the simulation and automatically collect the micro spatial configuration of the oil-water interface by performing topological snapshots, and obtain the displacement snapshot of the three-dimensional digital core model. Step 4: Extract capillary pressure inside the three-dimensional digital core model from the displacement snapshot of the three-dimensional digital core model based on the interface natural relaxation static equilibrium method, and construct a capillary pressure dataset. Step 5: Extract effective permeability from the displacement snapshot of the three-dimensional digital core model based on the capillary force steady-state seepage method, and construct an effective permeability dataset. Step 6: Fit the cross-scale constitutive parameter curve containing microscopic residual oil discontinuous occurrence information based on the capillary pressure dataset and permeability dataset, and map it into the reservoir numerical simulator to obtain the capillary pressure and relative permeability of the target segment in real time.

[0011] Preferably, in step 1, a core sample that can reflect the pore structure characteristics is obtained in the target layer, the porosity and permeability of the core sample are measured, and then the core sample is CT scanned. After image filtering and noise reduction and threshold segmentation binarization processing are performed on the CT scan image of the core sample, the pore throat radius distribution, average coordination number and pore throat ratio are extracted to construct a three-dimensional digital core model of the core sample.

[0012] Preferably, step 2 includes the following steps: Step 2.1: Set the oil-water two-phase micro-flow equations in the three-dimensional digital core model, including the Navier-Stokes equations, the continuity equation, and the solid wall boundary conditions. The Navier-Stokes equations are as follows: ; In the formula, Phase identifier, used to indicate the oil phase or aqueous phase ; , They are respectively Phase density and dynamic viscosity; For velocity field; For pressure field; For time; It is the acceleration due to gravity; For the strain rate tensor, ,in, It is the transpose matrix; For gradient operators; The continuity equation is: ; The solid wall boundary conditions are as follows: ; Step 2.2: Based on the asymptotic homogenization theory, the microscopic seepage equation of the oil-water two-phase flow is upgraded across scales to obtain the macroscopic seepage equation, which is used to obtain microscopic seepage information, including the mass conservation equation, the generalized Darcy equation, and the closure condition. in, The mass conservation equation is: ; In the formula, Porosity; for Phase saturation; The generalized Darcy equation is: ; ; in, , ; , ; In the formula, The Darcy velocity is the velocity of the aqueous phase. is the principal coefficient of the water phase, used to represent the contribution of the driving force of the water phase to the water phase velocity; For the water phase pressure field; The density of the aqueous phase; , The cross-coupling coefficient is used to represent the drag effect of one phase flow on the other phase flow in an oil-water two-phase flow. The effective permeability of the aqueous phase; The dynamic viscosity of the aqueous phase; The density is the oil phase density. The Darcy flow velocity is the oil phase. This is the principal coefficient of the oil phase, used to represent the contribution of the driving force of the oil phase to the oil phase velocity; For oil phase pressure field; The effective permeability of the oil phase; The dynamic viscosity of the oil phase; This refers to absolute penetration rate; The relative permeability of the aqueous phase; The relative permeability of the oil phase; The closure condition is: , ; In the formula, Water saturation; Oil saturation; This refers to capillary pressure.

[0013] Preferably, step 4 includes the following sub-steps: Step 4.1: For each displacement snapshot obtained in Step 3, obtain the volume fraction field in the displacement snapshot, determine the spatial distribution area of ​​oil and water phases inside the three-dimensional digital core model, and reset the velocity field and pressure field to zero to eliminate the inertial effect and viscous pressure drop left inside the three-dimensional digital core model during the displacement simulation. In addition, set the inlet and outlet boundaries of the three-dimensional digital core model to zero flux. Step 4.2: Use the solver to perform iterative calculations to allow the internal interface of the three-dimensional digital core model to relax naturally under the drive of surface tension, gradually eliminate the non-equilibrium deformation of the interface caused by dynamic displacement and converge to the static equilibrium configuration. Use the volume fraction field to perform phase weighted averaging of the pressure field to determine the volume average pressure of the oil phase and water phase respectively, and calculate the capillary pressure inside the three-dimensional digital core model under the current state. Step 4.3: Obtain the measured water saturation of the 3D digital core model after natural interface relaxation, and combine it with the current capillary pressure to obtain capillary pressure data points. ,in, For data point sequence numbers, For the first Measured water saturation at each data point For the first Capillary pressure at each data point; Step 4.4: Construct a capillary pressure dataset based on the capillary pressure data points obtained from each displacement snapshot.

[0014] Preferably, in step 4.2, the formula for calculating the capillary pressure is: ; in, ; ; In the formula, At the current water saturation level Capillary pressure; Water saturation; For oil phase pressure field; For the water phase pressure field; The pressure field output by the solver; For a volume fraction field, when When represents the pure oil phase, Time indicates the pure aqueous phase; The computational domain is the pore space. The volume of the pore space; Preferably, step 5 includes the following steps: Step 5.1: For each displacement snapshot obtained in Step 3, obtain the volume fraction field in the displacement snapshot, determine the interface position of the oil phase and water phase inside the three-dimensional digital core model, and reset the velocity field and pressure field to zero to eliminate the inertial effect and viscous pressure drop left inside the three-dimensional digital core model during the displacement simulation. In conjunction with setting the volume fraction field as a fixed scalar field, it is only used to determine the density and viscosity distribution of the oil phase and water phase in the pore space, and the interfacial tension coefficient is set to zero. Step 5.2: Using constant volume force as the driving pressure gradient, apply the driving force sequentially along the three orthogonal coordinate directions of the three-dimensional digital core model, and perform steady-state seepage simulation separately in each direction until the volume flow rate of each phase in the oil-water two-phase flow converges to a steady state. Then, obtain the Darcy apparent velocity of the oil phase and the water phase, and use Darcy's law to back-calculate and determine the effective permeability tensor components of the oil phase and the water phase. Next, the three-dimensional digital core model was fully saturated with single-phase fluids, using both oil and water phases. Driving forces were then applied sequentially along the three orthogonal coordinate directions of the three-dimensional digital core model, and single-phase seepage simulations were performed in each direction until the flow volumetric flow rate converged to a steady state. Finally, Darcy's law was used to back-calculate and determine the absolute permeability. ; Step 5.3: Based on the original water saturation and the corresponding effective permeability of each phase in the three-dimensional digital core model, determine the permeability data points. ,in, For data point sequence numbers, For the first The original water saturation of each data point For the first Among the data points Effective permeability of the phase; Step 5.4: Construct a penetration rate dataset based on the penetration rate data points obtained from each displacement snapshot.

[0015] Preferably, in step 5.2, the Darcy apparent velocity calculation formula for each phase in the oil-water two-phase flow is as follows: ; In the formula, For phase identification; The tensor component index; for Phase in Darcy apparent velocity in each direction; For the local velocity field at the pore scale The first phase One effective penetration rate tensor component; The total volume of the representative volume element; The computational domain is the pore space. for The volume fraction of the phase, where, for the oil phase, For the aqueous phase, ; The formula for calculating the effective permeability tensor component based on Darcy's law is as follows: ; In the formula, for The effective permeability tensor in the first phase The first direction One component; Water saturation; for The dynamic viscosity of the phase; For along the first A constant volume force applied in a specific direction.

[0016] Preferably, in step 6, regression fitting is performed on the data points in the capillary pressure dataset and the permeability dataset to obtain the capillary pressure curve and the effective permeability curve of each phase. Combined with the absolute permeability obtained from the single-phase seepage simulation in step 5, the relative permeability curve of each phase is obtained to obtain the cross-scale seepage constitutive parameter curve including the capillary pressure curve and the relative permeability curve of each phase. The formula for converting the effective permeability to the relative permeability is as follows: ; In the formula, for The relative permeability of the phase; Water saturation; for Effective permeability of the phase; This refers to absolute penetration rate; By mapping the cross-scale flow constitutive parameter curves to the reservoir numerical simulator, the capillary pressure and relative permeability of each phase within the target section can be obtained in real time using the cross-scale flow constitutive parameter curves, thereby realizing the cross-scale transmission of microscopic physical mechanisms to reservoir-scale production dynamics.

[0017] The present invention has the following beneficial effects: This invention proposes a cross-scale parameter calculation method for the discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs. Starting with a typical core sample from a specific reservoir segment, a saturation snapshot containing discontinuous phase topological information is obtained through fully dynamic microscopic displacement simulation. Constitutive parameters are extracted from the same snapshot via two independent paths. One path uses the interface natural relaxation static equilibrium method to extract intrinsic capillary pressure using the volume-averaged pressure difference between the two phases in equilibrium. The other path uses the capillary-force-free steady-state flow method to extract effective permeability after eliminating interfacial tension at the frozen interface of the original snapshot. The two paths are independent of each other, achieving complete physical decoupling of capillary forces and viscous forces. The obtained constitutive parameters are then mapped to a macroscopic reservoir numerical simulator.

[0018] This invention achieves full dynamic capture of the complete evolution history of the remaining oil transitioning from the continuous phase to the discontinuous phase. It extracts effective permeability and intrinsic capillary pressure separately through physical mechanism decoupling, and maps the extracted constitutive parameters containing microscopic information to the reservoir numerical simulator. It obtains the capillary pressure and relative permeability of each phase in the reservoir in real time, realizing that macroscopic numerical simulation can obtain physical information at the microscale, and provides technical support for the fine numerical simulation of medium-high permeability reservoirs during the ultra-high water cut period. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for calculating cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-high permeability reservoirs according to the present invention.

[0020] Figure 2 This is a schematic diagram of a three-dimensional digital core model.

[0021] Figure 3 The diagram shows the spatial configuration of the microscopic oil-water interface.

[0022] Figure 4 This is a schematic diagram of the capillary pressure curve obtained by the method of the present invention.

[0023] Figure 5 This is a schematic diagram of the relative permeability curve obtained by the method of the present invention.

[0024] Figure 6 This is a schematic diagram of the macroscopic target reservoir containing physical information on the discontinuous occurrence of residual oil, as presented in this invention. Detailed Implementation

[0025] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0026] This invention proposes a method for calculating cross-scale parameters of the discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs, such as... Figure 1 As shown, it includes the following steps: Step 1: Select the target layer in the medium-to-high permeability reservoir, obtain core samples from the target layer, and construct a three-dimensional digital core model.

[0027] In this embodiment, core samples reflecting pore structure characteristics are obtained from the target layer. The porosity and permeability of the core samples are measured to ensure they fall within the main range of the physical property distribution of the target layer. Then, the core samples are subjected to micron-resolution CT scans. After image filtering, noise reduction, and threshold segmentation binarization of the CT scan images, the pore throat radius distribution, average coordination number, and pore throat ratio are extracted. The consistency with existing mercury intrusion porosimetry data for the target layer is verified, and a three-dimensional digital core model of the core sample is constructed. Figure 2 As shown.

[0028] Step 2 involves upgrading the oil-water two-phase micro-flow equation of the 3D digital core model across scales to obtain the macro-flow equation of the 3D digital core model, which is used to acquire micro-flow information. This specifically includes the following steps: Step 2.1: At the pore scale (i.e., microscale), set the oil-water two-phase micro-flow equations in the three-dimensional digital core model, including the Navier-Stokes equation, the continuity equation, and the solid wall boundary conditions.

[0029] in, The Navier-Stokes equations are as follows: ; In the formula, Phase identifier, used to indicate the oil phase or aqueous phase ; , They are respectively Phase density and dynamic viscosity; For velocity field; For pressure field; For time; It is the acceleration due to gravity; For the strain rate tensor, ,in, It is the transpose matrix; This is the gradient operator.

[0030] The continuity equation is: .

[0031] The solid wall boundary conditions are as follows: .

[0032] Step 2.2: Based on the asymptotic homogenization theory, the microscopic seepage equation of the oil-water two-phase flow is upgraded across scales to obtain the macroscopic seepage equation, which is used to obtain microscopic seepage information, including the mass conservation equation, the generalized Darcy equation, and the closure condition.

[0033] in, The mass conservation equation is: ; In the formula, Porosity; for Phase saturation.

[0034] The generalized Darcy equation is: ; ; in, , ; , ; In the formula, The Darcy velocity is the velocity of the aqueous phase. is the principal coefficient of the water phase, used to represent the contribution of the driving force of the water phase to the water phase velocity; For the water phase pressure field; The density of the aqueous phase; , The cross-coupling coefficient is used to represent the drag effect of one phase flow on the other phase flow in an oil-water two-phase flow. The effective permeability of the aqueous phase; The dynamic viscosity of the aqueous phase; The density is the oil phase density. The Darcy flow velocity is the oil phase. This is the principal coefficient of the oil phase, used to represent the contribution of the driving force of the oil phase to the oil phase velocity; For oil phase pressure field; The effective permeability of the oil phase; The dynamic viscosity of the oil phase; This refers to absolute penetration rate; The relative permeability of the aqueous phase; The relative permeability of the oil phase.

[0035] The closure condition is: , ; In the formula, Water saturation; Oil saturation; This is the capillary pressure, specifically the difference between the macroscopic average pressure of the oil phase and the macroscopic average pressure of the water phase.

[0036] In this embodiment, the effective permeability is a total quantity that includes the absolute permeability of the rock and the actual conductivity of the fluid phase at a specific saturation level. The absolute permeability is the permeability when the pore space is completely saturated with a single-phase fluid, which can be directly obtained by performing single-phase steady-state seepage simulation on a digital core.

[0037] In practical engineering applications, the cross-coupling coefficient , The magnitude of the permeability is usually much smaller than that of the principal coefficients, and existing commercial reservoir numerical simulators generally adopt the standard Darcy's law. Therefore, the method of this invention directly extracts the effective permeability of the aqueous phase in step 5. and the effective penetration rate of the oil phase And as a whole quantity, it corresponds to the principal coefficient of the aqueous phase. Oil phase principal coefficient The permeability characterization in this context refers to the permeability of each phase driven by its own pressure gradient. In step 6, when mapping to a commercial reservoir numerical simulator, the effective permeability of the aqueous phase is used. and oil phase effective permeability Divide by the absolute permeability obtained from single-phase flow simulation This can be converted into the relative permeability input curve required by the simulator. When it is necessary to further extract the cross-coupling coefficient, in the steady-state seepage simulation in step 5, the driving force of a single phase can be applied while the volume-average velocity response of the other phase is statistically analyzed, thereby calculating the cross-mobility. Based on the macroscopic seepage equation obtained after the cross-scale upgrade, the microscopic seepage parameters to be solved are thus determined, namely, the effective permeability tensor and capillary pressure.

[0038] Step 3: Use a three-dimensional digital core model to perform full dynamic displacement simulation of microscopic two-phase flow, obtain the microscopic dynamic process of oil and water phases inside the three-dimensional digital core model, obtain the global water saturation in real time during the simulation and automatically collect the spatial configuration of the microscopic oil-water interface by performing topological snapshots, and obtain the displacement snapshot of the three-dimensional digital core model.

[0039] In this embodiment, a three-dimensional digital core model is used to simulate the full dynamic displacement of microscopic two-phase flow. The fluid volume method is used to solve the incompressible Navier-Stokes equations to simulate the full dynamic displacement of oil and water. The simulation process covers the complete water drive process from the initial high oil saturation to the ultra-high water cut stage, and fully captures the microscopic dynamic processes such as snap-off, oil filament breakage to form isolated oil droplets, oil droplet migration and re-coagulation.

[0040] Then, based on the saturation threshold, an automatic saving mechanism is set to collect the global water saturation of the three-dimensional digital core model in real time. When water saturation When the preset sampling node value is reached or exceeded for the first time, the complete flow field data at the current moment (including volume fraction field, pressure field, and velocity field) is automatically saved, and a topological snapshot is taken under the current water saturation conditions. That is, a record of the microscopic oil-water interface spatial configuration from the displacement history to the current moment is obtained, such as... Figure 3 As shown.

[0041] Step 4: Extract capillary pressure from the displacement snapshot of the 3D digital core model based on the interface natural relaxation static equilibrium method, and construct a capillary pressure dataset, including the following sub-steps: Step 4.1: For each displacement snapshot obtained in Step 3, obtain the volume fraction field in the displacement snapshot, determine the spatial distribution area of ​​oil and water phases inside the three-dimensional digital core model, and reset the velocity field and pressure field to zero to eliminate the inertial effect and viscous pressure drop left inside the three-dimensional digital core model during the displacement simulation. In addition, set the inlet and outlet boundaries of the three-dimensional digital core model to zero flux.

[0042] Step 4.2 involves iterative calculations using a solver, allowing the interfaces within the 3D digital core model to relax naturally under surface tension. This gradually eliminates the non-equilibrium deformation of the interfaces caused by dynamic displacement and converges to a static equilibrium configuration. Once the interfaces converge to static equilibrium, the flow field inside the 3D digital core model is completely static, and the pressure difference between the oil and water phases in the pressure field is entirely and solely composed of the Laplace pressure difference generated by the interface bending morphology. A volume fraction field is used to perform a phase-weighted average of the pressure field to determine the volume-average pressures of the oil and water phases, and the capillary pressure inside the 3D digital core model under the current state is calculated.

[0043] Specifically, the formula for calculating the capillary pressure is as follows: ; in, ; ; In the formula, At the current water saturation level Capillary pressure; Water saturation; For oil phase pressure field; For the water phase pressure field; The pressure field output by the solver; For a volume fraction field, when When represents the pure oil phase, Time represents the pure aqueous phase, and volume fraction field The interface transition area is naturally integrated. The pressure is distributed according to the proportion of each phase, and can be directly calculated using the output field of the fluid volume method without the need for interface reconstruction; The computational domain is the pore space. This refers to the volume of the pore space.

[0044] In this embodiment, for a discontinuous configuration containing multiple isolated oil droplets, the internal pressure of each droplet varies due to its different interface curvature. The volume-averaged pressure naturally weights each droplet according to its volume fraction, providing an equivalent capillary pressure consistent with the macroscopic thermodynamic definition. Furthermore, the pressure field, as a direct output variable of the solver, offers significantly higher numerical accuracy than the indirect method of reconstructing the interface from the volume fraction field and then calculating the curvature, effectively avoiding the numerical oscillation problem in interface curvature calculation in the fluid volume method.

[0045] Step 4.3: Obtain the measured water saturation of the 3D digital core model after natural interface relaxation, and combine it with the current capillary pressure to obtain capillary pressure data points. ,in, For data point sequence numbers, For the first Measured water saturation at each data point For the first Capillary pressure at each data point.

[0046] Step 4.4: Construct a capillary pressure dataset based on the capillary pressure data points obtained from each displacement snapshot.

[0047] Step 5: Extract effective permeability from the displacement snapshot of the 3D digital core model using the capillary-free steady-state flow method, construct an effective permeability dataset, and eliminate the interfacial tension effect by fixing the oil-water interface position in the original displacement snapshot, so that the flow resistance comes entirely from viscous shear force, thereby eliminating the interference of capillary forces such as the Jamin effect on permeability. Specifically, this includes the following steps: Step 5.1: For each displacement snapshot obtained in Step 3, obtain the volume fraction field in the displacement snapshot to determine the interface position between the oil and water phases inside the three-dimensional digital core model. Reset the velocity and pressure fields to zero to eliminate the inertial effect and viscous pressure drop left inside the three-dimensional digital core model during the displacement simulation. In conjunction with setting the volume fraction field as a fixed scalar field, it is only used to determine the density and viscosity distribution of the oil and water phases in the pore space. Set the interfacial tension coefficient to zero to completely eliminate capillary effects such as Laplace pressure difference and Jamin effect, and degenerate the flow into a low-velocity viscous flow problem under a given viscosity spatial distribution.

[0048] Step 5.2: Using a constant volume force as the driving pressure gradient, control the amplitude of the driving force to achieve the desired pore Reynolds number. The value is much less than 1, thus ensuring that the flow is within the linear Darcy flow range. In order to obtain the complete effective permeability tensor, the driving force is applied sequentially along the three orthogonal coordinate directions of the three-dimensional digital core model, and steady-state flow simulation is performed separately in each direction until the volumetric flow rates of each phase in the oil-water two-phase flow converge to a steady state. Then, the Darcy apparent velocities of the oil and water phases are obtained, and the effective permeability tensor components of the oil and water phases are determined by back-calculation using Darcy's law.

[0049] Next, the three-dimensional digital core model was fully saturated with single-phase fluids, using both oil and water phases. Driving forces were then applied sequentially along the three orthogonal coordinate directions of the three-dimensional digital core model, and single-phase seepage simulations were performed in each direction until the flow volumetric flow rate converged to a steady state. Finally, Darcy's law was used to back-calculate and determine the absolute permeability. .

[0050] Specifically, the Darcy apparent velocity calculation formula for each phase in the oil-water two-phase flow is as follows: ; In the formula, For phase identification; The tensor component index; for Phase in Darcy apparent velocity in each direction; For the local velocity field at the pore scale The first phase One effective penetration rate tensor component; The total volume of the representative volume element is equal to the pore space computational domain divided by the porosity, used to ensure that the obtained velocity is the apparent seepage velocity consistent with Darcy's law. The computational domain is the pore space. for The volume fraction of the phase, where, for the oil phase, For the aqueous phase, .

[0051] The formula for calculating the effective permeability tensor component based on Darcy's law is as follows: ; In the formula, for The effective permeability tensor in the first phase The first direction One component; Water saturation; for The dynamic viscosity of the phase; For along the first A constant volume force applied in a specific direction.

[0052] Step 5.3: Based on the original water saturation and the corresponding effective permeability of each phase in the three-dimensional digital core model, determine the permeability data points. ,in, For data point sequence numbers, For the first The original water saturation of each data point For the first Among the data points Effective permeability of the phase.

[0053] Step 5.4: Construct a penetration rate dataset based on the penetration rate data points obtained from each displacement snapshot.

[0054] Step 6: Fit the cross-scale constitutive parameter curve containing microscopic residual oil discontinuous occurrence information based on the capillary pressure dataset and permeability dataset, and map it into the reservoir numerical simulator to obtain the capillary pressure and relative permeability of the target segment in real time.

[0055] In this embodiment, data fitting methods such as the Brooks-Corey model, the Van Genuchten model, or spline interpolation are used to fit curves to the data points in the capillary pressure dataset and the permeability dataset, respectively, to obtain the capillary pressure curve and the effective permeability curve of each phase. Combined with the absolute permeability obtained from the single-phase flow simulation in step 5, the relative permeability curve of each phase is obtained, resulting in a multi-scale constitutive parameter curve for flow, including the capillary pressure curve and the relative permeability curves of each phase. Figure 4 and Figure 5 As shown.

[0056] Specifically, the formula for converting the effective permeability into relative permeability is as follows: ; In the formula, for The relative permeability of the phase; Water saturation; for Effective permeability of the phase; This represents the absolute penetration rate.

[0057] By mapping multi-scale flow constitutive parameter curves to a reservoir numerical simulator, the capillary pressure and relative permeability of each phase within the target section are obtained in real time using these curves. The macroscopic target reservoir, containing physical information about the discontinuous occurrence of remaining oil, is then displayed in the reservoir numerical simulator. Figure 6 As shown, this enables the cross-scale transmission of microscopic physical mechanisms to reservoir-scale production dynamics.

[0058] In summary, the method of this invention constructs constitutive parameter curves by accurately extracting information on the discontinuous occurrence of microscopic residual oil, and uses the relative permeability curve and capillary pressure curve in the constitutive parameter curves to obtain the relative permeability and capillary pressure inside the target reservoir in real time, thus realizing the cross-scale transmission of microscopic physical mechanisms to reservoir-scale production dynamics prediction.

[0059] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for calculating cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs, characterized in that, Includes the following steps: Step 1: Select the target section in the medium-to-high permeability reservoir, obtain core samples from the target section, and construct a three-dimensional digital core model; Step 2: Upgrade the oil-water two-phase micro-seepage equation of the three-dimensional digital core model across scales to obtain the macro-seepage equation of the three-dimensional digital core model, which is used to obtain micro-seepage information. Step 3: Use a three-dimensional digital core model to perform full dynamic displacement simulation of micro two-phase flow, obtain the micro dynamic process of oil and water phases inside the three-dimensional digital core model, obtain the global water saturation in real time during the simulation and automatically collect the micro spatial configuration of the oil-water interface by performing topological snapshots, and obtain the displacement snapshot of the three-dimensional digital core model. Step 4: Extract capillary pressure inside the three-dimensional digital core model from the displacement snapshot of the three-dimensional digital core model based on the interface natural relaxation static equilibrium method, and construct a capillary pressure dataset. Step 5: Extract effective permeability from the displacement snapshot of the three-dimensional digital core model based on the capillary force steady-state seepage method, and construct an effective permeability dataset. Step 6: Fit the cross-scale constitutive parameter curve containing microscopic residual oil discontinuous occurrence information based on the capillary pressure dataset and permeability dataset, and map it into the reservoir numerical simulator to obtain the capillary pressure and relative permeability of the target segment in real time.

2. The method for calculating the cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs according to claim 1, characterized in that, In step 1, a core sample that reflects the pore structure characteristics is obtained from the target layer. The porosity and permeability of the core sample are measured. Then, the core sample is CT scanned. After image filtering, noise reduction and threshold segmentation binarization are performed on the CT scan image of the core sample, the pore throat radius distribution, average coordination number and pore throat ratio are extracted to construct a three-dimensional digital core model of the core sample.

3. The method for calculating the cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs according to claim 1, characterized in that, Step 2 includes the following steps: Step 2.1: Set the oil-water two-phase micro-flow equations in the three-dimensional digital core model, including the Navier-Stokes equations, the continuity equation, and the solid wall boundary conditions. The Navier-Stokes equations are as follows: ； In the formula, Phase identifier, used to indicate the oil phase or aqueous phase ; , They are respectively Phase density and dynamic viscosity; For velocity field; For pressure field; For time; It is the acceleration due to gravity; For the strain rate tensor, ,in, It is the transpose matrix; For gradient operators; The continuity equation is: ； The solid wall boundary conditions are as follows: ； Step 2.2: Based on the asymptotic homogenization theory, the microscopic seepage equation of the oil-water two-phase flow is upgraded across scales to obtain the macroscopic seepage equation, which is used to obtain microscopic seepage information, including the mass conservation equation, the generalized Darcy equation, and the closure condition. in, The mass conservation equation is: ； In the formula, Porosity; for Phase saturation; The generalized Darcy equation is: ； ； in, ， ； ， ； In the formula, The Darcy velocity is the velocity of the aqueous phase. is the principal coefficient of the water phase, used to represent the contribution of the driving force of the water phase to the water phase velocity; For the water phase pressure field; The density of the aqueous phase; , The cross-coupling coefficient is used to represent the drag effect of one phase flow on the other phase flow in an oil-water two-phase flow. The effective permeability of the aqueous phase; The dynamic viscosity of the aqueous phase; The density is the oil phase density. The Darcy flow velocity is the oil phase. This is the principal coefficient of the oil phase, used to represent the contribution of the driving force of the oil phase to the oil phase velocity; For oil phase pressure field; The effective permeability of the oil phase; The dynamic viscosity of the oil phase; This refers to absolute penetration rate; The relative permeability of the aqueous phase; The relative permeability of the oil phase; The closure condition is: ， ； In the formula, Water saturation; Oil saturation; This refers to capillary pressure.

4. The method for calculating the cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs according to claim 1, characterized in that, Step 4 includes the following sub-steps: Step 4.1: For each displacement snapshot obtained in Step 3, obtain the volume fraction field in the displacement snapshot, determine the spatial distribution area of ​​oil and water phases inside the three-dimensional digital core model, and reset the velocity field and pressure field to zero to eliminate the inertial effect and viscous pressure drop left inside the three-dimensional digital core model during the displacement simulation. In addition, set the inlet and outlet boundaries of the three-dimensional digital core model to zero flux. Step 4.2: Use the solver to perform iterative calculations to allow the internal interface of the three-dimensional digital core model to relax naturally under the drive of surface tension, gradually eliminate the non-equilibrium deformation of the interface caused by dynamic displacement and converge to the static equilibrium configuration. Use the volume fraction field to perform phase weighted averaging of the pressure field to determine the volume average pressure of the oil phase and water phase respectively, and calculate the capillary pressure inside the three-dimensional digital core model under the current state. Step 4.3: Obtain the measured water saturation of the 3D digital core model after natural interface relaxation, and combine it with the current capillary pressure to obtain capillary pressure data points. ,in, For data point sequence numbers, For the first Measured water saturation at each data point For the first Capillary pressure at each data point; Step 4.4: Construct a capillary pressure dataset based on the capillary pressure data points obtained from each displacement snapshot.

5. The method for calculating the cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs according to claim 4, characterized in that, In step 4.2, the formula for calculating the capillary pressure is: ； in, ； ； In the formula, At the current water saturation level Capillary pressure; Water saturation; For oil phase pressure field; For the water phase pressure field; The pressure field output by the solver; For a volume fraction field, when When represents the pure oil phase, Time indicates the pure aqueous phase; The computational domain is the pore space. The volume of the pore space; The method for calculating the discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs across scales according to claim 1 is characterized in that step 5 includes the following steps: Step 5.1: For each displacement snapshot obtained in Step 3, obtain the volume fraction field in the displacement snapshot, determine the interface position of the oil phase and water phase inside the three-dimensional digital core model, and reset the velocity field and pressure field to zero to eliminate the inertial effect and viscous pressure drop left inside the three-dimensional digital core model during the displacement simulation. In conjunction with setting the volume fraction field as a fixed scalar field, it is only used to determine the density and viscosity distribution of the oil phase and water phase in the pore space, and the interfacial tension coefficient is set to zero. Step 5.2: Using constant volume force as the driving pressure gradient, apply the driving force sequentially along the three orthogonal coordinate directions of the three-dimensional digital core model, and perform steady-state seepage simulation separately in each direction until the volume flow rate of each phase in the oil-water two-phase flow converges to a steady state. Then, obtain the Darcy apparent velocity of the oil phase and the water phase, and use Darcy's law to back-calculate and determine the effective permeability tensor components of the oil phase and the water phase. Next, the three-dimensional digital core model was fully saturated with single-phase fluids, using both oil and water phases. Driving forces were then applied sequentially along the three orthogonal coordinate directions of the three-dimensional digital core model, and single-phase seepage simulations were performed in each direction until the flow volumetric flow rate converged to a steady state. Finally, Darcy's law was used to back-calculate and determine the absolute permeability. ; Step 5.3: Based on the original water saturation and the corresponding effective permeability of each phase in the three-dimensional digital core model, determine the permeability data points. ,in, For data point sequence numbers, For the first The original water saturation of each data point For the first Among the data points Effective permeability of the phase; Step 5.4: Construct a penetration rate dataset based on the penetration rate data points obtained from each displacement snapshot.

6. The method for calculating the cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs according to claim 5, characterized in that, In step 5.2, the Darcy apparent velocity calculation formula for each phase in the oil-water two-phase flow is as follows: ； In the formula, For phase identification; The tensor component index; for Phase in Darcy apparent velocity in each direction; For the local velocity field at the pore scale The first phase One effective penetration rate tensor component; The total volume of the representative volume element; The computational domain is the pore space. for The volume fraction of the phase, where, for the oil phase, For the aqueous phase, ; The formula for calculating the effective permeability tensor component based on Darcy's law is as follows: ； In the formula, for The effective permeability tensor in the first phase The first direction One component; Water saturation; for The dynamic viscosity of the phase; For along the first A constant volume force applied in a specific direction.

7. The method for calculating the cross-scale parameters of discontinuous evolution of microscopic residual oil in medium-to-high permeability reservoirs according to claim 1, characterized in that, In step 6, regression fitting is performed on the data points in the capillary pressure dataset and the permeability dataset to obtain the capillary pressure curve and the effective permeability curve of each phase. Combined with the absolute permeability obtained from the single-phase seepage simulation in step 5, the relative permeability curve of each phase is obtained, resulting in a cross-scale seepage constitutive parameter curve including the capillary pressure curve and the relative permeability curve of each phase. The formula for converting the effective permeability to the relative permeability is as follows: ； In the formula, for The relative permeability of the phase; Water saturation; for Effective permeability of the phase; This refers to absolute penetration rate; By mapping the cross-scale flow constitutive parameter curves to the reservoir numerical simulator, the capillary pressure and relative permeability of each phase within the target section can be obtained in real time using the cross-scale flow constitutive parameter curves, thereby realizing the cross-scale transmission of microscopic physical mechanisms to reservoir-scale production dynamics.

Images