Method for evaluating cross-scale multi-component deep shale gas mobility

Abstract

The application discloses a kind of cross-scale multi-component deep shale gas mobility evaluation method, it is related to oil and gas resource evaluation technical field.The application utilizes molecular simulation software to construct multi-component deep shale gas pore occurrence model, utilizes its simulation to obtain the density variation curve of each component of shale gas fluid under different pressure conditions, divides adsorption zone and free zone, then determines the shale gas density calculation function of adsorption zone and free zone, establishes shale gas mobility evaluation formula under molecular scale and is applied to pore scale, in combination with the pore network model extracted from the core of deep shale reservoir according to field acquisition, obtains the size distribution of its throat and pore, respectively calculates adsorption zone volume and free zone volume, utilizes shale gas mobility evaluation formula to evaluate the shale gas operation of deep shale reservoir, realizes accurate evaluation to multi-component deep shale gas mobility on molecular level, provides technical support for the efficient formulation of deep shale gas development plan.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas resource evaluation, and particularly relates to a method for evaluating cross-scale multi-component deep shale gas mobility. BACKGROUND

[0002] With the advancement of technology and the growing demand for clean energy, shale gas, as a clean and low-carbon energy source, its development helps to alleviate the shortage of conventional natural gas resources, optimize energy structure, reduce greenhouse gas emissions, and promote green energy transformation. However, the geological conditions of deep shale gas are complex, and the understanding of its occurrence mechanism and production situation is unclear, making it difficult to develop a reasonable and efficient development plan, resulting in low production efficiency and poor economic performance.

[0003] The occurrence of deep shale gas is controlled by multiple factors such as organic matter thermal maturity, reservoir properties and tectonic movement. Part of the shale gas is adsorbed by the wall surface to become adsorbed state, and its adsorption capacity is closely related to the abundance of reservoir organic matter, pore structure and the change of physical state caused by formation pressure. The adsorption capacity and characteristics of different components also have significant differences. Free gas fills in shale matrix pores, natural fractures and artificial fractures, and its density is controlled by pore size, temperature and pressure. Due to the characteristics of nanoscale pores, low permeability and rich organic matter in deep shale reservoirs, the proportion of adsorbed gas is usually significantly higher than that in conventional reservoirs.

[0004] Currently, the evaluation of shale gas mobility in deep shale nanoscale pores mainly has the following problems:

[0005] (1) There is a lack of understanding of the mobility of different components in shale gas in nanoscale pores, and there is no method that can directly evaluate the mobility of different components of shale gas according to the pore type and size of shale, and it is impossible to determine the difference in occurrence in nanoscale pores before and after shale gas exploitation.

[0006] (2) With the extension of shale gas development to higher pressure and more complex reservoir conditions, traditional experimental measurement methods can provide some reference data, but they have many limitations.

[0007] For example, adsorption and desorption experiments usually use crushed rock samples, which destroy the natural pore structure and fracture network in shale, and cannot completely reproduce the real occurrence state in the formation; isothermal adsorption experiments can only measure the excess adsorption amount, and cannot determine the specific volume of adsorbed and free phases, and cannot distinguish between components in shale gas. At the same time, the non-homogeneous characterization of conventional experimental measurement results is insufficient, and the distribution of shale organic matter is millimeter-scale heterogeneous, but the sample quality of conventional experiments is mostly tens of grams, which can only reflect the macroscopic average response and cannot distinguish local adsorption hotspots.

[0008] (3) Considering the difference between the occurrence characteristics of shale gas and the shallow reservoir, most laboratory equipment cannot restore the temperature and pressure conditions of deep shale, and cannot restore the real formation environment of deep shale gas, resulting in large deviation between the experimental results and the actual situation of the reservoir.

[0009] Therefore, it is urgent to propose a cross-scale multi-component deep shale gas mobility evaluation method based on molecular simulation method, to restore the adsorption and phase behavior of each component in the shale pore network, accurately characterize the occurrence characteristics of different components of deep shale gas, obtain the dynamic change rule of the content of each component of shale gas and the mobility of each component under different pores and different pressures, and realize accurate evaluation of the mobility of deep shale gas. SUMMARY

[0010] The present application aims to solve the above problems, and provides a cross-scale multi-component deep shale gas mobility evaluation method, which characterizes the occurrence characteristics of shale gas in different pores by constructing a multi-component deep shale gas pore occurrence model, uses the multi-component deep shale gas pore occurrence model to simulate the adsorption volume of different components of shale gas on different pore walls, determines the adsorbed gas density and free gas density, fits the shale gas density calculation function of the adsorption zone and the free zone, establishes a shale gas mobility evaluation formula at the molecular scale, and couples it to the pore network model extracted from the real shale core, fully considers the adsorption and phase behavior of each component of shale gas, and realizes accurate evaluation of the mobility of multi-component shale gas in deep shale reservoir.

[0011] The present application adopts the following technical solutions:

[0012] A cross-scale multi-component deep shale gas mobility evaluation method, comprising the following steps:

[0013] Step 1, a multi-component deep shale gas pore occurrence model composed of a deep shale nanometer pore molecular model and a shale gas fluid model is constructed by using a molecular simulation software, wherein the deep shale nanometer pore molecular model is provided with a pore wall model, including an organic pore wall and an inorganic mineral wall, and the shale gas fluid model is assigned to the pore wall model;

[0014] Step 2, set the force field information, preprocess the shale gas fluid model based on the energy minimization algorithm, make it stably assigned to the deep shale nanometer pore molecular model, set the simulation temperature, use the shale gas fluid model to simulate the density change curve of each component of shale gas fluid under different pressure conditions, and divide the adsorption zone and free zone of each component of shale gas fluid in the deep shale nanometer pore molecular model;

[0015] Step 3, according to the divided adsorption zone and free zone, the shale gas adsorption layer thickness at the organic pore wall surface is obtained, the free density and adsorption density of shale gas fluid and each component thereof at the organic pore wall surface under different pressures at the current temperature condition are determined respectively, the shale gas density calculation functions of the adsorption zone and the free zone are fitted, and a shale gas mobility evaluation formula at the molecular scale is established;

[0016] Step 4, the shale gas mobility evaluation formula is applied to the pore scale, and a pore network model is extracted from a deep shale reservoir core collected on site;

[0017] Step 5, the structure parameter analysis is performed on the pore network model, the size distribution of the throat and the pore is obtained, and the volumes of the adsorption zone and the free zone in the deep shale nanometer pore molecular model are calculated respectively in combination with the shale gas adsorption layer thickness at the organic pore wall surface;

[0018] Step 6, for the deep shale reservoir to be evaluated, an initial formation pressure is set, the shale gas mobility evaluation formula is used, the volumes of the adsorption zone and the free zone are combined, and the shale gas density calculation function is used to evaluate the production of shale gas in the deep shale reservoir to be evaluated after the formation pressure is reduced.

[0019] Preferably, in step 1, in the molecular simulation software, the kerogen molecular unit and the inorganic mineral unit are selected, the kerogen molecule and the inorganic mineral cell are obtained, the CVFF force field label and the charge information are added to the kerogen molecule, the kerogen molecular model is constructed, the inorganic pore wall surface is obtained by cutting the inorganic mineral cell, the CLAYFF force field label and the charge information are added to the inorganic mineral cell in combination with the atomic types at different positions in the inorganic mineral cell, and the inorganic mineral cell model is constructed;

[0020] According to the molar mass of the kerogen molecule and the preset organic pore wall surface size, a plurality of kerogen molecular models are randomly placed in the deep shale nanometer pore space, the deep shale nanometer pore space is compressed by setting a virtual wall, the organic pore wall surface is formed, the energy minimization is performed on the organic pore wall surface, the roughness of the organic pore wall surface is increased, and a wall rough organic pore wall surface model is obtained. The inorganic mineral cell model is replicated and expanded, the inorganic mineral wall surface model is constructed, and the CLAYFF force field parameters of the inorganic mineral wall surface model are set according to the CLAYFF force field label of the inorganic mineral cell model;

[0021] The organic pore wall surface model and the inorganic mineral wall surface model are imported into the Lammps molecular simulator to construct a deep shale nanometer pore molecular model;

[0022] The shale gas fluid model is obtained by adding OPLS-AA force field tags and charge information to each atom in the shale gas molecular model, and setting OPLS-AA force field parameters of the shale gas molecular model.

[0023] The multi-component deep shale gas pore occurrence model is constructed by combining the deep shale nanopore molecular model with the shale gas fluid model, and an atomic plate for controlling pressure is set, and the pore pressure in the multi-component deep shale gas pore occurrence model is controlled by using the atomic plate.

[0024] Preferably, the force on a unit atom on the atomic plate is:

[0025] ;

[0026] In the formula, is the force on a unit atom, and the unit is ; is the system pressure, and the unit is ; is the area of the atomic plate, and the unit is ; is the number of atoms in the atomic plate;

[0027] The interaction between atoms in the multi-component deep shale gas pore occurrence model is:

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] In the formula, is the Coulomb interaction energy; is the point charge; is the vacuum permittivity; , are both atomic numbers; is the charge amount of the atom ; is the charge amount of the atom ; is the distance between the atom and the atom ; is the van der Waals interaction energy; , are both fitting empirical coefficients; is the bond interaction energy; is an empirical coefficient for the bond interaction; is an angle interaction energy; is an empirical coefficient for the angle interaction; is a bond-stretching equilibrium distance; is an atom , an atom , an atom , and an angle between atoms is an equilibrium angle.

[0033] Preferably, in step 2, in the multi-component deep shale gas pore occurrence model, a cutoff radius is set, the PPPM algorithm is used to determine the Coulomb interaction force, the arithmetic mixing rule is used to set the interaction between different types of atoms, and the model boundary is set as a periodic boundary, the force on the atoms in the deep shale nanopore molecular model is set to zero, which is used to maintain the position stability of the deep shale nanopore molecular model;

[0034] The shale gas fluid model is preprocessed based on the energy minimization algorithm, the atomic positions are made so that there is no overlapping molecular structure in the shale gas fluid model, after obtaining the stable shale gas fluid initial configuration, the shale gas fluid model is placed in the NVT canonical ensemble, the Nosé-Hoover algorithm is used to control the temperature of the shale gas fluid model, the simulation step, simulation temperature and simulation pressure are set, and the multi-component deep shale gas pore occurrence model is used to simulate under the current pressure condition, to obtain the density variation curve of each component of the shale gas fluid in the deep shale nanopore molecular model at different positions, and determine the density variation curve of each component of the shale gas fluid at different positions in the pore space of the deep shale gas reservoir.

[0035] According to the density variation curve of each component of the shale gas fluid, the density variation smooth region is divided into a free zone, and the remaining region is divided into an adsorption zone, so as to determine the adsorption zone and the free zone of each component of the shale gas fluid in the deep shale nanopore molecular model.

[0036] Preferably, the PPPM algorithm determines the Coulomb interaction potential energy as follows:

[0037] ;

[0038] wherein,

[0039] ;

[0040] wherein, is the Coulomb interaction potential energy; is the short-range Coulomb interaction potential energy; is the long-range Coulomb interaction potential energy;

[0041] The long-range Coulomb interaction is obtained by solving using gridded charges:

[0042] ;

[0043] In the formula, This is the long-range Coulomb interaction potential energy function; This is the function for calculating the grid potential; For atoms Position coordinates; For grid points Position coordinates; For grid point numbers; It is an interpolation function;

[0044] The arithmetic mixing rule is set as follows:

[0045] ;

[0046] ;

[0047] In the formula, For atoms With atoms The potential well depth between them; Two atoms The potential well depth between them; Two atoms The potential well depth between them; For atoms With atoms The collision volume between them; Two atoms The collision volume between them; Two atoms The collision volume between them.

[0048] Preferably, in step 3, based on the Langmuir isotherm adsorption formula, the adsorption density of shale gas under the same temperature and different pressure conditions in the adsorption density dataset is fitted to obtain the shale gas density calculation function of the adsorption zone:

[0049] ;

[0050] In the formula, The density of shale gas in the adsorption zone under a specified pressure; The maximum adsorption density; The adsorption constant is denoted by . Formation pressure;

[0051] By fitting the free density data of shale gas under the same temperature and different pressure conditions in the free density dataset, the calculation function for shale gas density in the free zone is obtained as follows:

[0052] ;

[0053] In the formula, Shale gas density in the downstream zone at a specified pressure; , , All are fitting coefficients for the free region;

[0054] Based on the shale gas density calculation function of the adsorption and free zones, a formula for evaluating shale gas mobility at the molecular scale is established, yielding:

[0055] ;

[0056] in,

[0057] ;

[0058] ;

[0059] In the formula, The degree to which shale gas is usable; This represents the adsorbed mass of shale gas in the pores of deep shale. The free-state mass of shale gas per unit length; This represents the adsorbed mass of shale gas under initial formation pressure. This represents the free-state mass of shale gas under initial formation pressure. This represents the shale gas density in the adsorption zone under the current pore pressure. This represents the shale gas density in the downstream zone under the current pore pressure. The volume of the adsorption region; Let V be the volume of the free region.

[0060] Preferably, the pores inside the deep shale nanopore molecular model are set as cylindrical pipe pores, resulting in the following shale gas mobility evaluation formula for the multi-component deep shale gas pore occurrence model at the molecular scale:

[0061] ;

[0062] In the formula, Where is the pore radius; The thickness of the shale gas adsorption layer at the organic pore wall is given.

[0063] Preferably, in the step 4, the SEM scanning image of the deep shale reservoir core is obtained by scanning the deep shale reservoir core collected on site, the binary image is obtained after threshold segmentation of the SEM scanning image, and the three-dimensional digital core is reconstructed by using the Markov chain Monte Carlo method, and the pore network model is extracted based on the SNOW algorithm of the watershed method.

[0064] The binary assignment formula of the threshold segmentation is:

[0065] ;

[0066] In the formula, is the pixel point after binaryzation; is the gray value of the pixel point ; is the preset threshold value; is the horizontal coordinate of the pixel point in the SEM scanning image; is the vertical coordinate of the pixel point in the SEM scanning image.

[0067] Preferably, in the step 5, the total pore volume in the deep shale nanometer pore molecular model is:

[0068] ;

[0069] In the formula, is the total pore volume of the deep shale nanometer pore molecular model; is the pore radius corresponding to the pore ; is the throat radius corresponding to the throat ; is the throat length corresponding to the throat ; is the pore serial number; is the throat serial number; is the total number of pores; is the total number of throats;

[0070] The free zone volume in the deep shale nanometer pore molecular model is:

[0071] ;

[0072] In the formula, is the free zone volume; is the shale gas adsorption layer thickness at the organic pore wall surface;

[0073] The adsorption zone volume in the deep shale nanometer pore molecular model is:

[0074] ;

[0075] wherein, is the adsorption zone volume.

[0076] The present application has the following beneficial effects:

[0077] (1) The present application provides a cross-scale multi-component deep shale gas mobility evaluation method, which uses molecular simulation software to construct a multi-component deep shale gas pore occurrence model, uses the multi-component deep shale gas pore occurrence model to simulate the density distribution of deep shale gas fluid, divides the adsorption zone and the free zone, and calculates the adsorbed gas density and free gas density of each component of the shale gas fluid in the deep shale nanometer pore under different pressures, determines the shale gas density calculation function of the adsorption zone and the free zone, establishes a shale gas mobility evaluation formula for the deep shale reservoir at the molecular scale, and realizes accurate evaluation of the shale gas mobility in the adsorption zone and the free zone in the nanometer pore.

[0078] Meanwhile, the present application method is also based on the deep shale reservoir core scanning electron microscope image, obtains a binary image by threshold segmentation on the deep shale reservoir core scanning electron microscope image, cooperates with the reconstruction of a three-dimensional digital core by using the Markov chain Monte Carlo method, extracts a pore network model by the SNOW algorithm, obtains the structure parameters of the real deep shale reservoir, determines the adsorption zone volume and the free zone volume, and combines the adsorption zone volume, the free zone volume, the shale gas density of the adsorption zone and the shale gas density of the free zone to calculate the shale gas content of each component in the multi-component deep shale gas pore occurrence model under different pressures, sets an initial formation pressure, obtains the ideal production situation of the core scale deep shale gas during depletion, accurately determines the mobility of different components of the deep shale gas, provides technical support for efficient development of the deep shale gas development plan, and has good engineering application prospect.

[0079] (2) The present application provides a cross-scale multi-component deep shale gas mobility evaluation method, which can not only truly restore the temperature environment and pressure environment of the deep shale reservoir, but also can distinguish the occurrence of the shale gas in different mineral component pores in detail, cooperates with the molecular trajectory obtained by using the molecular simulation method to calculate the density distribution of the shale gas fluid in the deep shale reservoir, and accurately obtains the characteristic information of the shale gas adsorption layer. Meanwhile, the present application method also fully considers the influence of multi-component, adsorption and phase behavior on the deep shale gas mobility evaluation, realizes accurate evaluation of the multi-component deep shale gas mobility at the molecular level based on the density change characteristics of the adsorbed gas and the free gas.

[0080] (3) The application provides a cross-scale multi-component deep shale gas mobility evaluation method, which is combined with a real deep shale core extracted pore network model through cross-system parameter transmission, realizes evaluation on shale gas mobility in a deep shale reservoir, is favorable for helping oil and gas field development personnel to study mobility of different component shale gas according to characteristics of the deep shale reservoir, solves the problems of difficulty in determining different component production conditions and evaluating shale gas mining difficulty in different deep shale reservoirs in the current development process, has a wide application prospect, provides theoretical and method guidance for selecting a more targeted development strategy, and lays a foundation for improving deep shale gas development efficiency and recovery. BRIEF DESCRIPTION OF DRAWINGS

[0081] Figure 1 The flow chart of the cross-scale multi-component deep shale gas mobility evaluation method of the application.

[0082] Figure 2 The structure schematic diagram of the kerogen molecule.

[0083] Figure 3 The schematic diagram of the organic pore wall surface constructed based on the kerogen matrix.

[0084] Figure 4 The density distribution curve diagram of the single-component shale gas fluid simulated by the application.

[0085] Figure 5 The density distribution curve diagram of the multi-component shale gas fluid simulated by the application; in the diagram, (a) is the density distribution curve diagram of methane in the multi-component shale gas fluid, and (b) is the density distribution curve diagram of propane in the multi-component shale gas fluid.

[0086] Figure 6 The density fitting curve diagram of different component shale gas fluids; in the diagram, (a) is the density fitting curve diagram of the single-component shale gas fluid, and (b) is the density fitting curve diagram of the multi-component shale gas fluid.

[0087] Figure 7 The density fitting curve of propane in the corrected multi-component shale gas fluid.

[0088] Figure 8 The curve of the shale gas mobility ratio with pressure when the temperature is 393K.

[0089] Figure 9 The schematic diagram of the pore network model.

[0090] Figure 10 The throat diameter distribution diagram of the pore network model.

[0091] Figure 11 The throat length distribution diagram of the pore network model.

[0092] Figure 12 A pore diameter distribution graph for the pore network model.

[0093] Figure 13 A producing curve of shale gas fluid in a deep shale reservoir; in the figure, (a) is a producing curve of single-component shale gas fluid in a deep shale reservoir, and (b) is a producing curve of multi-component shale gas fluid in a deep shale reservoir. DETAILED DESCRIPTION

[0094] The specific embodiments of the present application are further described below with reference to the accompanying drawings: EMBODIMENT

[0095] The present application provides an evaluation method for cross-scale multi-component deep shale gas mobility, as shown in the figure, comprising the following steps: Figure 1

[0096] Step 1, based on molecular simulation software such as Materials Studio software and LAMMPS molecular simulator, a multi-component deep shale gas pore occurrence model is constructed, the multi-component deep shale gas pore occurrence model is composed of a deep shale nanometer pore molecular model and a shale gas fluid model, wherein the deep shale nanometer pore molecular model is provided with a pore wall model, including an organic pore wall established based on a CVFF force field and an inorganic mineral wall established based on a CLAYFF force field, the shale gas fluid model is established based on an OPLS force field and is assigned in the pore wall model.

[0097] Further, in the molecular simulation software, according to the reservoir characteristics of the deep shale reservoir to be evaluated, kerogen molecular units and inorganic mineral units are selected, the kerogen molecules and inorganic mineral unit cells are imported or drawn by using the Materials Studio software, after the kerogen molecules and inorganic mineral unit cells are obtained, the AC module is called to add the CVFF force field label and charge information to the kerogen molecules, the kerogen molecular model is constructed, the Bulid module is called to cut the inorganic mineral unit cell to obtain the inorganic pore wall, the CLAYFF force field label and charge information are added to the inorganic mineral unit cell in combination with the atom types at different positions in the inorganic mineral unit cell, the inorganic mineral unit cell model is constructed, and the kerogen molecular model and the inorganic mineral unit cell model are stored in a car format file, which is processed into a data format file by the msi2lmp software package and then exported, to obtain the kerogen molecular model file and the inorganic mineral unit cell model file.

[0098] ​The Moltemplate program is called to obtain the force field and charge information of the kerogen molecular model, the molar mass of the kerogen molecule, and the size and density of the preset organic pore wall surface are combined, the PACKMOL software package is called to randomly place multiple kerogen molecular models in the deep shale nanometer pore space, and after obtaining the pdb file of the kerogen matrix, the Moltemplate program is called to add corresponding CVFF force field tags and charge information to each atom in the deep shale nanometer pore molecular model, and exported as a data format recognizable by the LAMMPS molecular simulator.

[0099] According to the size of the preset organic pore wall surface, virtual walls are created in the x, y and z directions in the LAMMPS molecular simulator to compress the deep shale nanometer pore space and form the organic pore wall surface. During the compression process, the kerogen matrix becomes smooth under the compression of the smooth wall, so after removing the virtual wall, the organic pore wall surface needs to be energy minimized to increase the roughness of the organic pore wall surface, and the organic pore wall surface model with rough wall surface is obtained. The data format file of the inorganic mineral cell model is imported into the OVITO software for replication and cell expansion, and the inorganic mineral wall surface model is constructed and exported. According to the CLAYFF force field tag of the inorganic mineral cell model, the ClayFF force field parameter information of the inorganic mineral cell is copied to the data format file of the inorganic mineral wall surface model, and the CLAYFF force field parameters of the inorganic mineral wall surface model are set. Finally, the organic pore wall surface model and the inorganic mineral wall surface model are imported into the Lammps molecular simulator to construct the deep shale nanometer pore molecular model.

[0100] A plurality of component shale gas molecular information is obtained from the LigPargen website database to obtain the model coordinate file of the plurality of component shale gas molecules, determine the shale gas molecular model, call the PACKMOL software to obtain the pdb file of the single component shale gas fluid and the plurality of component shale gas fluid, call the Materials Studio software to add corresponding OPLS-AA force field tags and charge information to each atom in the shale gas molecular model, and convert it into a data format recognizable by the LAMMPS molecular simulator. Set the OPLS-AA force field parameters of the shale gas molecular model to complete the construction of the shale gas fluid model.

[0101] The deep shale nanometer pore molecular model and the shale gas fluid model are combined to obtain a multi-component deep shale gas pore occurrence model, and an atom plate for controlling pressure is set. In this embodiment, fixed atom walls are arranged in parallel on the left and right sides of the pore wall model, and movable atom walls are arranged on the upper and lower sides of the pore wall model. The fixed atom walls and the movable atom walls are used to control the pore pressure inside the multi-component deep shale gas pore occurrence model.

[0102] According to the pore pressure of the reservoir and the area of the atomic plate, the force required by the atomic plate is calculated, and then divided by the number of atoms on the atomic plate to obtain the force size required by a single atom, which is used to control the shale pore pressure.

[0103] Specifically, the force of a unit atom on the atomic plate is:

[0104] ;

[0105] In the formula, is the force of a unit atom, and the unit is ; is the system pressure, and the unit is ; is the area of the atomic plate, and the unit is ; is the number of atoms in the atomic plate.

[0106] The influence of the force field and charge information in the multi-component deep shale gas pore occurrence model on the interaction between atoms is:

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] In the formula, is the Coulomb interaction energy, and the unit is ; is a point charge; is the vacuum permittivity; , are both atomic numbers; is the charge amount of atom ; is the charge amount of atom ; is the distance between atom and atom , and the unit is ; is the van der Waals interaction energy, which is used to represent the balance between intermolecular attraction and repulsion, and the unit is ; , are both fitting empirical coefficients, which are used to represent the interaction between atom and atom , and the unit of fitting empirical coefficient is , and the unit of fitting empirical coefficient The unit of ; The unit of is the bond interaction energy, the unit of ; The unit of is the bond interaction empirical coefficient; The unit of ; The unit of is the angle interaction empirical coefficient; The unit of ; The unit of is the angle between atoms , atoms , atoms ; The unit of .

[0112] Step 2, set the force field information to describe the interaction rules between atoms, preprocess the shale gas fluid model based on the energy minimization algorithm, and obtain a relatively stable initial configuration. At this time, the shale gas fluid model is stably assigned to the deep shale nanometer pore molecular model. Set the simulation temperature, change the stress of the atomic plate, apply various pressures to the shale gas fluid model, simulate the density change curve of each component in the shale gas fluid model under different pressure conditions, and divide the adsorption zone and free zone of each component of the shale gas fluid in the deep shale nanometer pore molecular model.

[0113] Further, in the multi-component deep shale gas pore occurrence model, the action of short-range van der Waals force and Coulomb force is considered, the cutoff radius is set, and in order to solve the problem of low calculation efficiency of long-range Coulomb interaction, the PPPM algorithm is used to determine the Coulomb interaction force in the embodiment. Since the most basic molecular force field parameters can only represent the interaction between atoms of the same type, the arithmetic mixing rule is used to approximate the interaction between atoms of different types. The model boundary of the deep shale nanometer pore molecular model is set as a periodic boundary, and the force of the atoms in the deep shale nanometer pore molecular model is set to zero, which is used to maintain the position stability of the deep shale nanometer pore molecular model.

[0114] The shale gas fluid model is preprocessed using an energy minimization algorithm. By adjusting atomic positions to ensure no overlapping molecular structures, a stable initial configuration of the shale gas fluid is obtained. The model is then placed within an NVT canonical ensemble, and the Nosé-Hoover algorithm is used to control its temperature, ensuring a realistic representation of the temperature environment of deep shale reservoirs. The simulation step size is set to 1 fs, and simulation temperature and pressure are defined. Atomic forces under different pressures are calculated to achieve equilibrium simulation at corresponding pressures. Based on the output atomic plate trajectories, the simulation continues until the system pressure stabilizes. The atomic plates are then fixed, and the shale gas occurrence simulation continues, with real-time calculation of the shale gas density in the z-direction. When the shale gas density no longer changes, molecular trajectory and density distribution data are acquired and processed.

[0115] The `dump` command is used to output the molecular trajectory file of the shale gas fluid model, determining the density distribution of each component in the shale gas fluid model. The pore space is divided into multiple regions in the z-direction at 5nm intervals, and the density of each component of the shale gas fluid in each region is calculated. A simulation calculation is performed every 0.2ns, and the density of each component in the shale gas fluid within the past 0.2ns is averaged to obtain the density distribution curve of each component in the shale gas fluid as a function of time and spatial location throughout the simulation. Based on the density change curve of each component of the shale gas fluid, the region with stable density change is divided into the free region, and the remaining regions are divided into the adsorption region, thus determining the adsorption and free regions of each component of the shale gas fluid in the deep shale nanoporous molecular model.

[0116] Specifically, the PPPM algorithm determines the potential energy of the Coulomb interaction as follows:

[0117] ;

[0118] in,

[0119] ;

[0120] In the formula, This represents the Coulomb interaction potential energy. This represents the short-range Coulomb interaction potential energy. This represents the long-range Coulomb interaction potential energy.

[0121] The long-range Coulomb interaction potential is realized by gridding the charges and solving the Poisson equation, and its potential function is:

[0122] ;

[0123] In the formula, This is the long-range Coulomb interaction potential energy function; This is the function for calculating the grid potential; is the position coordinate of atom is the position coordinate of grid point is the serial number of grid point is an interpolation function used to map the grid potential back to the particle position.

[0124] The arithmetic mixing rule is set as:

[0125]

[0126]

[0127] wherein, is the potential well depth between atom and atom is the potential well depth between two atoms is the potential well depth between two atoms is the collision volume between atom and atom is the collision volume between two atoms is the collision volume between two atoms

[0128] Further, the NVT ensemble is used to keep the number of atoms and the simulation space unchanged, the temperature is controlled by the Nosé-Hoover algorithm in the prior art, the key is to construct an "extended Hamiltonian" containing a heat bath variable, and the temperature control is realized by solving the corresponding motion equation, and relevant instructions are called to divide the simulation area to calculate the density of each component in the shale gas fluid.

[0129] Step 3, according to the adsorption zone and free zone divided by each component of the shale gas fluid, the shale gas adsorption layer thickness at the organic pore wall surface is obtained, the free density and adsorption density of the shale gas fluid and each component thereof at the organic pore wall surface under different pressures at the current temperature condition are determined respectively, the adsorption density data set and the free density data set of the shale gas are obtained, based on the Langmuir isothermal adsorption formula, data fitting is carried out respectively for the adsorption density data set and the free density data set, the density calculation functions of the adsorption zone and the free zone are determined, and the shale gas mobility evaluation formula at the molecular scale is established.

[0130] ​​​​​​​​​​Further, based on Langmuir isothermal adsorption formula, the adsorption density data set of shale gas fluid under the same temperature and different pressure conditions is fitted to obtain a shale gas density calculation function of the adsorption zone as follows:

[0131]

[0132] wherein, is the shale gas density of the adsorption zone under the specified pressure, and the unit is is the maximum adsorption density, and the unit is is the adsorption constant, and the unit is is the formation pressure, and the unit is .

[0133] The free density data set of shale gas fluid under the same temperature and different pressure conditions is fitted to obtain a shale gas density calculation function of the free zone as follows:

[0134]

[0135] wherein, is the shale gas density of the free zone under the specified pressure, and the unit is all are the fitting coefficients of the free zone.

[0136] Based on the shale gas density calculation functions of the adsorption zone and the free zone, a shale gas mobility evaluation formula is established at the molecular scale to obtain:

[0137]

[0138] wherein,

[0139]

[0140]

[0141] wherein, is the shale gas mobility degree; is the shale gas adsorbed state mass in the deep shale pores, and the unit is is the shale gas free state mass per unit length, and the unit is is the shale gas adsorbed state mass under the initial formation pressure, and the unit is is the shale gas free state mass under the initial formation pressure, and the unit is​​​​​​​​​​​​​​ ; is the shale gas density in the adsorbed zone under the current pore pressure, and the unit is g / cm3. ; is the shale gas density in the free zone under the current pore pressure, and the unit is g / cm3. ; is the adsorbed zone volume; is the free zone volume.

[0142] Specifically, taking the pipe pore as an example, the internal pore of the deep shale nanometer pore molecular model is set as a pipe pore in the form of a cylinder, and the shale gas mobility evaluation formula of the multi-component deep shale gas pore occurrence model under the molecular scale is obtained as follows:

[0143] ;

[0144] In the formula, is the pore radius, and the unit is cm; ; is the shale gas adsorption layer thickness at the organic pore wall, and the unit is cm. .

[0145] Step 4, the shale gas mobility evaluation formula is applied to the pore scale, the deep shale reservoir core is scanned, the SEM scanning image of the deep shale reservoir core is obtained, the threshold segmentation is performed on the SEM scanning image to obtain a binary image, the three-dimensional digital core is reconstructed by using the Markov chain Monte Carlo method in the prior art, and the pore network model is extracted based on the SNOW algorithm of the watershed method.

[0146] Further, the deep shale reservoir core is scanned, the SEM scanning image of the deep shale reservoir core is obtained, the threshold segmentation is performed on the SEM scanning image after noise reduction processing, the region with a voxel value of 0 represents a pore, and the region with a voxel value of 1 represents rock, a binary image is obtained, a three-dimensional digital core is reconstructed by using the Markov chain Monte Carlo method, an open source program package OpenPNM is used, a pore network structure model is extracted based on the SNOW algorithm of the watershed method in the prior art, and the pore and throat information in the three-dimensional digital core is assigned to the pore network model.

[0147] Specifically, the threshold segmentation binary assignment formula is as follows:

[0148] ;

[0149] In the formula, is the pixel point voxel value after binaryzation; is the gray value of the pixel point ; and The preset threshold is used for selecting the pixel point according to the gray value; The horizontal coordinate of the pixel point in the SEM scanning image is denoted as x. The vertical coordinate of the pixel point in the SEM scanning image is denoted as y.

[0150] Step 5: Structural parameter analysis is performed on the pore network model to obtain the size distribution of the throats and pores in the pore network model, and the thickness of the shale gas adsorption layer at the organic pore wall is combined to calculate the volume of the adsorption zone and the free zone in the deep shale nanometer pore molecular model, respectively.

[0151] Further, the total pore volume in the deep shale nanometer pore molecular model is:

[0152] ;

[0153] In the formula, The total pore volume of the deep shale nanometer pore molecular model is denoted as V, and the unit is ; The pore corresponding to the pore radius is denoted as r. The throat corresponding to the throat radius is denoted as r. The throat corresponding to the throat length is denoted as l. The pore serial number is denoted as i. The throat serial number is denoted as j. The total number of pores is denoted as Np. The total number of throats is denoted as Nt. The free zone volume in the deep shale nanometer pore molecular model is: ;

[0154] In the formula,

[0155] The free zone volume is denoted as Vf, and the unit is ; The thickness of the shale gas adsorption layer at the organic pore wall is denoted as d, and the unit is

[0156] . The adsorption zone volume in the deep shale nanometer pore molecular model is: ;

[0157] In the formula,

[0158] The adsorption zone volume is denoted as Va, and the unit is .

[0159]

[0160] ​​​Step 6, for the deep shale reservoir to be evaluated, set the initial formation pressure, use the shale gas mobility evaluation formula, combine the volume of the adsorption zone and the free zone, and the shale gas density calculation function to evaluate the use of shale gas in the deep shale reservoir to be evaluated after the formation pressure is reduced.

[0161] Example 2

[0162] In order to verify the application effect of the cross-scale multi-component deep shale gas mobility evaluation method proposed by the method of the present application, the cross-scale multi-component deep shale gas mobility evaluation method described in Example 1 is used, which specifically includes the following steps:

[0163] Step 1, use molecular simulation software to construct a multi-component deep shale gas pore occurrence model.

[0164] In this embodiment, taking organic pores as an example, the kerogen molecules of the deep shale reservoir of the Lower Silurian Longmaxi Formation in South Sichuan with a depth of about 4145m are taken as basic units, as shown in FIG. 1, the kerogen molecules are drawn in the Materials Studio software according to the molecular information of the kerogen molecules, the AC module is called to add the CVFF force field tag and the charge information to the kerogen molecules, and after the kerogen molecule model is constructed, the kerogen molecule model file is exported in the car format and processed by the msi2lmp software package to obtain the data format file of the kerogen molecule model, and the Moltemplate program is called to obtain the force field information of the kerogen molecules. Figure 2 According to the molar mass of the kerogen molecules and the size and density of the preset organic pore wall surface, the PACKMOL software package is called to randomly place 15 kerogen molecule models in a deep shale nanometer pore space with a size of 30

[0165] After obtaining the pdb file of the kerogen matrix, the Moltemplate program is called to add corresponding CVFF force field tags and charge information to each atom in the deep shale nanometer pore molecular model, and export it as a data format recognizable by the LAMMPS molecular simulator. According to the preset size of the organic pore wall surface, a virtual wall is created in the LAMMPS molecular simulator in the x, y, and z directions to compress the deep shale nanometer pore space, as shown in FIG. 2, forming an organic pore wall surface with a density of 1.3

[0166] After the compression process, the kerogen matrix becomes smooth under the compression of the smooth wall, so the organic pore wall surface obtained by removing the virtual wall is simulated using the NVT ensemble for annealing, and the FIRE algorithm in the prior art is used for energy minimization to increase the roughness of the organic pore wall surface, thereby obtaining an organic pore wall surface model with rough wall surface. Figure 3 After the compression process, the kerogen matrix becomes smooth under the compression of the smooth wall, so the organic pore wall surface obtained by removing the virtual wall is simulated using the NVT ensemble for annealing, and the FIRE algorithm in the prior art is used for energy minimization to increase the roughness of the organic pore wall surface, thereby obtaining an organic pore wall surface model with rough wall surface. ​

[0167] Call LigPargen website database to obtain multi-component shale gas molecule information, and in this embodiment, single-component shale gas fluid and multi-component shale gas fluid are taken as examples, wherein the composition of the single-component shale gas fluid is pure methane, and the multi-component shale gas fluid is composed of methane and propane with a mass fraction of 50%.

[0168] Obtain the xyz coordinate file of each molecule model in the single-component shale gas fluid and the multi-component shale gas fluid, call the software package PACKMOL to place 2000 methane molecules in the single-component shale gas fluid, 1500 methane molecules, 546 propane molecules and 30 n-dodecane molecules in the multi-component shale gas fluid in the deep shale nanometer pore molecular model of 9 nm x 5 nm x 9 nm, respectively, to obtain the pdb files of the single-component shale gas fluid and the multi-component shale gas fluid, call the Materials Studio software to add corresponding OPLS-AA force field tags and charge information to each atom in the shale gas molecule model, and convert it into a data format recognizable by the LAMMPS molecular simulator, set the OPLS-AA force field parameters of the shale gas molecule model, and complete the construction of the shale gas fluid model.

[0169] Combine the deep shale nanometer pore molecular model with the shale gas fluid model to obtain a multi-component deep shale gas pore occurrence model, and set fixed atom walls on the left and right sides of the pore wall model and movable atom walls on the upper and lower sides of the pore wall model, and use the fixed atom walls and the movable atom walls to control the pore pressure inside the multi-component deep shale gas pore occurrence model.

[0170] Step 2, set the force field information to describe the interaction rules between atoms, preprocess the shale gas fluid model based on the energy minimization algorithm to obtain a relatively stable initial configuration, at this time the shale gas fluid model is stably stored in the deep shale nanometer pore molecular model, set the simulation temperature, change the stress condition of the atom plate, apply various pressures to the shale gas fluid model, simulate to obtain the density change curve of each component in the shale gas fluid model under different pressure conditions, and divide the adsorption zone and the free zone of each component of the shale gas fluid in the deep shale nanometer pore molecular model.

[0171] In this embodiment, in the multi-component deep shale gas pore occurrence model, the short-range van der Waals force and the Coulomb force are considered simultaneously, the cutoff radius is set, and the PPPM algorithm is used to determine the Coulomb interaction force in order to solve the problem of low calculation efficiency of long-range Coulomb interaction. Since the basic molecular force field parameters can only represent the interaction between atoms of the same type, the arithmetic mixing rule is used to approximate the interaction between atoms of different types. The boundary of the deep shale nanometer pore molecular model is set as a periodic boundary, and the force on the atoms in the deep shale nanometer pore molecular model is set to zero, which is used to maintain the position stability of the deep shale nanometer pore molecular model.

[0172] The shale gas fluid model is preprocessed based on the energy minimization algorithm, the atomic positions are used to make the molecular structure in the shale gas fluid model without overlapping, and after obtaining the stable initial configuration of the shale gas fluid, the shale gas fluid model is placed in an NVT canonical ensemble, the Nosé-Hoover algorithm is used to control the temperature of the shale gas fluid model, which is used to satisfy the real reduction of the temperature environment of the deep shale reservoir, the simulation step is set to 1 fs and the simulation temperature is set to 393 K, the simulation pressure is set to 10 MPa, 20 MPa, 30 MPa, 40 MPa, 50 MPa, 60 MPa and 70 MPa, respectively, the atomic force under different pressures is calculated, and the balance simulation under the corresponding pressure is realized. According to the output atomic plate trajectory, the atomic plate is fixed and the shale gas occurrence simulation is continued after the system pressure tends to be stable, the density of shale gas in the z direction is calculated in real time, and when the simulation runs to the shale gas density no longer changes, the molecular trajectory and density distribution data are obtained and processed.

[0173] The dump command is called to output the molecular trajectory file of the shale gas fluid model, the density distribution of each component in the shale gas fluid model is determined, the pore space is divided into multiple regions in the z direction at an interval of 5 nm, the density of each component of the shale gas fluid in each region is calculated, the simulation calculation is performed once every 0.2 ns, and the shale gas fluid component density obtained by a total of 200,000 simulation calculations in the past 0.2 ns is averaged to obtain the density distribution curve of each component of the shale gas fluid in the entire simulation process with respect to time and spatial position (i.e., along the Z-axis direction). According to the density change curve of each component of the shale gas fluid, the density change stable region is divided into a free zone, and the remaining region is divided into an adsorption zone, so as to determine the adsorption zone and the free zone of each component of the shale gas fluid in the deep shale nanometer pore molecular model.

[0174] Figure 4 The density distribution curve of the single-component shale gas fluid simulated by the present application is shown in the following figure, Figure 5 The density distribution curve of the multi-component shale gas fluid simulated by the present application is shown in the following figure.Figure 4 and Figure 5 It can be seen that the components in shale gas fluid have the characteristics of multi-layer adsorption at the organic pore wall surface, according to which the free state and adsorption state of shale gas are distinguished, that is, the smooth density change region is divided into a free zone, and the remaining region with higher density than the free zone is divided into an adsorption zone, so as to determine the adsorption zone and free zone of each component in the shale gas fluid in the deep shale nanopore molecular model. Moreover, for both single-component shale gas fluid and multi-component shale gas fluid, the adsorption layer thickness of methane is 1.18 nm and does not change with pressure, and the adsorption layer thickness of propane is 1.5 nm, thereby providing convenience for the calculation of the volume of the adsorption zone and the free zone.

[0175] Step 3: According to the adsorption zone and free zone of each component in shale gas fluid, the shale gas adsorption layer thickness at the organic pore wall surface is obtained, and the free density and adsorption density of shale gas fluid and each component thereof at the organic pore wall surface under different pressures at the current temperature condition are determined, as shown in Table 1. In Table 1, Nos. 1-8 are the adsorption gas density and free gas density of single-component shale gas fluid, and Nos. 9-15 are the adsorption gas density and free gas density of multi-component shale gas fluid.

[0176] Table 1: Adsorption gas density and free gas density under different pressure conditions at a temperature of 393 K

[0177]

[0178] According to the free density and adsorption density of shale gas fluid and each component thereof at the organic pore wall surface under different pressures, the adsorption density data set and the free density data set of shale gas are constructed, and data fitting is performed on the adsorption density data set and the free density data set based on the Langmuir isothermal adsorption formula, and the fitting coefficients are shown in Table 2, thereby obtaining the shale gas density calculation functions in the adsorption zone and the free zone for single-component shale gas fluid and multi-component shale gas fluid.

[0179] Table 2: Fitting coefficients of shale gas density calculation functions

[0180]

[0181] Thus, the shale gas density calculation functions in the adsorption zone and the free zone are determined, and the density fitting curves of single-component shale gas fluid and multi-component shale gas fluid in the adsorption zone and the free zone are obtained, as shown in Table 3. Figure 6 After comparison, it is found that the density fitting curves and the simulation data are well fitted, and the fitting accuracy of both is higher than 0.99, which shows that the fitting formula is appropriate in the current temperature and pressure range, and can accurately represent the shale gas component density values in the adsorption zone and the free zone in the organic pore under the change of pressure.

[0182] At the same time, because the phase behavior of propane dominates the change of adsorption layer density during the pressure change, the influence of methane on the adsorption layer of propane is different at different pressures. Under the combined influence of phase behavior, multi-component competitive adsorption and wall adsorption, the density curve of propane is more complex. The optimal model function of Origin software is used to fully consider the above influences to select the best function model for curve fitting, and the corrected propane density fitting curve is obtained, as shown in Figure 7 .

[0183] Based on the adsorption zone and free zone shale gas density calculation function, the shale gas mobility evaluation formula is established at the molecular scale, and the following is obtained:

[0184]

[0185]

[0186]

[0187]

[0188] In the formula, is the degree of shale gas mobility; is the adsorbed state mass of shale gas in deep shale pores, with the unit of ; is the free state mass of shale gas per unit length, with the unit of ; is the adsorbed state mass of shale gas under the initial formation pressure, with the unit of ; is the free state mass of shale gas under the initial formation pressure, with the unit of ; is the density of shale gas in the adsorption zone under the current pore pressure, with the unit of ; is the density of shale gas in the free zone under the current pore pressure, with the unit of ; is the volume of the adsorption zone; is the volume of the free zone.

[0189] Specifically, taking the pipeline pore as an example, the internal pore of the deep shale nanometer pore molecular model is set as a pipeline pore in the form of a cylinder, and the shale gas mobility evaluation formula of the multi-component deep shale gas pore occurrence model at the molecular scale is obtained as follows:

[0190]

[0191] In the formula, is the pore radius, with the unit of ; is the thickness of the shale gas adsorption layer at the organic pore wall, with the unit of​​​​​ .

[0192] In this embodiment, the initial formation pressure is set to 70 MPa and the temperature to 393 K. The mobility of methane within pores of different radii as pressure decreases is calculated using the above formula. Figure 8 As shown, by Figure 8 It can be seen that under the condition of fully depleted extraction, the larger the pore size, the higher the utilization of shale gas. When the deep shale reservoir reaches a certain pore pressure, the difference in methane utilization within different pore radii has reached 10%. Based on this figure and the actual situation, the extraction scheme can be planned to achieve the best recovery rate.

[0193] Step 4: The shale gas mobility evaluation formula is applied to the pore scale. Core scanning is performed on deep shale reservoir cores collected in the field to obtain SEM scan images. After noise reduction processing of the SEM scan images, threshold segmentation is performed. During threshold segmentation, regions with a voxel value of 0 represent pores, and regions with a voxel value of 1 represent rock. After obtaining the binarized image, the Markov chain Monte Carlo method is used to reconstruct the 3D digital core, resulting in a 200×200×200 voxel 3D digital core. The pore network structure model is extracted using the open-source package OpenPNM and the existing SNOW algorithm based on the watershed method. The pore and throat information from the 3D digital core is then incorporated into the pore network model. Figure 9 As shown.

[0194] Step 5: Perform structural parameter analysis on the pore network model to obtain the size distribution of throats and pores in the pore network model, such as... Figures 10-12 As shown, the volumes of the adsorption and free zones in the deep shale nanoporous molecular model are calculated by combining the thickness of the shale gas adsorption layer at the organic pore wall.

[0195] In this embodiment, the calculated adsorption volume of methane is: The volume of the free region is The volume of the propane adsorption zone is The volume of the free region is .

[0196] Step 6: For the deep shale reservoir to be evaluated in this embodiment, the initial formation pressure is set to 70 MPa and the formation temperature to 393 K. Using the shale gas mobility evaluation formula, combined with the volume of the adsorption and free zones and the shale gas density calculation function, the mobility of single-component and multi-component shale gas fluids within the deep shale reservoir is analyzed, such as... Figure 13 As shown.

[0197] By comparing the exploitation of single-component shale gas fluid and multi-component shale gas fluid in deep shale reservoirs, it is found that because the core pore size of the deep shale reservoir in the embodiment is generally higher than 10 nm, the exploitation of total shale gas is more close to free gas, and the mobility of propane is strong in the initial stage of pressure reduction, which is mainly due to that the density of free propane is more sensitive to pressure change at high pressure, and the expansion of propane is faster when the pressure is reduced, and the mobility of adsorbed propane is very poor in the early stage of pressure reduction, and until the late stage of pressure reduction, a large amount of adsorbed propane is desorbed and can be exploited.

[0198] In summary, the method of the present application can accurately evaluate the mobility of multi-component deep shale gas, obtain the ideal exploitation degree of multi-component deep shale gas, and effectively evaluate the influence of various factors on the mobility of different components of deep shale gas in the depletion exploitation, thereby providing a basis for guiding the exploration and development of deep shale gas.

[0199] Of course, the above description is not a limitation of the present application, and the present application is not limited to the above examples, and the changes, modifications, additions or replacements made by the person skilled in the art within the essential scope of the present application should also belong to the protection scope of the present application.

Claims

1. A method for evaluating the mobility of multi-component deep shale gas across scales, characterized in that, The method comprises the following steps: Step 1, a multi-component deep shale gas pore occurrence model is constructed by using molecular simulation software, which is combined by a deep shale nanopore molecular model and a shale gas fluid model, wherein the deep shale nanopore molecular model is internally provided with a pore wall model, including an organic pore wall and an inorganic mineral wall, and the shale gas fluid model is stored in the pore wall model; Step 2, force field information is set, the shale gas fluid model is pretreated based on an energy minimization algorithm to make it stably stored in the deep shale nanopore molecular model, a simulation temperature is set, and the shale gas fluid model is used to simulate the density change curve of each component of the shale gas fluid under different pressure conditions, and the adsorption zone and the free zone of each component of the shale gas fluid in the deep shale nanopore molecular model are divided; Step 3, according to the divided adsorption zone and free zone, the shale gas adsorption layer thickness at the organic pore wall is obtained, the free density and the adsorption density of the shale gas fluid and each component thereof at the organic pore wall under different pressures at the current temperature condition are determined respectively, the shale gas density calculation functions of the adsorption zone and the free zone are fitted, and a shale gas mobility evaluation formula at a molecular scale is established; Step 4, the shale gas mobility evaluation formula is applied to a pore scale, and a pore network model is extracted from a deep shale reservoir core collected on site; The internal pores of the deep shale nanopore molecular model are set as pipeline pores in the form of cylinders, and the shale gas mobility evaluation formula of the multi-component deep shale gas pore occurrence model at a molecular scale is obtained as follows: ； wherein Pf is the formation pressure; rp is the pore radius; is the thickness of the adsorbed layer of shale gas at the organic pore wall surface; is the density of the adsorbed zone of shale gas at the current pore pressure; is the density of the free zone of shale gas at the current pore pressure; Step 5, the structure parameters of the pore network model are analyzed to obtain the size distribution of the throats and pores, and the volumes of the adsorption zone and the free zone in the deep shale nanopore molecular model are calculated in combination with the shale gas adsorption layer thickness at the organic pore wall; Step 6, for a deep shale reservoir to be evaluated, an initial formation pressure is set, the shale gas mobility evaluation formula is used in combination with the volumes of the adsorption zone and the free zone and the shale gas density calculation function to evaluate the production of the shale gas in the deep shale reservoir to be evaluated after the formation pressure is reduced.

2. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 1, characterized in that, In step 1, in the molecular simulation software, a kerogen molecular unit and an inorganic mineral unit are selected, kerogen molecules and inorganic mineral cells are obtained, a CVFF force field tag and charge information are added to the kerogen molecules to construct a kerogen molecular model, inorganic pore walls are obtained by cutting the inorganic mineral cells, a CLAYFF force field tag and charge information are added to the inorganic mineral cells in combination with the atomic types at different positions in the inorganic mineral cells to construct an inorganic mineral cell model. According to the molar mass of kerogen molecules and the preset size of the organic pore wall surface, a plurality of kerogen molecule models are randomly placed in the deep shale nanometer pore space, a virtual wall is arranged to compress the deep shale nanometer pore space, an organic pore wall surface is formed, the energy of the organic pore wall surface is minimized, the roughness of the organic pore wall surface is increased, and a wall rough organic pore wall surface model is obtained; the inorganic mineral cell model is replicated and expanded, an inorganic mineral wall surface model is constructed, and the CLAYFF force field parameters of the inorganic mineral wall surface model are set according to the CLAYFF force field label of the inorganic mineral cell model; The organic pore wall surface model and the inorganic mineral wall surface model are imported into the Lammps molecular simulator to construct a deep shale nanometer pore molecular model; A plurality of component shale gas molecular information is obtained, a shale gas molecular model is determined, OPLS-AA force field labels and charge information are added to each atom in the shale gas molecular model, OPLS-AA force field parameters of the shale gas molecular model are set, and a shale gas fluid model is obtained; The deep shale nanometer pore molecular model and the shale gas fluid model are combined to construct a multi-component deep shale gas pore occurrence model, an atomic plate for controlling pressure is set, and the pore pressure inside the multi-component deep shale gas pore occurrence model is controlled by using the atomic plate.

3. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 2, characterized in that, The force per unit atom on the atomic plate is: ； wherein is the unit atomic force, in ; is the system pressure, in ; is the atomic plate area, in ; is the number of atoms in the atomic plate; The interaction between atoms in the multi-component deep shale gas pore occurrence model is: ； ； ； ； wherein is the coulomb interaction energy; is the point charge; is the vacuum permittivity; , are both atomic numbers; is the charge amount of atom ; is the charge amount of atom ; is the distance between atom and atom ; is the van der Waals interaction energy; , are both fitting empirical coefficients; is the bond interaction energy; is the bond interaction empirical coefficient; is the angle interaction energy; is the angle interaction empirical coefficient; is the bond stretch equilibrium distance; is the angle between atom , atom , atom ; is the equilibrium angle.

4. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 3, characterized in that, In step 2, in the multi-component deep shale gas pore occurrence model, the cutoff radius is set, the PPPM algorithm is used to determine the Coulomb interaction force, the arithmetic mixing rule is used to set the interaction between different types of atoms, and the model boundary is set as a periodic boundary, the atomic force in the deep shale nanometer pore molecular model is set to zero, and the position of the deep shale nanometer pore molecular model is maintained stable; The shale gas fluid model is preprocessed based on the energy minimization algorithm, the atomic positions are used to make the shale gas fluid model without overlapping molecular structures, a stable shale gas fluid initial configuration is obtained, the shale gas fluid model is placed in an NVT canonical ensemble, the Nosé-Hoover algorithm is used to control the temperature of the shale gas fluid model, the simulation step, simulation temperature and simulation pressure are set, and the multi-component deep shale gas pore occurrence model is simulated under the current pressure condition to obtain the density variation curve of each component of the shale gas fluid in the deep shale nanometer pore molecular model at different positions, and the density variation curve of each component of the shale gas fluid at different positions in the deep shale gas reservoir pore space is determined. According to the density variation curve of each component of the shale gas fluid, the density variation stable region is divided into a free region, and the remaining region is divided into an adsorption region, so as to determine the adsorption region and the free region of each component of the shale gas fluid in the deep shale nanometer pore molecular model.

5. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 4, characterized in that, The PPPM algorithm determines the Coulomb interaction potential energy as: ； wherein, ； wherein is the Coulomb interaction potential; is the short-range Coulomb interaction potential; is the long-range Coulomb interaction potential; The long-range Coulomb interaction is solved by using the gridding charge as: ； wherein is the long-range Coulomb interaction potential energy function; is the grid potential calculation function; is the position coordinate of atom ; is the position coordinate of grid point ; is the grid point index number; is the interpolation function; The arithmetic mixing rule is set as: ； ； wherein is the potential well depth between atom and atom ; is the potential well depth between two atoms ; is the potential well depth between two atoms ; is the collision volume between atom and atom ; is the collision volume between two atoms ; is the collision volume between two atoms .

6. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 1, wherein, In the step 3, based on the Langmuir isothermal adsorption formula, the adsorption density data set of the shale gas fluid under the same temperature and different pressure conditions is fitted to obtain the shale gas density calculation function of the adsorption zone as follows: ； wherein is the shale gas density at the specified pressure in the adsorption zone; is the maximum adsorption density; is the adsorption constant; is the formation pressure; In the step 4, the SEM scanning image of the deep shale reservoir core is obtained by scanning the deep shale reservoir core collected on site, and the binary image is obtained after the threshold segmentation of the SEM scanning image, and the three-dimensional digital core is reconstructed by using the Markov chain Monte Carlo method, and the pore network model is extracted based on the SNOW algorithm of the watershed method. ； wherein is the shale gas density at the specified pressure downstream the free zone; , , are the free zone fitting coefficients; The threshold segmentation binary assignment formula is as follows: ； In the step 5, the total pore volume in the deep shale nanometer pore molecular model is as follows: ； ； wherein is the shale gas movable degree; is the shale gas adsorbed state mass in deep shale pores; is the shale gas free state mass per unit length; is the shale gas adsorbed state mass under initial formation pressure; is the shale gas free state mass under initial formation pressure; is the shale gas density in the adsorbed zone under current pore pressure; is the shale gas density in the free zone under current pore pressure; is the adsorbed zone volume; is the free zone volume.

7. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 1, wherein, The free zone volume in the deep shale nanometer pore molecular model is as follows: The adsorption zone volume in the deep shale nanometer pore molecular model is as follows: ； In the formula, is a pixel point is a voxel value after binarization; is a pixel point is a gray value of the pixel point; is a preset threshold value; is an abscissa of a pixel point in the SEM scanning image; is an ordinate of a pixel point in the SEM scanning image.

8. The method for evaluating the cross-scale multi-component deep shale gas mobility according to claim 1, wherein, The threshold segmentation binary assignment formula is as follows: ； wherein, is the total volume of the pore for the deep shale nanopore molecular model; is the pore corresponding to the pore radius; is the throat corresponding to the throat radius; is the throat corresponding to the throat length; is the pore number; is the throat number; is the total number of pores; is the total number of throats; ​ ； wherein is the volume of the free zone; is the thickness of the shale gas adsorption layer at the organic pore wall. ​ ； In the formula, is the volume of the adsorption zone.

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