A simulation method for synergistic oil displacement by CO2 and CH4 based on LBM

Abstract

This invention relates to the field of carbon dioxide and methane synergistic oil displacement simulation technology, and discloses a CO2 and CH4 synergistic oil displacement simulation method based on LBM. This method addresses the problems in existing synergistic oil displacement simulations at the pore scale, which cannot distinguish the oil displacement contributions of different gas components and characterize the dynamic changes in oil phase viscosity. The method includes obtaining core samples, performing CT scanning and threshold segmentation, and selecting characteristic pore structures as the simulation computational domain; constructing a synergistic oil displacement simulation model using a three-dimensional D3Q19 discrete model and MRT-LBM format, solving the flow and concentration equations, and dynamically adjusting the oil phase viscosity; and visually analyzing the simulation results using Tecplot to clarify the oil displacement effect. This invention accurately simulates the synergistic oil displacement process, providing reliable numerical support for optimizing mixed gas injection schemes in shale gas reservoirs.

Description

Technical Field

[0001] This invention relates to the field of carbon dioxide and methane synergistic displacement simulation technology, and specifically discloses a CO2 and CH4 synergistic displacement simulation method based on LBM. Background Technology

[0002] In existing technologies, gas-driven oil recovery is an important development method widely used in tight oil reservoirs. Among these, carbon dioxide (CO2) Methane ( ) is considered a displacement medium with significant production potential due to its easy solubility in crude oil, ability to reduce oil phase viscosity, and improved flowability; As a major component of natural gas, it possesses good compressibility and diffusion capabilities, giving it an advantage in expanding the displacement swept volume. In engineering practice, it has gradually developed… and The synergistic oil displacement method using mixed gas injection aims to balance interface regulation capability and displacement coverage.

[0003] From a numerical simulation perspective, traditional reservoir numerical simulations are typically based on the continuum assumption, describing fluid flow at a macroscopic scale. While these methods possess mature theoretical and computational frameworks for engineering-scale predictions, they struggle to directly characterize microscopic mechanisms such as the evolution of the oil-gas interface, the formation of local gas channeling pathways, and the distribution of residual oil at the pore scale. With the development of digital core technology, high-resolution two-dimensional or three-dimensional digital images of the core pore structure can be obtained using experimental techniques such as X-ray computed tomography (XCT), scanning electron microscopy (SEM), and focused ion beam scanning electron microscopy (FIB-SEM). Through image processing and voxel reconstruction, digital core models that realistically reflect the pore structure characteristics can be constructed in a computer, providing a foundation for pore-scale flow simulation.

[0004] The Lattice Boltzmann Method (LBM), as a mesoscopic numerical method, is widely used in the study of porous media flow and multiphase flow problems due to its regular lattice-based approach, ease of handling complex boundaries, and good parallel computing characteristics. By introducing a multiphase model and interaction force terms, LBM can simulate oil-gas two-phase flow processes at the pore scale and output physical quantities such as velocity field, density field, and phase saturation distribution.

[0005] In existing numerical simulation studies of gas-driven oil displacement at the pore scale, the closest technical solutions to this invention mainly focus on single-component gas-driven oil displacement simulation methods based on the multiphase lattice Boltzmann method. These methods typically include the following basic steps: Existing technologies primarily rely on digital cores and the multiphase lattice Boltzmann method for single-component gas-driven oil displacement at the pore scale. These methods typically first acquire images of the core pore structure using imaging techniques such as X-ray tomography, scanning electron microscopy, or focused ion beam scanning electron microscopy, and then construct a three-dimensional digital core model. Subsequently, the single-relaxation-time or multi-relaxation-time lattice Boltzmann method is used to solve for the two-phase flow of oil and gas in the pore structure. A stable interface is formed by setting oil-gas interaction force terms to achieve numerical simulation of the gas displacement of the oil phase. Finally, post-processing is used to obtain results such as the displacement front morphology, oil phase saturation distribution, and residual oil occurrence characteristics. In existing methods, the injected gas is usually treated as a single equivalent gas phase, or the influence of gas on interfacial tension and oil phase viscosity is only indirectly characterized by adjusting empirical parameters. The methods do not address the specific effects of gas on interfacial tension and oil phase viscosity. and A unified coupled description is provided for the migration, diffusion, local enrichment of different gas components in the pore structure under mixed gas injection conditions, and their differential effects on oil phase properties and interfacial propulsion behavior. Summary of the Invention

[0006] This invention provides a CO2 and CH4 synergistic oil displacement simulation method based on LBM, to solve the problem that traditional gas injection oil displacement simulation is difficult to accurately characterize at the pore scale. and The question of the synergistic dissolution and viscosity reduction mechanism.

[0007] This invention provides the following technical solution: a simulation method for synergistic oil displacement by CO2 and CH4 based on LBM, comprising the following steps: S1. Digital core construction: The core is subjected to three-dimensional imaging to obtain grayscale images. The pores and solid skeleton are divided by grayscale differences, discretized into NX×NY×NZ regular voxel meshes, and a structure identifier array is generated as the geometric boundary conditions for flow simulation. S2. Establish a lattice Boltzmann model, and use the three-dimensional D3Q19MRT-LBM method to construct an oil-gas two-phase flow model. Based on the voxel grid, divide the flow region and solid boundary, and solve the flow distribution of the oil phase and gas phase. S3. Coupled dissolution and synergistic viscosity reduction mechanism, introduced based on the oil-gas two-phase flow model. and The component convection-diffusion transport model is based on the oil phase adjacent unit. and The local concentration was used to determine the dissolution effect, and a dissolution criterion was established. The equivalent viscosity of the oil phase was dynamically corrected based on the gas concentration to achieve synergistic viscosity reduction and displacement characterization. S4. Simulation results analysis: Export fluid density, viscosity, and velocity field data, perform visualization processing, and obtain data at the pore scale. and Dynamic distribution results of synergistic displacement.

[0008] Furthermore, in the structure identifier array, identifier value 0 represents the pore flow region, and identifier value 1 represents the solid skeleton region; The voxel grid and the structure identifier array correspond one-to-one in spatial location.

[0009] Furthermore, the construction of the oil-gas two-phase flow model using the three-dimensional D3Q19 MRT-LBM method includes: employing a three-dimensional D3Q19 discrete velocity model, using the multi-relaxation time MRT-LBM scheme, and transforming the matrix... With diagonal relaxation matrix Perform fluid evolution calculations and determine the relaxation time based on the fluid's kinematic viscosity; The three-dimensional D3Q19 discrete velocity model contains 19 ion discrete velocity directions. , The discrete velocity set used to characterize the migration direction and velocity of fluid particles in three-dimensional space is as follows: ; In the formula, For the first The velocity vector corresponding to each discrete velocity direction The direction of ion movement; Employing the multi-relaxation time MRT-LBM scheme, through the transformation matrix With diagonal relaxation matrix The fluid evolution calculation is performed, and the relaxation time is determined based on the fluid's kinematic viscosity using the following formula: ; In the formula, for Time, location The first The density distribution function in each discrete velocity direction, i.e., the distribution state after migration. for Time, location The first The density distribution function along each discrete velocity direction, i.e., the distribution state before migration. For the first A velocity vector in a discrete velocity direction For time step, Let be the transformation matrix. Transformation matrix The inverse matrix, It is a diagonal relaxation matrix. For particles in space and time along The equilibrium density distribution function in the direction.

[0010] Furthermore, the transformation matrix for: .

[0011] Furthermore, the diagonal relaxation matrix for: ; In the formula, The relaxation factor corresponding to the mass conservation moment is set to 1. , , , This is a free parameter, with a value of 1.1. and The relaxation factor for the beam-related moments is set to 1. , Relaxation time; The relaxation time With fluid kinematic viscosity The relationship is: ; In the formula For the speed of sound in a lattice, the value is... .

[0012] Furthermore, the aforementioned introduction of [the concept / method] based on the oil-gas two-phase flow model... and Component convection-diffusion transport models include: Based on the oil-gas two-phase flow model, a description of gas phase component transport is introduced to... and Migration, diffusion and within the pore scale and The enrichment process near the oil-gas interface was characterized; the macroscopic transport behavior of gas components in porous media was described by the convection-diffusion equation. ; In the formula, This represents the rate of change of the concentration of the gaseous component over time. In the middle of the gas phase or The equivalent concentration, The local velocity field is calculated using the LBM-MRT two-phase flow model. denoted as the diffusion coefficient of the gaseous component in the gas phase.

[0013] Furthermore, the method based on the oil phase adjacent unit and Local concentration is used to determine dissolution effects, and dissolution criteria are established, including those obtained through convection-diffusion equations. and Concentration distribution within the pore space and and With the evolution of time, and Concentration distribution within the pore space and and Using the laws of evolution over time as a basis for judgment, establish and Criteria for the dissolving effect on the fluidity of the oil phase; The criterion for dissolution is: Local – Oil relative threshold As Reference concentration threshold, when At that time, the judgment It dissolves into the oil phase and affects the viscosity of the oil phase; The reference concentration threshold is determined by the grid points at the oil-gas interface. The determination is based on the local relative content of the oil phase. When the local relative content of the oil phase is >0.4, it is considered... It plays an auxiliary role in dissolving and diluting, and The improvement in oil phase viscosity is less than ; When both types of dissolution conditions are met simultaneously, synergistic viscosity reduction is triggered, dynamically adjusting the equivalent viscosity of the oil phase to complete the process. Dominant dissolution and viscosity reduction Simulation of synergistic oil displacement with aided dissolution and dilution.

[0014] Furthermore, the equivalent viscosity of the oil phase The equivalent viscosity of the oil phase varies with the local gas component concentration. for: ; In the formula, The initial viscosity of crude oil before gas dissolution occurs. and They are respectively and The solubility viscosity reduction coefficient, and satisfies , and Each is located in adjacent units of the oil phase. and The equivalent concentration.

[0015] Furthermore, S4 specifically includes: the fluid data obtained based on S3 and and Concentration distribution and evolution patterns, establishing - Criteria for dissolution and its effect on oil phase fluidity; based on the oil phase adjacent units Concentration and Concentration, determination The dominant dissolution and viscosity reduction effect and The auxiliary dissolution and dilution effects are used to distinguish the functional states of each gas component in different regions; and the characteristic parameters of each unit obtained from the simulation are exported and visualized in Tecplot software. - Simulation results of synergistic dissolution and viscosity reduction, and oil and gas distribution at different dynamic stages.

[0016] Compared with the prior art, the present invention has the following technical effects:

[0017] This invention constructs a pore-scale model based on the lattice Boltzmann method (LBM). and A refined simulation model for synergistic oil displacement accurately characterizes the flow, diffusion, and dissolution behavior of different gas components, clearly distinguishing between them. The dominant dissolution and viscosity reduction effect and The auxiliary dilution effect enables real-time characterization of the dynamic changes in oil phase viscosity with gas concentration, improving the accuracy and reliability of the simulation and providing reliable numerical support for optimizing mixed gas injection and oil displacement schemes in complex reservoirs such as shale gas reservoirs. Attached Figure Description

[0018] Figure 1 This is a flowchart of the technology of the present invention; Figure 2 This is the original CT scan image of the core sample in this embodiment; Figure 3 This is a three-dimensional structural image of the pore phase of the core after threshold segmentation; Figure 4 This is a three-dimensional structural image of the matrix phase of the core after threshold segmentation; Figure 5 for Schematic diagram of the dominant displacement oil and gas distribution; Figure 6 for Schematic diagram of the dominant displacement oil and gas distribution; Figure 7 The curves show the comparison of crude oil recovery rates under different displacement schemes. Detailed Implementation

[0019] The present invention will be further illustrated below with reference to embodiments. These embodiments are for illustrative purposes only and are not intended to limit the invention in any way. It should be understood that the described embodiments are merely some, not all, of the embodiments described in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0020] A simulation method for synergistic oil displacement by CO2 and CH4 based on LBM, such as Figure 1 As shown, this embodiment takes shale gas reservoir core samples as the research object. First, digital cores are acquired and preprocessed to provide a realistic and reliable pore structure model for subsequent simulations.

[0021] The core sample was scanned in three dimensions using X-ray CT scanning technology with a resolution of 0.1 micrometers. After scanning, a three-dimensional CT image of the core with dimensions of 1000×1000×1000 pixels (length×width×height) was obtained. This image fully presents the microscopic pore and matrix distribution characteristics inside the core. Figure 2 The original CT scan image of the core sample in the example.

[0022] Threshold segmentation was performed on the CT scan images. First, the three-dimensional grayscale images obtained from the CT scans were linearly normalized to a grayscale range of 0–255. Regions with grayscale values ​​greater than 135 were marked as solid skeleton regions, i.e., the matrix, and represented by 1; regions with grayscale values ​​less than or equal to 135 were marked as porous regions and represented by 0. The image was then segmented into two parts: the porous phase and the matrix phase, resulting in the segmented core structure. Figure 3 This is a three-dimensional structure image of the pore phase of the core after threshold segmentation. Figure 4 This is a three-dimensional structural image of the matrix phase of the core after threshold segmentation.

[0023] To balance simulation efficiency and computational accuracy, a 100×100×100 pixel feature pore structure is selected as the computational domain for the collaborative oil displacement simulation in this embodiment within the segmented porous phase region. This feature structure can realistically reflect the core microscopic features of the original core, such as pore connectivity and pore size distribution.

[0024] The obtained 100×100×100 pixel feature structure digital core is imported into the multiphase lattice Boltzmann method (LBM). and In the synergistic oil displacement simulation method, a three-dimensional D3Q19 discrete velocity model and a multiple relaxation time (MRT) LBM calculation scheme are constructed during the simulation process to simulate the oil-gas two-phase flow process; simultaneously, the convection-diffusion equations of the gas components are solved to obtain... and The concentration distribution within the pore space and its evolution over time; based on the gas concentration data triggering the dissolution criterion, in this embodiment... and The dissolution criterion adopts the form of a normalized concentration threshold. Specifically, it is: when the oil phase is adjacent to a cell... When the normalized concentration is greater than 0.2, the vicinity of this unit is considered to be... Reaching an effective dissolution and enrichment state triggers Dominant oil phase viscosity reduction correction; when the oil phase is adjacent to the cell When the normalized concentration is greater than 0.4, it is judged... When conditions for aiding dissolution or dilution are met, triggering Assisted viscosity reduction correction. If both conditions are met simultaneously, a synergistic viscosity reduction effect is triggered, dynamically adjusting the equivalent viscosity of the oil phase to complete the process. Dominant dissolution and viscosity reduction Simulation of synergistic oil displacement with aided dissolution and dilution.

[0025] The characteristic parameters such as density, viscosity, and flow velocity of each unit obtained from the simulation are exported and visualized in Tecplot software. - Simulation results of synergistic dissolution and viscosity reduction, and oil and gas distribution at different dynamic stages. (Example:) Figure 5 and Figure 6 As shown, the simulation uses two gas ratios, one of which is 80%. and 20% Another is 80%. and 20% .

[0026] contrast Dominance and As can be seen from the dominant two gas systems, such as Figure 7 As shown, Under the dominant operating conditions, the gas phase mainly advances along the dominant channels with high connectivity, while the oil phase is still relatively well preserved in the distal end, side edges, and weakly connected regions. The displacement process is mainly driven by pressure difference and channelization. Under the dominant operating conditions, except for free In addition to advancing along the main channel, The dissolved zone is more widely distributed within the pore network, indicating that its strong dissolving ability can enhance the oil-gas contact range and promote the reuse of remaining oil through crude oil expansion, viscosity reduction, and weakened interfacial resistance. Therefore, under the same pore structure conditions, The dominant system is relatively The dominant system has a stronger non-main channel spillover capability and residual oil utilization potential.

[0027] contrast Dominance and As can be seen from the two dominant gas systems, Under the dominant operating conditions, the gas phase mainly advances along the dominant channels with high connectivity, while the oil phase is still relatively well preserved in the distal end, side edges, and weakly connected regions. The displacement process is mainly driven by pressure difference and channelization. Under the dominant operating conditions, except for free In addition to advancing along the main channel, The dissolved zone is more widely distributed within the pore network, indicating that its strong dissolving ability can enhance the oil-gas contact range and promote the reuse of remaining oil through crude oil expansion, viscosity reduction, and weakened interfacial resistance. Therefore, under the same pore structure conditions, The dominant system is relatively The dominant system has a stronger non-main channel spillover capability and residual oil utilization potential.

[0028] 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 simulation method for synergistic oil displacement by CO2 and CH4 based on LBM, characterized in that, Includes the following steps: S1. Digital core construction: The core is subjected to three-dimensional imaging to obtain grayscale images. The pores and solid skeleton are divided by grayscale differences, discretized into NX×NY×NZ regular voxel meshes, and a structure identifier array is generated as the geometric boundary conditions for flow simulation. S2. Establish a lattice Boltzmann model, and use the three-dimensional D3Q19MRT-LBM method to construct an oil-gas two-phase flow model. Based on the voxel grid, divide the flow region and solid boundary, and solve the flow distribution of the oil phase and gas phase. S3. Coupled dissolution and synergistic viscosity reduction mechanism, introduced based on the oil-gas two-phase flow model. and The component convection-diffusion transport model is based on the oil phase adjacent unit. and The local concentration was used to determine the dissolution effect, and a dissolution criterion was established. The equivalent viscosity of the oil phase was dynamically corrected based on the gas concentration to achieve synergistic viscosity reduction and displacement characterization. S4. Simulation results analysis: Export fluid density, viscosity, and velocity field data, perform visualization processing, and obtain data at the pore scale. and Dynamic distribution results of synergistic displacement.

2. The method for synergistic oil displacement simulation of CO2 and CH4 based on LBM according to claim 1, characterized in that, In the structural identifier array, identifier value 0 represents the pore flow region, and identifier value 1 represents the solid skeleton region. The voxel grid and the structure identifier array correspond one-to-one in spatial location.

3. The method for synergistic oil displacement simulation of CO2 and CH4 based on LBM according to claim 1, characterized in that, The construction of the oil-gas two-phase flow model using the three-dimensional D3Q19 MRT-LBM method includes: employing a three-dimensional D3Q19 discrete velocity model, using the multi-relaxation time MRT-LBM scheme, and transforming the matrix... With diagonal relaxation matrix Perform fluid evolution calculations and determine the relaxation time based on the fluid's kinematic viscosity; The three-dimensional D3Q19 discrete velocity model contains 19 ion discrete velocity directions. , The discrete velocity set used to characterize the migration direction and velocity of fluid particles in three-dimensional space is as follows: ； In the formula, For the first The velocity vector corresponding to each discrete velocity direction The direction of ion movement; Employing the multi-relaxation time MRT-LBM scheme, through the transformation matrix With diagonal relaxation matrix The fluid evolution calculation is performed, and the relaxation time is determined based on the fluid's kinematic viscosity using the following formula: ； In the formula, for Time, location The first The density distribution function in each discrete velocity direction, i.e., the distribution state after migration. for Time, location The first The density distribution function along each discrete velocity direction, i.e., the distribution state before migration. For the first A velocity vector in a discrete velocity direction For time step, Let be the transformation matrix. Transformation matrix The inverse matrix, It is a diagonal relaxation matrix. For particles in space and time along The equilibrium density distribution function in the direction.

4. The method for synergistic oil displacement simulation of CO2 and CH4 based on LBM according to claim 3, characterized in that, The transformation matrix for: 。 5. The LBM-based CO2 and CH4 synergistic oil displacement simulation method according to claim 3, characterized in that, The diagonal relaxation matrix for: ； In the formula, The relaxation factor corresponding to the mass conservation moment is set to 1. , , , This is a free parameter, with a value of 1.

1. and The relaxation factor for the beam-related moments is set to 1. , Relaxation time; The relaxation time With fluid kinematic viscosity The relationship is: ； In the formula For the speed of sound in a lattice, the value is... .

6. The method for synergistic oil displacement simulation of CO2 and CH4 based on LBM according to claim 1, characterized in that, The introduction based on the oil-gas two-phase flow model and Component convection-diffusion transport models include: Based on the oil-gas two-phase flow model, a description of gas phase component transport is introduced to... and Migration, diffusion and at the pore scale and The enrichment process near the oil-gas interface was characterized; the macroscopic transport behavior of gas components in porous media was described by the convection-diffusion equation. ； In the formula, This represents the rate of change of the concentration of the gaseous component over time. In the middle of the gas phase or The equivalent concentration, The local velocity field is calculated using the LBM-MRT two-phase flow model. denoted as the diffusion coefficient of the gaseous component in the gas phase.

7. The method for synergistic oil displacement simulation of CO2 and CH4 based on LBM according to claim 1, characterized in that, The oil phase adjacent unit and Local concentration is used to determine dissolution effects, and dissolution criteria are established, including those obtained through convection-diffusion equations. and Concentration distribution within the pore space and and With the evolution of time, and Concentration distribution within the pore space and and Using the laws of evolution over time as a basis for judgment, establish and Criteria for the dissolving effect on the fluidity of the oil phase; The criterion for dissolution is: Local – Oil relative threshold As Reference concentration threshold, when At that time, the judgment It dissolves into the oil phase and affects the viscosity of the oil phase; The reference concentration threshold is determined by the grid points at the oil-gas interface. The determination is based on the local relative content of the oil phase. When the local relative content of the oil phase is >0.4, it is considered... It plays an auxiliary role in dissolving and diluting, and The improvement in oil phase viscosity is less than ; When both types of dissolution conditions are met simultaneously, synergistic viscosity reduction is triggered, dynamically adjusting the equivalent viscosity of the oil phase to complete the process. Dominant dissolution and viscosity reduction Simulation of synergistic oil displacement with aided dissolution and dilution.

8. The method for synergistic oil displacement simulation of CO2 and CH4 based on LBM according to claim 1, characterized in that, The equivalent viscosity of the oil phase The equivalent viscosity of the oil phase varies with the local gas component concentration. for: ； In the formula, The initial viscosity of crude oil before gas dissolution occurs. and They are respectively and The solubility viscosity reduction coefficient, and satisfies , and Each is located in an adjacent unit of the oil phase. and The equivalent concentration.

9. The LBM-based CO2 and CH4 synergistic oil displacement simulation method according to claim 1, characterized in that, S4 specifically includes: fluid data obtained based on S3 and... and Concentration distribution and evolution patterns, establishing - Criteria for dissolution and its effect on oil phase fluidity; based on the oil phase adjacent units Concentration and Concentration, determination The dominant dissolution and viscosity reduction effect and The auxiliary dissolution and dilution effects are used to distinguish the functional states of each gas component in different regions; and the characteristic parameters of each unit obtained from the simulation are exported and visualized in Tecplot software. - Simulation results of synergistic dissolution and viscosity reduction, and oil and gas distribution at different dynamic stages.

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