A shale nanochannel shale gas transport behavior prediction method

Abstract

The application discloses a shale nanochannel shale gas transport behavior prediction method, which comprises the following steps: establishing a shale nanochannel model, including a group of relatively arranged upper rock wall and lower rock wall, and the wall can be set as a rough surface; injecting gas into the nanochannel model and maintaining a stable gas pressure value; setting the atomic interlayer parameter in the nanochannel model, giving the gas molecule a gas pressure gradient force, establishing a gas transport process in the nanochannel model, and obtaining the position information and thermodynamic information of the gas molecules in the gas transport process; during the gas transport process in the nanochannel model, the motion trajectory of the gas molecules is image processed to establish a velocity-density distribution diagram of the gas molecules; the gas molecules are relaxed to reach an equilibrium state in the nanochannel model, and the viscosity of the gas in the nanochannel model is obtained; the transport velocity distribution of the gas in the nanochannel model is obtained, and the transport behavior prediction of the gas in the nanochannel model is completed.

Description

Technical Field

[0001] This invention relates to the field of shale gas transportation, and in particular to a method for predicting shale gas transportation behavior within shale nanochannels. Background Technology

[0002] Compared to energy sources such as coal, oil, and nuclear power, shale gas is characterized by abundant reserves and environmental friendliness. For example, it releases less sulfur oxides, hydrocarbons, and carbon monoxide during combustion, thus gaining widespread application. Statistics show that global shale gas production currently accounts for approximately 50% of total natural gas resources, and it is predicted that shale gas production will continue to increase over the next two decades.

[0003] Shale gas is commonly found in the pores of shale reservoirs or confined within mineral particles or organic matter with significantly low permeability, leading to complex shale gas transport behavior and low recovery efficiency. Since nanochannels play a dominant role in shale gas reservoirs, the transport and absorption behavior of shale gas within nanochannels has become a research hotspot. However, unlike smooth channels, nanochannels are small and complex in size, and surface roughness and complex connectivity are prevalent. Therefore, although scholars have conducted extensive research on the absorption and transport behavior of shale gas reservoirs, the transport behavior of gas in nanochannels with fractal roughness remains unclear, and the influence of surface roughness is not well understood. Accurately predicting shale gas migration behavior remains challenging. Given the significant complexity and randomness exhibited by the surface roughness of shale nanochannels, which can be accurately described using fractal analysis, using simulation methods to predict the transport characteristics of shale gas within nanochannels will help understand the migration patterns of shale gas in roughened nanochannels, thereby guiding practical applications to improve shale gas recovery rates. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a method for predicting shale gas transport behavior within shale nanochannels.

[0005] The technical solution of this invention is as follows:

[0006] A method for predicting shale gas transport behavior within shale nanochannels includes the following steps:

[0007] S1, establish a nanochannel model of shale, the nanochannel model includes at least a set of oppositely arranged upper rock walls and lower rock walls, and at least one surface of the oppositely arranged upper rock walls and / or lower rock walls may be set as a rough surface;

[0008] S2, gas is injected into the nanochannel model, and a stable gas pressure value is set inside the nanochannel model;

[0009] S3, set periodic boundary conditions; set the atomic mass of gas molecules and rock wall elements; set the interaction between rock wall atoms in the nanochannel model; set the interaction between rock wall atoms and gas molecules in the nanochannel model; set the interaction between gas molecules; apply a pressure gradient force to the gas molecules, establish the gas transport process in the nanochannel model, and obtain the position information and thermodynamic information of the gas molecules during the gas transport process;

[0010] S4, during the gas transport process within the nanochannel model, the motion trajectory of the gas molecules is imaged to establish a velocity-density distribution map of the gas molecules; the gas molecules are relaxed to reach an equilibrium state within the nanochannel model to obtain the viscosity of the gas within the nanochannel model; the gas transport velocity distribution within the nanochannel model is calculated to complete the prediction of the gas transport behavior within the nanochannel model.

[0011] Furthermore, a nanochannel model of shale is established in the Lammps software; the nanochannel model is at least set as a rough surface on the upper rock wall and / or the lower rock wall, and set as a surface with random roughness, to establish nanochannels with random roughness.

[0012] Furthermore, establishing the nanochannel model includes the following steps:

[0013] S1. In Materials Studio software, create an initial model of a nanochannel. This initial model includes a set of opposing rock walls: an upper rock wall and a lower rock wall. The rock walls are made of silicon atoms. The length of the initial nanochannel model is... Width is Height is

[0014] S2. Export the initial nanochannel model as a PDB file in Materials Studio software; and read and modify the initial nanochannel model using VMD software, then export it as a nanochannel initial model data.txt file.

[0015] S3. Using MATLAB software, read the initial model data.txt file of the nanochannel and program the WM fractal function of the initial model of the nanochannel to generate random roughness planes on the upper rock wall and / or lower rock wall. Export the nanochannel model data.txt file and use Lammps software to read it and reduce the atoms on the upper rock wall and / or lower rock wall of the nanochannel model to generate nanochannels with random roughness.

[0016] Furthermore, when programming the WM fractal function, the fractal parameter D is preferably set to 2.5, and the fractal roughness parameter G is preferably set to 1.0; the expression of the WM fractal function is:

[0017]

[0018] In the formula:

[0019] x, y, z are coordinates, G is the fractal roughness parameter, D is the fractal parameter, γ is the frequency parameter, M is the number of surface overlaps, m is the natural sequence number, n is the natural sequence number, L MAX Φ is the sample length. m,n It is a random number for each n and m, where n max For frequency coefficients.

[0020] Furthermore, the boundary conditions in the x, y, and z coordinate directions are set as periodic boundary conditions.

[0021] Furthermore, the interatomic interactions on the rock wall are described using the TERSOFF potential function, expressed as follows:

[0022]

[0023] V ij =f C (r ij )[f R (r ij )+b ij f A (r ij )]

[0024] In the formula,

[0025] E is the total energy of the system, V ij f is the bonding energy between atoms. A and f R These are the attracting and repulsive terms of the potential, f, respectively. C For smooth cutoff function, r ij b represents the distance between atoms. ij Let be the attraction potential function.

[0026] Furthermore, the interaction between atoms and gas molecules on the rock wall, as well as the interaction between gas molecules themselves, are characterized by the LJ potential function, expressed as follows:

[0027] E=4ε[(λ / r) 12 -(λ / r) 6 ]

[0028] In the formula,

[0029] E is the LJ potential energy, ε is the potential well depth, reflecting the strength of the mutual attraction between two atoms, λ is the equilibrium distance between atoms, and r is the distance between atoms.

[0030] Furthermore, relaxation is performed using an NVT ensemble, followed by an equilibration process to bring the system to equilibrium. A pressure gradient force is applied to the gas molecules, causing the gas entering the nanochannel model to be transported under the action of the pressure gradient force. During the gas transport process, the positional and thermodynamic information of the gas molecules is stored in the dump and log files.

[0031] Furthermore, the dump file is imported into OVITO software, and the gas transport process in the nanochannel model is image-processed using OVITO software to obtain the motion trajectory of gas molecules, thereby observing and analyzing the transport law of gas molecules, and further obtaining the velocity-density distribution map of gas molecules in the gas transport process.

[0032] Furthermore, based on the equilibrium process stage, the pressure fluctuation of the gas during the transport process is calculated, and the viscosity of the gas in the nanochannel model is calculated using the Green-Kubo formula, which is expressed as follows:

[0033]

[0034]

[0035] In the formula,

[0036] η is the gas viscosity, V is the corresponding gas volume, and k B Where P is Boltzmann's constant, T is ambient temperature, t is time parameter, and P is time parameter. αβ It is the symmetric component of the stress tensor σ, σ αβ For stress components, σ βα For stress components, δ αβ For Kronecker parameters, σ yy Let y be the stress component, and y be the parameter corresponding to the component.

[0037] Furthermore, the expression for the gas transport velocity distribution in the nanochannel model is as follows:

[0038] v = v ns +v s

[0039] In the formula,

[0040] ν ns ν is the gas flow velocity. s Let be the sliding velocity of the gas on the boundary wall.

[0041] Furthermore, the gas flow velocity ν between the upper and lower rock walls is described based on the theory of incompressible fluid continuum. ns The equation is:

[0042]

[0043] In the formula,

[0044] v ns Here, H represents the gas velocity and the gas velocity at the channel boundary, H is the channel height, and η is the gas viscosity. denoted by , z represents the pressure gradient, and z represents the location of the gas.

[0045] Furthermore, the sliding velocity of the gas on the boundary wall of the nanochannel model is described based on the Maxwell sliding model, and the equation is as follows:

[0046]

[0047] In the formula,

[0048] p and q are the slip coefficients, set to 1 and 0.5 respectively; Kn is the Knudsen number; L2 is the width of the initial nanochannel model; η is the gas viscosity; and ∂P / ∂t is the slip coefficient. x This represents the pressure gradient.

[0049] The beneficial effects of this invention are as follows:

[0050] The prediction method described in this invention establishes a model of shale gas transport in nanochannels using molecular dynamics simulation software. This model simulates the transport characteristics of shale gas in roughened nanochannels, performing simulation calculations on gas transport behavior at the microscale. By utilizing molecular dynamics simulation, various transport characteristics of shale gas are obtained, providing necessary evaluation parameters for the exploration and development of shale gas in rock nanochannels. The transport characteristics of shale gas are verified and analyzed using theoretical calculations. Therefore, this invention provides a reliable method for predicting shale gas transport behavior within nanochannels at the microscale, offering a basis and technical guidance for practical applications such as shale gas extraction, and has broad application prospects. Attached Figure Description

[0051] Figure 1 Schematic diagram of a nanochannel model;

[0052] Figure 2 A snapshot of different motion patterns of gas molecules on a rock wall in a nanochannel model;

[0053] Figure 3 : A schematic diagram of the gas molecule transport velocity distribution on the cross section of a nanochannel in a nanochannel model;

[0054] Figure 4Schematic diagram of theoretical calculation results and simulation results. Detailed Implementation

[0055] The contents of this invention will be described in detail below with reference to the accompanying drawings.

[0056] As attached Figure 1 To be continued Figure 4 As shown, this invention describes a method for predicting shale gas transport behavior within shale nanochannels, comprising the following steps.

[0057] S1. Create a nanochannel model of shale in the Lammps software.

[0058] The length of the nanochannel model is preferably set to Width is preferably set to The preferred height setting is

[0059] The rock walls of the nanochannel model include at least a set of opposing upper and lower walls. To better simulate actual shale nanochannels, the nanochannel model should be configured as a nanochannel with random roughness, specifically: at least one surface on the inner side of the opposing walls is a rough surface; or, at least the upper and / or lower walls are rough surfaces.

[0060] Specifically, it includes the following sub-steps:

[0061] S11. Create an initial model of the nanochannel in Materials Studio software and assign the following characteristics to the initial model of the nanochannel:

[0062] a. The initial model of the nanochannel is set with a set of opposing rock walls: an upper wall and a lower wall;

[0063] b. Since silicon is the most abundant element in rocks, the material used for the rock face is silicon atoms;

[0064] c. Set the initial model length L1 of the nanochannel to... Width L2 is Height H is

[0065] S12, Export the initial nanochannel model as a PDB file in Materials Studio software;

[0066] The PDB file was read using the visualization software VMD, and the initial model of the nanochannel was modified to avoid atomic misalignment.

[0067] The modified initial model of the nanochannel is exported as a data.txt file.

[0068] S13. Using MATLAB software, read the initial model data.txt file of the nanochannel and programmatically establish the WM fractal function of the initial model of the nanochannel to generate random roughness planes on the upper and / or lower walls of the initial model of the nanochannel. Export the data.txt file of the nanochannel model that can be read by the Lammps software.

[0069] When programming to establish the WM fractal function, the fractal parameter D is preferably set to 2.5, and the fractal roughness parameter G is preferably set to 1.0.

[0070] The full name of the WM fractal function is the Weierstrass-Mandelbrot fractal function, and its expression is:

[0071]

[0072] In the formula:

[0073] x, y, z are coordinates

[0074] G is the fractal roughness parameter.

[0075] D is the fractal parameter.

[0076] γ is a frequency parameter.

[0077] M represents the number of times the surfaces overlap.

[0078] m is the number of natural sequences.

[0079] n is the number of natural sequences.

[0080] L MAX For sample length,

[0081] Φ m,n It is a random number for each n and m, where n max For frequency coefficients.

[0082] S14. The nanochannel model data.txt file is read using the Lammps software, and silicon atoms on the upper and / or lower walls of the nanochannel model are reduced according to the generated WM function surface to generate nanochannels with random roughness. The fractal parameter range can be selected from 2 to 3.

[0083] S2, in the Lammps software, gas molecules, such as methane molecules, are introduced into the nanochannel model using the giant canonical Monte Carlo method, and the gas pressure inside the nanochannel model is brought to and stabilized at 20 MPa.

[0084] S3. Write an .in file in the Lammps software to establish a simulation process of shale gas transport within nanochannels.

[0085] Specifically, it includes the following sub-steps:

[0086] S31, Set the boundary conditions in the x, y, z directions as periodic boundary conditions;

[0087] S32, set the atomic mass of gas molecules and silicon, and preferably use a coarse-grained model for gas molecules;

[0088] S33, the interaction between silicon atoms on the rock wall surface is described by the TERSOFF potential function, and the expression is:

[0089]

[0090] V ij =f C (r ij )[f R (r ij )+b ij f A (r ij )]

[0091] In the formula,

[0092] E is the total energy of the system.

[0093] V ij The bonding energy between atoms.

[0094] f A and f R These are the attracting and repulsive terms of the potential, respectively.

[0095] f C It is a smooth cutoff function.

[0096] r ij The distance between atoms.

[0097] b ij Let be the attraction potential function;

[0098] S34, using the LJ potential function to characterize the interaction between silicon atoms and gas molecules on the rock wall, as well as the interaction between gas molecules, is expressed as follows:

[0099] E=4ε[(λ / r) 12 -(λ / r) 6 ]

[0100] In the formula,

[0101] E represents the potential energy of LJ.

[0102] ε represents the depth of the potential well, reflecting the strength of the attraction between the two atoms.

[0103] λ is the equilibrium distance between atoms.

[0104] r represents the distance between atoms.

[0105] S35, use NVT ensemble for relaxation, then perform balancing process to bring the model system to equilibrium;

[0106] Applying a pressure gradient force to gas molecules allows the gas entering the nanochannel model to be transported under the action of the pressure gradient force.

[0107] During gas transport, the positional and thermodynamic information of gas molecules is automatically saved in dump and log files, respectively.

[0108] S4. Import the dump file into OVITO software. Use OVITO software to perform image processing on the gas transport process in the nanochannel model, obtain the motion trajectory of gas molecules, observe and analyze the transport law of gas molecules, and further obtain the velocity-density distribution map of gas molecules in the gas transport process.

[0109] S5, in step S35, the pressure fluctuation of the gas during the transport process is calculated based on the equilibrium stage, and the viscosity of the gas in the nanochannel model is calculated using the Green-Kubo formula; the gas flux in the nanochannel model can be calculated using the velocity-density distribution map in step S4, and the gas transport efficiency can be evaluated.

[0110] The expression for the Green-Kubo formula is:

[0111]

[0112]

[0113] In the formula,

[0114] η is the gas viscosity.

[0115] V represents the corresponding gas volume.

[0116] k B Boltzmann's constant,

[0117] T represents the ambient temperature.

[0118] t is a time parameter.

[0119] P αβ It is the symmetric component of the stress tensor σ.

[0120] σ αβ For stress components,

[0121] σ βα For stress components,

[0122] δ αβ For Kronecker parameters,

[0123] σ yy For stress components,

[0124] y is the parameter corresponding to the component.

[0125] S6, based on the theory of incompressible fluid continuum and Maxwell's sliding model, calculates the gas transport velocity distribution in the nanochannel model and compares it with the results obtained from the simulation of a straight pipe to verify the rationality of the model and complete the prediction of gas transport behavior in the nanochannel model.

[0126] The expression for the gas transport velocity distribution in the nanochannel model is as follows:

[0127] v = v ns +v s

[0128] In the formula,

[0129] ν ns For gas flow velocity,

[0130] ν s Let be the sliding velocity of the gas on the boundary wall.

[0131] in,

[0132] The gas flow velocity ν through two parallel walls is described based on the theory of incompressible fluid continuum. ns The equation is:

[0133]

[0134] In the formula,

[0135] v ns Gas velocity,

[0136] H is the channel height.

[0137] η is the gas viscosity.

[0138] Represents the pressure gradient.

[0139] z represents the location of the gas.

[0140] The sliding velocity of gas at the boundary wall is described by the Maxwell sliding model, and the equation is:

[0141]

[0142] In the formula,

[0143] p and q are the slip coefficients, set to 1 and 0.5 respectively.

[0144] Kn is the Knudsen number.

[0145] L2 is the width of the initial nanochannel model.

[0146] η is the gas viscosity.

[0147] This represents the pressure gradient.

[0148] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for predicting shale gas transport behavior within shale nanochannels, characterized in that, Includes the following steps: S1, establish a nanochannel model of shale, the nanochannel model including at least a set of oppositely arranged upper rock walls and lower rock walls, at least one inner wall of the upper rock wall and / or lower rock wall is set as a rough surface; S2, gas is injected into the nanochannel model and a stable gas pressure value is maintained; S3, set periodic boundary conditions; set the atomic mass of gas molecules and rock wall elements; set the interaction between rock wall atoms in the nanochannel model; set the interaction between rock wall atoms and gas molecules in the nanochannel model; set the interaction between gas molecules; apply a pressure gradient force to the gas molecules, establish the gas transport process in the nanochannel model, and obtain the position information and thermodynamic information of the gas molecules during the gas transport process; S4, during the gas transport process within the nanochannel model, the motion trajectory of the gas molecules is imaged to establish a velocity-density distribution map of the gas molecules; the gas molecules are relaxed to reach an equilibrium state within the nanochannel model to obtain the viscosity of the gas within the nanochannel model; the gas transport velocity distribution within the nanochannel model is calculated to complete the prediction of the gas transport behavior within the nanochannel model.

2. The method for predicting shale gas transport behavior within shale nanochannels according to claim 1, characterized in that, A nanochannel model of shale is established in the Lammps software; the nanochannel model is at least set as a rough surface on the upper rock wall and / or the lower rock wall, and set as a surface with random roughness, to establish nanochannels with random roughness.

3. The method for predicting shale gas transport behavior within shale nanochannels according to claim 2, characterized in that, Establishing the nanochannel model includes the following steps: S1. In Materials Studio software, an initial model of a nanochannel is created. The initial model of the nanochannel has a set of opposing rock walls, namely an upper rock wall and a lower rock wall. The material of the rock walls is silicon atoms. The initial model of the nanochannel has a length of 100 Å, a width of 100 Å, and a height of 60 Å. S2, Export the initial nanochannel model as a PDB file in Materials Studio software; and read and modify the initial nanochannel model using VMD software, and export it as a nanochannel initial model data.txt file; S3. Using MATLAB software, read the initial model data.txt file of the nanochannel and program the WM fractal function of the initial model of the nanochannel to generate random roughness planes on the upper rock wall and / or lower rock wall. Export the nanochannel model data.txt file and use Lammps software to read it and reduce the atoms on the upper rock wall and / or lower rock wall of the nanochannel model to generate nanochannels with random roughness.

4. The method for predicting shale gas transport behavior within shale nanochannels according to claim 3, characterized in that, When programming the WM fractal function, the fractal parameter D is set to 2.5, and the fractal roughness parameter G is set to 1.0; the expression of the WM fractal function is: In the formula: x, y, z are coordinates G is the fractal roughness parameter. D is the fractal parameter. For frequency parameters, M represents the number of times the surfaces overlap. m is the number of natural sequences. n is the number of natural sequences. L MAX For sample length, It is a random number for each n and m, where n max For frequency coefficients.

5. The method for predicting shale gas transport behavior within shale nanochannels according to claim 2, characterized in that, Set the boundary conditions in the x, y, and z directions to periodic boundary conditions.

6. The method for predicting shale gas transport behavior within shale nanochannels according to claim 2, characterized in that, The interaction between atoms on the rock wall is described by the TERSOFF potential function, expressed as follows: ， ， In the formula, E is the total energy of the system. V ij The bonding energy between atoms. f A and f R These are the attracting and repulsive terms of the potential, respectively. f C It is a smooth cutoff function. r ij The distance between atoms. b ij Let be the attraction potential function.

7. The method for predicting shale gas transport behavior within shale nanochannels according to claim 2, characterized in that, The LJ potential function characterizes the interactions between atoms and gas molecules on the rock wall, as well as the interactions between gas molecules themselves. The expression is: ， In the formula, E represents the potential energy of LJ. ε represents the depth of the potential well, reflecting the strength of the attraction between the two atoms. λ is the equilibrium distance between atoms. r represents the distance between atoms.

8. The method for predicting shale gas transport behavior within shale nanochannels according to claim 2, characterized in that, The system is relaxed using an NVT ensemble, followed by an equilibration process to bring it to equilibrium. A pressure gradient force is applied to the gas molecules to transport the gas entering the nanochannel model under the action of the pressure gradient force. During gas transport, the positional and thermodynamic information of gas molecules is stored in dump and log files.

9. The method for predicting shale gas transport behavior within shale nanochannels according to claim 8, characterized in that, The dump file is imported into OVITO software. The gas transport process in the nanochannel model is image-processed using OVITO software to obtain the motion trajectory of gas molecules, thereby observing and analyzing the transport law of gas molecules, and further obtaining the velocity-density distribution map of gas molecules in the gas transport process.

10. The method for predicting shale gas transport behavior within shale nanochannels according to claim 8, characterized in that, Based on the equilibrium process stage, the pressure fluctuation of the gas during the transport process is calculated, and the viscosity of the gas in the nanochannel model is calculated using the Green-Kubo formula, which is expressed as follows: ， ， In the formula, η is the gas viscosity. V represents the corresponding gas volume. k B Boltzmann's constant, T represents the ambient temperature. t is a time parameter. It is the symmetric component of the stress tensor σ. σ αβ For stress components, σ βα For stress components, δ αβ For Kronecker parameters, σ yy For stress components, y is the parameter corresponding to the component.

11. The method for predicting shale gas transport behavior within shale nanochannels according to claim 2, characterized in that, The expression for the gas transport velocity distribution in the nanochannel model is as follows: ， In the formula, ν ns For gas flow velocity, ν s Let be the sliding velocity of the gas on the boundary wall.

12. The method for predicting shale gas transport behavior within shale nanochannels according to claim 11, characterized in that, The gas flow velocity ν between the upper and lower rock walls is described based on the theory of incompressible fluid continuum. ns The equation is: ， In the formula, The gas velocity and the gas velocity at the channel boundary, H is the channel height. η is the gas viscosity. Represents the pressure gradient. z represents the location of the gas.

13. The method for predicting shale gas transport behavior within shale nanochannels according to claim 11, characterized in that, The sliding velocity of gas on the boundary wall of the nanochannel model is described by the Maxwell sliding model, and the equation is: ， In the formula, p and q are the slip coefficients, set to 1 and 0.5 respectively. Kn is the Knudsen number. L2 is the width of the initial nanochannel model. η is the gas viscosity. This represents the pressure gradient.

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