Method, device and equipment for determining pore adsorption capacity

Abstract

The invention discloses a method, device and equipment for determining the pore adsorption capacity, and the method comprises the steps: obtaining the pore shape features of a target reservoir, the pore shape features being used for representing the shape features of various pores of the target reservoir; based on the pore shape features, multiple target pore structure models are established, and different pore structure models correspond to the shape features of different pores; adsorption simulation is conducted on the multiple target pore structure models in different environments, adsorption simulation data are obtained, and the adsorption simulation data comprise different environment data and the adsorption amount of pores under different pore shape characteristics; constructing an adsorption capacity model based on the adsorption simulation data, and determining the adsorption capacity of pores of the target reservoir based on the adsorption capacity model; the adsorption capacity model is used for representing the mapping relation between the adsorption capacity of the pores and the environment and pore shape characteristics. By means of the method, the accuracy and reliability of predicting the adsorption capacity of the pores of the target reservoir can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a method, device and equipment for determining pore adsorption capacity. Background Art

[0002] With the increasing demand for oil and gas resources and the in-depth exploration and development theory and methods, continental shale reservoirs have increasingly become an important field for obtaining stable oil and gas production. The adsorption capacity of shale gas is an important parameter for predicting the resource volume of shale gas reservoirs. Kerogen organic pores are the main storage spaces in shale gas reservoirs, mainly micron- and nano-scale pores. The pore structure characteristics affect the physical properties of the reservoir, thereby affecting the occurrence state, content and seepage mechanism of fluids inside the reservoir.

[0003] Currently, experimental testing techniques are mostly used to characterize pore structures, which can visually characterize the organic / inorganic pore structures and throat distribution characteristics of rocks. However, due to the limitations of experimental conditions and samples, it is impossible to study the influence mechanism of a single type of pore structure on methane adsorption, and the accuracy and reliability of the pore adsorption capacity determined by existing solutions are relatively low.

[0004] Regarding the problem of relatively low accuracy and reliability in predicting pore adsorption capacity, no effective solution has been found yet. Summary of the Invention

[0005] The purpose of the embodiments of this specification is to provide a method, device and equipment for determining pore adsorption capacity to solve the problem of relatively low accuracy and reliability in predicting pore adsorption capacity.

[0006] To solve the above technical problems, the first aspect of this specification provides a method for determining pore adsorption capacity, including:

[0007] Obtain the pore shape characteristics of the target reservoir, where the pore shape characteristics are used to characterize the shape characteristics of various pores in the target reservoir;

[0008] Based on the pore shape characteristics, establish multiple target pore structure models, and different pore structure models correspond to different pore shape characteristics;

[0009] Perform adsorption simulations on multiple target pore structure models in different environments respectively to obtain adsorption simulation data, where the adsorption simulation data includes the adsorption capacity of pores under different environmental data and different pore shape characteristics;

[0010] Construct an adsorption capacity model based on the adsorption simulation data to determine the adsorption capacity of the pores in the target reservoir based on the adsorption capacity model; the adsorption capacity model is used to characterize the mapping relationship between the adsorption capacity of pores and the environment and pore shape characteristics.

[0011] In some embodiments of this specification, obtaining the pore shape characteristics of the target reservoir includes:

[0012] Obtaining a shale sample of the target reservoir;

[0013] Scanning the shale sample with a scanning electron microscope to obtain a scanned image of the shale sample;

[0014] Performing an isothermal adsorption experiment on the shale sample and determining the pore size distribution of the shale sample based on the experimental results;

[0015] Based on the scanned image and the pore size distribution, determining the pore shape characteristics, where the pore shape characteristics include pore shape information and size information.

[0016] In some embodiments of this specification, based on the pore shape characteristics, establishing multiple target pore structure models includes:

[0017] Constructing a periodic simulation box in the shape of a cube with a preset side length;

[0018] Based on the pore shape characteristics, determining the size parameters of multiple columnar pores with the same volume, different shapes as cross-sections, and a preset side length, and constructing multiple pore boundaries based on the size parameters of the multiple columnar pores;

[0019] Uniformly filling kerogen molecules between each pore boundary and the simulation box to construct multiple target pore structure models.

[0020] In some embodiments of this specification, uniformly filling kerogen molecules between each pore boundary and the simulation box to construct multiple target pore structure models includes:

[0021] Uniformly filling kerogen molecules between each pore boundary and the simulation box to obtain multiple initial pore structure models;

[0022] Performing simulated geometric optimization on each initial pore structure model using the canonical ensemble to obtain multiple pore structure models in an equilibrium state;

[0023] Performing annealing treatment on each pore structure model in an equilibrium state using the isothermal-isobaric ensemble, and based on the relationship between the density of the kerogen periodic system and temperature determined during the analysis of the annealing process, when it is determined that the density of the kerogen molecules satisfies a preset density threshold, stopping the annealing treatment and taking the pore structure model corresponding to the last frame of the annealing treatment as the target pore structure model corresponding to each pore structure model in an equilibrium state.

[0024] In some embodiments of this specification, adsorption simulations are respectively performed on multiple target pore structure models under different environments to obtain adsorption simulation data, including:

[0025] Set the force field, charge distribution method, electrostatic interaction, van der Waals interaction method, and methane fugacity of the adsorption simulation;

[0026] Perform grand canonical Monte Carlo simulations of methane adsorption on each of the target pore structure models to obtain the methane maximum adsorption amount data corresponding to each target pore structure model under different temperature and pressure conditions;

[0027] Calculate the absolute adsorption amount data of methane corresponding to each target pore structure model based on the methane maximum adsorption amount data corresponding to each target pore structure model.

[0028] In some embodiments of this specification, the absolute adsorption amount data is calculated by the following formula:

[0029]

[0030] where Y A is the absolute adsorption amount, mmol / g; Y0 is the maximum adsorption amount, mmol / g; x p is the fugacity of the gas, MPa; is the saturated gas pressure, MPa; D is a parameter related to the affinity between the adsorption matrix and the gas; G C is the pore shape factor.

[0031] In some embodiments of this specification, an adsorption amount model is constructed based on the adsorption simulation data, including:

[0032] Establish a multiple nonlinear regression model of temperature, pressure, pore shape factor, and methane absolute adsorption amount. The pore shape factor includes the pore cross-sectional perimeter and the pore cross-sectional area;

[0033] Based on the temperature data, pressure data, pore shape factor data, and the corresponding methane absolute adsorption amount data in the adsorption simulation data, fit the multiple nonlinear regression model, and calculate the value of the coefficient of determination based on the fitted model;

[0034] Adjust the multiple nonlinear regression model based on the value of the coefficient of determination until the value of the coefficient of determination meets the preset conditions to obtain the adsorption amount model.

[0035] In some embodiments of this specification, the adsorption amount model is represented by the following formula:

[0036]

[0037] Among them, Y represents the absolute methane adsorption amount, x1 represents the temperature, x2 represents the pressure, x3 represents the pore shape factor, and a, b, c, e, f, and τ represent coefficient constants.

[0038] The second aspect of this specification provides a device for determining pore adsorption amount, including:

[0039] A data acquisition module, configured to acquire the pore shape characteristics of the target reservoir, where the pore shape characteristics are used to characterize the shape characteristics of various pores in the target reservoir;

[0040] A first model construction module, configured to establish multiple target pore structure models based on the pore shape characteristics, where different pore structure models correspond to the shape characteristics of different pores;

[0041] An adsorption simulation module, configured to perform adsorption simulations on multiple target pore structure models in different environments respectively to obtain adsorption simulation data, where the adsorption simulation data includes the adsorption amounts of pores under different environmental data and different pore shape characteristics;

[0042] A second model construction module, configured to construct an adsorption amount model based on the adsorption simulation data to determine the adsorption amount of the pores in the target reservoir based on the adsorption amount model; the adsorption amount model is used to characterize the mapping relationship between the adsorption amount of pores and the environment and pore shape characteristics.

[0043] The third aspect of this specification provides an electronic device, including: a memory and a processor, where the processor and the memory are communicatively connected to each other, and the memory stores computer instructions, and the processor realizes the steps of the method described in the first aspect by executing the computer instructions.

[0044] The fourth aspect of this specification provides a computer-readable storage medium, where the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the method described in the first aspect are realized.

[0045] The method, device, and equipment for determining the pore adsorption capacity provided in the embodiments of this specification obtain the pore shape characteristics of the target reservoir, where the pore shape characteristics are used to characterize the shape characteristics of various pores in the target reservoir; based on the pore shape characteristics, multiple target pore structure models are established, and different pore structure models correspond to the shape characteristics of different pores; adsorption simulations are respectively performed on the multiple target pore structure models under different environments to obtain adsorption simulation data, and the adsorption simulation data includes the maximum adsorption capacity of pores under different environmental data and different pore shape characteristics; an adsorption capacity model is constructed based on the adsorption simulation data to determine the adsorption capacity of the pores in the target reservoir based on the adsorption capacity model; the adsorption capacity model is used to characterize the mapping relationship between the adsorption capacity of the pores and the environment and pore shape characteristics. The above method provided in the embodiments of this specification takes into account the influence of different pore shape characteristics on the methane adsorption capacity of the pores, constructs multiple target pore structure models corresponding to different pore shape characteristics, and respectively performs adsorption simulations on each target pore structure model. Based on the simulated data, an adsorption capacity model can be constructed to characterize the mapping relationship between the adsorption capacity of the pores and the environment and pore shape characteristics. Furthermore, based on the constructed adsorption capacity model, the accuracy and reliability of predicting the methane adsorption capacity of the pores in the target reservoir can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only some embodiments recorded in the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 Shown is a schematic diagram of the method for determining the pore adsorption capacity provided in the embodiments of this specification;

[0048] Figure 2 Shown is a schematic diagram of the method for quantitatively evaluating the methane adsorption of organic pores in continental shale provided in the embodiments of this specification;

[0049] Figure 3 Shown is a schematic diagram of the microscopic morphology of the shale sample provided in the embodiments of this specification;

[0050] Figure 4 Shown is a schematic diagram of the three-dimensional structure of the kerogen molecule provided in the embodiments of this specification;

[0051] Figure 5 Shown is a schematic diagram of the variation relationship between the density of the kerogen molecule and temperature provided in the embodiments of this specification;

[0052] Figure 6The figure shows a schematic diagram of the pore shape of the three-dimensional kerogen organic pore structure model provided by the embodiments of this specification;

[0053] Figure 7 The figure shows a schematic diagram of the absolute adsorption curves of different three-dimensional kerogen organic pore structure models provided by the embodiments of this specification under different temperature and pressure conditions;

[0054] Figure 8 The figure shows a schematic diagram of a device for determining the pore adsorption amount provided by the embodiments of this specification;

[0055] Figure 9 The figure shows a schematic diagram of an electronic device provided by the embodiments of this specification. Detailed implementation manners

[0056] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0057] As described above, the accuracy and reliability of the pore adsorption amount calculated by the existing solutions are relatively low. To solve the above problems, the embodiments of this specification provide a method for determining the pore adsorption amount. By obtaining the pore shape characteristics of the target reservoir, the pore shape characteristics are used to characterize the shape characteristics of various pores in the target reservoir; based on the pore shape characteristics, multiple target pore structure models are established, and different pore structure models correspond to different pore shape characteristics; the adsorption simulations are respectively performed on the multiple target pore structure models in different environments to obtain adsorption simulation data, and the adsorption simulation data includes the adsorption amounts of pores under different environmental data and different pore shape characteristics; an adsorption amount model is constructed based on the adsorption simulation data to determine the adsorption amount of the pores in the target reservoir based on the adsorption amount model; the adsorption amount model is used to characterize the mapping relationship between the adsorption amount of the pores and the environment and the pore shape characteristics.

[0058] The above method provided by the embodiments of this specification takes into account the influence of different pore shape characteristics on the methane adsorption amount of the pores, constructs multiple target pore structure models corresponding to different pore shape characteristics, and performs adsorption simulations on each target pore structure model respectively. Based on the simulated data, an adsorption amount model can be constructed to characterize the mapping relationship between the adsorption amount of the pores and the environment and the pore shape characteristics and the environment. Furthermore, based on the constructed adsorption amount model, the accuracy and reliability of the prediction of the adsorption amount of the pores in the target reservoir can be improved.

[0059] In the method provided by the embodiments of this specification, the execution subject of each step may be an electronic device, which refers to an electronic device with data calculation, processing, and storage capabilities. The electronic device may be a terminal such as a personal computer (PC), a tablet computer, a smart phone, a wearable device, a smart robot, etc.; it may also be a server. Among them, the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0060] First, with reference to the accompanying drawings, the method for determining the pore adsorption amount provided by the embodiments of this application will be introduced.

[0061] Figure 1 Shown is a schematic diagram of the method for determining the pore adsorption amount provided by the embodiments of this specification. Although this specification provides method operation steps or device structures as shown in the following embodiments or drawings, based on routine or non-creative labor, more or fewer operation steps or module units may be included in the method or device. In steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments of this specification or the drawings. When the method or module structure is applied to an actual device, server, or terminal product, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (for example, in an environment of parallel processors or multi-threaded processing, and even including an implementation environment of distributed processing and server clusters). As Figure 1 Shown, the method may include:

[0062] S101: Obtain the pore shape characteristics of the target reservoir, where the pore shape characteristics are used to characterize the shape characteristics of various pores in the target reservoir.

[0063] It can be understood that the pore shape characteristics can characterize the shape-related characteristics of the pores in the target reservoir, such as shape type, shape factor, etc. Exemplarily, the shape type may include types such as triangle, slit pore shape, circle, square, pentagon, etc., and the shape factor may include information such as the cross-sectional perimeter, angle, and cross-sectional area of the pore.

[0064] In some embodiments of this specification, obtaining the pore shape characteristics of the target reservoir may include: obtaining a shale sample of the target reservoir; scanning the shale sample using a scanning electron microscope to obtain a scan image of the shale sample; performing an isothermal adsorption experiment on the shale sample, and determining the pore size distribution of the shale sample based on the experimental results; based on the scan image and the pore size distribution, determining the pore shape characteristics, where the pore shape characteristics include the shape information and size information of the pores.

[0065] It can be understood that the shale sample can be a sample selected from the target reservoir, specifically a kerogen sample. After obtaining the shale sample, a scanning electron microscope (SEM) scan can be performed on the shale sample to obtain a two-dimensional scan of the kerogen in the shale sample, and the two-dimensional scan of the kerogen in the shale sample can be used to characterize the two-dimensional scan of the kerogen in the target reservoir. Moreover, an isothermal adsorption experiment can also be carried out on the shale sample, and based on the experimental results, the pore size distribution of the kerogen in the shale sample can be obtained to characterize the pore size distribution of the kerogen in the target reservoir. Further, the two-dimensional scan and the pore size distribution can be analyzed to determine the pore shape characteristics of the kerogen pores in the shale sample to characterize the pore shape characteristics of the kerogen pores in the target reservoir.

[0066] Specifically, when performing an isothermal adsorption experiment on the shale sample, CO2 adsorption can be carried out under isothermal conditions, and based on the adsorption results, a CO2 adsorption curve can be obtained. Then, further analysis and processing can be performed on the CO2 adsorption curve to obtain the pore size distribution characteristics of the kerogen in the target reservoir.

[0067] In some embodiments of this specification, when selecting the shale sample of the target reservoir, information such as the regional location and formation depth of the shale sample in the target reservoir can also be obtained to determine the geothermal gradient and geostatic gradient corresponding to the shale sample, and then the temperature and pressure of the formation where the shale sample is located can be measured. This temperature and pressure information can be used in subsequent adsorption simulation processes and / or the prediction of the adsorption amount based on the adsorption model.

[0068] S102: Based on the pore shape characteristics, establish multiple target pore structure models, and different pore structure models correspond to different pore shape characteristics.

[0069] It can be understood that the pore shape characteristics can include characteristics related to the pore shape type, shape factor, etc., and can include the shape characteristics of multiple pores. Among them, different pores can be different pore types, such as triangular pores, slit-shaped pores, circular pores, square pores, pentagonal pores, etc. Furthermore, corresponding target pore structure models can be constructed respectively for the shape characteristics of multiple pores. Among them, the pores of the multiple target pore structure models constructed are the same. By controlling the pore volume variable, the methane adsorption amounts of pores with different shapes when the pore volume is constant can be obtained, which can fully reflect the influence of pores with different shapes on the methane adsorption amount, and the prediction accuracy of the adsorption amount model for the methane adsorption amount is higher.

[0070] In some embodiments of the present specification, based on the pore shape characteristics, establishing a plurality of target pore structure models may include: constructing a periodically simulated box in the shape of a cube with a preset side length; based on the pore shape characteristics, determining the size parameters of a variety of columnar pores with the same volume, having different shapes as cross-sections and a preset side length, and constructing a plurality of pore boundaries based on the size parameters of the variety of columnar pores; and uniformly filling kerogen molecules between each pore boundary and the simulated box to construct a plurality of target pore structure models.

[0071] Specifically, through the characteristics related to the shape type and shape factor of the pores, as well as the density results of the kerogen in the actual shale, first establish a periodically bounded box in the shape of a cube. Preset the pores as columnar, with the column length equal to the side length of the cube, and the cross-section as geometric pores of different shapes. The centers of the geometric pores of different shapes coincide with the center of the cube box. Write the modeling code for the geometric boundaries of different shapes, and use the Lammps software to construct the box with periodically bounded cube. The pores of different geometric shapes coincide with the center of the box, and both the pore boundaries and the periodically bounded box are periodic. Uniformly fill kerogen molecules between each pore boundary and the simulated box to construct a plurality of kerogen models with target pore boundaries.

[0072] Among them, the kerogen molecules filled between the pore boundary and the simulated box can be three-dimensional molecules with a stable structure.

[0073] Furthermore, after molecular filling between the pore boundary and the simulated box, an initial three-dimensional molecular structure can be obtained. At this time, the energy of the three-dimensional molecular structure is not in the lowest state, and the stability of the structure is relatively low. Therefore, it is necessary to perform geometric structure optimization and simulated annealing on the model after molecular filling to obtain a model with a stable structure with minimized energy as the target pore structure model.

[0074] Specifically, uniformly filling kerogen molecules between each pore boundary and the simulated box to obtain a plurality of target pore structure models may include: uniformly filling kerogen molecules between each pore boundary and the simulated box to obtain a plurality of initial pore structure models; performing simulated geometric optimization on each initial pore model using the canonical ensemble to obtain a plurality of pore structure models in an equilibrium state; performing annealing treatment on each pore structure model in an equilibrium state using the isothermal-isobaric ensemble. Based on the relationship between the density of the kerogen periodic system in the pore model determined during the annealing process and the temperature, when it is determined that the density of the kerogen molecules satisfies the preset density threshold, stop the annealing treatment, and use the pore structure model corresponding to the last frame of the annealing treatment as the target pore structure model corresponding to each of the pore structure models in an equilibrium state.

[0075] Among them, the canonical ensemble (NVT) is the macro-canonical ensemble, which has a definite number of particles (N), volume (V), and temperature (T). The equilibrium system is a closed system, a constant-temperature system in thermal contact equilibrium with a large heat source, and the kinetic energy of the system can be kept constant by adjusting the velocities of the atoms. The constant-pressure, constant-temperature ensemble (NPT) has a definite number of particles (N), pressure (P), and temperature (T), and the pressure can be kept constant by adjusting the volume of the system. When performing structural optimization and simulated annealing, the NVT ensemble can be used to relax the initial pore structure model first. Exemplarily, the Forcite module can be used to perform molecular dynamics (MD) simulation, set the temperature to T0, where the temperature T0 is higher than the reservoir temperature T, and then a pore structure model in a certain state can be obtained. Then, the NPT system can be used to further anneal the pore structure model in the equilibrium state. Exemplarily, two simulation steps can be set. Step 1: The temperature is T1, the set pressure is the reservoir pressure P, the thermostat is selected as Nose, the barostat is selected as Berendsen, and the time step is set. Step 2: Set the temperature to decrease from T1 to T2, the pressure is the reservoir pressure, the barostat is selected as Berendsen, and the corresponding time step. After the ensemble setting is completed, by continuously decreasing the set temperature, repeat the simulation of Step 2 until approaching the formation temperature T, then repeat the simulation of Step 1 for the obtained model structure, and select the model corresponding to the lowest energy as the target pore structure model. Through the molecular simulation of the NVT ensemble and the NPT ensemble, a target pore structure model with stable structure and the lowest energy can be obtained.

[0076] S103: Perform adsorption simulations on multiple target pore structure models in different environments respectively to obtain adsorption simulation data, where the adsorption simulation data includes adsorption amounts of pores under different environmental data and different pore shape characteristics.

[0077] It can be understood that when performing adsorption simulation, the adsorption isotherm of methane by simulating different target pore structure models can be obtained, and the simulation results and simulation conditions can be used as adsorption simulation data for subsequent construction of the adsorption amount model.

[0078] In some embodiments of this specification, performing adsorption simulations on multiple target pore structure models in different environments respectively to obtain adsorption simulation data may include:

[0079] Set the force field, charge distribution method, electrostatic interaction, van der Waals interaction method, and methane fugacity of the adsorption simulation;

[0080] Perform grand canonical Monte Carlo simulations of methane adsorption on each of the target pore structure models to obtain the methane maximum adsorption capacity data corresponding to each target pore structure model under different temperature and pressure conditions;

[0081] Calculate the absolute adsorption capacity data of methane corresponding to each pore structure model based on the methane maximum adsorption capacity data corresponding to each target pore structure model. It can be understood that the above adsorption simulation process can adopt molecular simulation methods. Molecular simulation methods are important means for studying the microscopic behavior of shale, which can reveal the structure, properties and dynamic behavior of substances and provide microscopic insights for studying complex macroscopic systems. Obtaining adsorption simulation data through molecular simulation methods can provide a data basis for the construction of subsequent adsorption capacity models. Other environmental data such as humidity can also be controlled during the simulation process, which is not limited in this specification.

[0082] It can be understood that each target pore structure model may include pores corresponding to the pore shape factor. Therefore, the methane maximum adsorption capacity data corresponding to each target pore structure model obtained by calculation are the methane maximum adsorption capacity data corresponding to different pore shape factors. Furthermore, the absolute adsorption capacity data of methane corresponding to each pore structure model calculated based on the methane maximum adsorption capacity data corresponding to each target pore structure model are the absolute adsorption capacity data of methane corresponding to different pore shape factors.

[0083] Specifically, the Peng-Robinson-H equation is used to calculate the fugacity of methane. This cubic equation, which takes the improved Peng-Robinson equation as the object, can have a better fitting effect when calculating the fugacity and density of methane gas molecules. The fugacity data of methane under different temperature and pressure conditions can be calculated in advance using the equation for numerical setting before adsorption simulation based on the calculated methane fugacity data.

[0084] Specifically, the grand canonical Monte Carlo simulation method can be used for adsorption simulation during molecular simulation. Through this method, the adsorption behavior of methane molecules in the target pore structure model (such as a three-dimensional kerogen pore structure model) under different temperature and pressure conditions can be simulated, and the absolute adsorption capacity of methane in the target pore structure model (i.e., the absolute methane adsorption capacity) can be calculated based on the maximum adsorption capacity of methane in the simulation results. The absolute methane adsorption capacity, as well as the corresponding temperature, pressure, and pore shape factor of the target pore structure model, are used as adsorption simulation data to construct an absolute adsorption capacity model based on this adsorption simulation data.

[0085] Exemplarily, the grand canonical Monte Carlo (GCMC) method can be selected to simulate the methane adsorption isotherms of different organic pore shape models of shale kerogen. The module is selected as Sorption, the force field is selected as COMPASSⅡ, the charge assignment method is selected as QEq, and both the electrostatic interaction and the van der Waals interaction adopt Ewald. The chemical potential is a function of fugacity, and the fugacity of methane can be calculated using the Peng-Robinson-H equation.

[0086] Specifically, the absolute adsorption amount data can be calculated by the following formula:

[0087]

[0088] where Y A is the absolute adsorption amount data, mmol / g; Y0 is the maximum adsorption amount, mmol / g; x p is the fugacity of the gas, MPa; is the saturated gas pressure, MPa; D is a parameter related to the affinity between the adsorption matrix and the gas; G C is the pore shape factor; S is the pore cross-sectional area; C is the corresponding pore cross-sectional perimeter.

[0089] S104: Construct an adsorption amount model based on the adsorption simulation data to determine the adsorption amount of the pores of the target reservoir based on the adsorption amount model; the adsorption amount model is used to characterize the mapping relationship between the adsorption amount of the pores and the environment and pore shape characteristics.

[0090] It can be understood that when constructing an adsorption amount model based on the adsorption simulation data, the adsorption simulation data can be used as training data or input data to construct a relationship model between the environmental data, pore characteristic data and adsorption amount in the adsorption simulation data. Specifically, before constructing the model, the influencing factors on the adsorption amount in the adsorption simulation data can be quantified first, such as quantifying the temperature, pressure in the environmental data, and the pore shape characteristics. And before constructing the adsorption amount model based on the quantified adsorption simulation data, an initial model can be constructed first, which can include the selection of the model framework, the initialization of the model coefficients, etc. When constructing the model, an appropriate model framework can be selected based on the data characteristics of the environmental data, pore characteristic data and adsorption amount in the adsorption simulation data, and the coefficients of the model can be initialized. After constructing the initial model, an adsorption amount model can be constructed based on the quantified adsorption simulation data. The initial model can adopt a regression model, an artificial intelligence model, etc., and this specification does not limit this.

[0091] In some embodiments of the present specification, the constructed adsorption capacity model can be a multiple non - linear regression model. Further, constructing an adsorption capacity model based on the adsorption simulation data may include: establishing a multiple non - linear regression model of temperature, pressure, pore shape factor, and absolute methane adsorption capacity, where the pore shape factor includes the perimeter of the pore cross - section and the area of the pore cross - section; based on the temperature data, pressure data, pore shape factor data, and the corresponding absolute methane adsorption capacity data in the adsorption simulation data, fitting the multiple non - linear regression model, and calculating the value of the coefficient of determination based on the fitted model; adjusting the multiple non - linear regression model based on the value of the coefficient of determination until the value of the coefficient of determination meets the preset conditions to obtain the adsorption capacity model.

[0092] In the embodiments of the present specification, through the study of the pore adsorption mechanism, it is found that the pore shape factor (which can be characterized as an angle, for example) affects the steric hindrance effect during shale gas adsorption, making the angle have a greater impact on the methane adsorption capacity. That is, the important factor affecting the methane adsorption capacity of pores with different shapes is the angle. For example, there are differences in the angle of slit pores, circular pores, triangular pores, etc., and this difference has a greater impact on the adsorption capacity. Considering that the pore angle can be better quantified by the pore shape factor and a better fitting effect can be achieved, the shape characteristics of pores with different shapes can be quantified through the pore shape factor. And the environmental data that have a greater impact on the adsorption capacity are temperature and pressure. Therefore, based on the adsorption simulation data, constructing a multiple non - linear regression model representing the relationship between temperature, pressure, pore shape factor, and absolute methane adsorption capacity as the adsorption capacity model can improve the accuracy of calculating the pore adsorption capacity.

[0093] In some embodiments of the present specification, the coefficient of determination can be calculated by the following formula:

[0094]

[0095] where R 2 can represent the coefficient of determination, can represent the fitted value of the adsorption capacity, y can represent the actual absolute adsorption capacity, can represent the average value of the adsorption capacity.

[0096] In some embodiments of the present specification, the adsorption capacity model can be represented by the following formula:

[0097]

[0098] where Y represents the absolute methane adsorption capacity, x1 represents temperature, x2 represents pressure, x3 represents the pore shape factor, and a, b, c, e, f, τ represent coefficient constants.

[0099] ReferenceFigure 2 As shown in the figure, the embodiment of the present specification also provides a quantitative evaluation method for methane adsorption by organic pores in continental shale, including the following steps:

[0100] S1: Determine the shape and size information of organic matter in the shale sample through scanning electron microscopy and CO2 adsorption curves.

[0101] S2: Based on the pore shape and size information of the organic matter, establish three-dimensional periodic kerogen models with pore boundaries of different shapes.

[0102] Furthermore, step S2 may include the following sub-steps:

[0103] S21: According to step S1, the microscopic morphology of the shale sample can be obtained. The main morphologies of the organic pores in the shale sample can be referred to as Figure 3 shown in the figure, mainly including circular, slit, triangular, quadrilateral, and polygonal shapes. Set the side length of the square pore as the area as the pore volume as From this, calculate the side length / radius dimensions of other pore shapes so that the pore volumes are basically the same.

[0104] S22: Based on the organic pore shape and size information obtained in step S21, use Lammps software to write modeling codes with geometric boundaries of different shapes, establish pore boundaries of different shapes in the center of the periodic simulation box, and uniformly fill the space between the pore boundary and the simulation box with stable-structured kerogen molecules. The three-dimensional structure of the kerogen molecules can be as Figure 4 shown in the figure, to obtain the initial model of the kerogen organic pore structure.

[0105] S23: Relax the initial models of different kerogen organic pore structures, perform molecular dynamics (MD) simulations using the Forcite module, select the NVT ensemble, set the temperature as T0, and the temperature T0 is higher than the reservoir temperature T.

[0106] S24: Select the NPT ensemble, set the temperature as T1, set the pressure as the reservoir pressure P, select the Nose for the thermostat, select the Berendsen for the barostat, and the time step is 0.25 fs.

[0107] S25: Select the NPT ensemble, set the temperature to decrease from T1 to T2, the pressure is the reservoir pressure, select the Berendsen for the barostat, and the time step is 0.25 fs.

[0108] S26: Repeat step S25, continuously perform simulations after reducing the set temperature until it is close to the formation temperature T. At this time, the density D of the kerogen molecules in the obtained model is close to the experimentally measured true density D0 (D0 - 1 ≤ D ≤ D0 + 1), and D0 = 1.402 g / cm3 , D = 1.325 g / cm 3 , such as Figure 5 shown.

[0109] S27: Repeat step S24 for the structure obtained in step S26, select the model corresponding to the lowest energy, and finally obtain the energy-minimized three-dimensional kerogen organic pore structure model. The pore shapes include: slit pores, circular pores, triangular pores, square pores, pentagonal pores, as Figure 6 shown.

[0110] S3: Carry out molecular simulation of methane adsorption based on the three-dimensional kerogen organic pore structure model, and calculate the absolute methane adsorption amount of the three-dimensional kerogen organic pore structure model under different temperatures, pressures, and pore shapes;

[0111] Furthermore, step S3 may include the following sub-steps:

[0112] S31: Adopt the grand canonical Monte Carlo (GCMC) method to simulate the methane adsorption isotherms of different organic pore shape models of shale kerogen. Select Sorption for the module, COMPASSⅡ for the force field, QEq for the charge distribution method, and Ewald for both electrostatic interaction and van der Waals interaction. The chemical potential is a function of fugacity, and the Peng-Robinson-H equation is used to calculate the methane fugacity; as Figure 7 shown, obtain the absolute adsorption curves of different three-dimensional kerogen organic pore structure models under different temperature and pressure conditions.

[0113] S4: Quantify the effects of organic pore structure, temperature, and pressure on methane adsorption, and establish a quantitative model of methane adsorption amount and shale organic pore characteristics as the adsorption amount model.

[0114] Furthermore, step S4 may include using SPSS software to establish a multiple nonlinear regression model for temperature, pressure, organic pore size, and absolute adsorption amount. Specifically: Based on the simulation results of step S31, use the coefficient of determination R2 analysis method to perform regression fitting on temperature, pressure, pore size, and absolute adsorption amount. The value of R2 ranges from [0,1]. The closer it is to 1, the better the regression fitting effect. Continuously adjust the regression curve to make the R2 value close to 1. At this time, the regression equation is the optimal function. When fitting, the coefficient of determination R2 = 0.949. At this time, a = -0.13, b = -1.59, c = -1.82, e = -2.17, f = 4.946, τ = -1 in the aforementioned formula (3), and the final fitting equation can be expressed as:

[0115]

[0116] After obtaining the above fitting equation, the temperature, pressure, and pore shape factor of the actual reservoir can be substituted into the above formula to calculate the absolute methane adsorption amount in the pores of the reservoir, realizing the quantitative and accurate calculation of the absolute methane adsorption amount.

[0117] The above method provided by the embodiments of this specification adopts experimental characterization, molecular dynamics simulation, and numerical simulation methods to construct a shale kerogen organic pore structure model. Through the adsorption simulation of the model, a quantitative model of temperature, pressure, organic pore shape factor, and absolute adsorption amount is established, realizing the quantitative prediction of methane adsorption by shale organic pores, and can break through the limitation that experimental means cannot study the influence of a single type of pore structure on methane adsorption.

[0118] Based on the above method for determining the pore adsorption amount, one or more embodiments of this specification also provide a device for determining the pore adsorption amount. The device may include devices (including distributed systems), software (applications), modules, plugins, servers, clients, etc. that use the method described in the embodiments of this specification and are combined with the necessary implementation hardware. Based on the same innovative concept, the devices in one or more embodiments provided by the embodiments of this specification are as described in the following embodiments. Since the implementation solutions for the device to solve problems are similar to the method, the implementation of the specific device in the embodiments of this specification may refer to the implementation of the foregoing method, and the repeated parts will not be elaborated. As used hereinafter, the term "unit" or "module" may be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated. Figure 8 Shown is a schematic diagram of a device for determining the pore adsorption amount provided by an embodiment of the present application. As Figure 8 shown, the device 800 for determining the pore adsorption amount may include:

[0119] A data acquisition module 801, configured to acquire the pore shape characteristics of the target reservoir, where the pore shape characteristics are used to characterize the shape characteristics of multiple pores in the target reservoir.

[0120] A first model construction module 802, configured to establish multiple target pore structure models based on the pore shape characteristics, where different pore structure models correspond to different pore shape characteristics.

[0121] An adsorption simulation module 803, configured to perform adsorption simulations on multiple target pore structure models in different environments respectively to obtain adsorption simulation data, where the adsorption simulation data includes the adsorption amounts of pores under different environmental data and different pore shape characteristics.

[0122] The second model construction module 804 is configured to construct an adsorption amount model based on the adsorption simulation data, so as to determine the adsorption amount of the pores of the target reservoir based on the adsorption amount model; the adsorption amount model is used to characterize the mapping relationship between the adsorption amount of the pores and the environment and pore shape characteristics.

[0123] In some embodiments of the present specification, the data acquisition module 801 may specifically be configured to: acquire a shale sample of the target reservoir; scan the shale sample using a scanning electron microscope to obtain a scanned image of the shale sample; perform an isothermal adsorption experiment on the shale sample, and determine the pore size distribution of the shale sample based on the experimental results; based on the scanned image and the pore size distribution, determine the pore shape characteristics, where the pore shape characteristics include pore shape information and size information.

[0124] In some embodiments of the present specification, the first model construction module 802 may specifically be configured to: construct a cubic periodic simulation box with a preset side length; based on the pore shape characteristics, determine the size parameters of a plurality of columnar pores with the same volume, different shapes as cross-sections, and a preset side length, and construct a plurality of pore boundaries based on the size parameters of the plurality of columnar pores; uniformly fill kerogen molecules between each pore boundary and the simulation box to construct a plurality of target pore structure models.

[0125] In some embodiments of the present specification, when the first model construction module 802 uniformly fills kerogen molecules between each pore boundary and the simulation box to obtain a plurality of pore structure models, it may specifically be configured to: uniformly fill kerogen molecules between each pore boundary and the simulation box to obtain a plurality of initial pore structure models; perform simulated geometric optimization on each initial pore structure model using the canonical ensemble to obtain a plurality of pore structure models in an equilibrium state; perform annealing treatment on each pore structure model in an equilibrium state using the isothermal-isobaric ensemble, and based on the relationship between the density of the kerogen periodic system in the pore model determined from the analysis during the annealing treatment and the temperature, when it is determined that the density of the kerogen molecules satisfies a preset density threshold, stop the annealing treatment, and use the pore structure model corresponding to the last frame of the annealing treatment as the target pore structure model corresponding to each of the pore structure models in an equilibrium state.

[0126] In some embodiments of this specification, the adsorption simulation module 803 may specifically be used for: setting the force field, charge distribution method, electrostatic interaction, van der Waals interaction method, and methane fugacity of the adsorption simulation; performing grand canonical Monte Carlo simulation of methane adsorption on each of the target pore structure models to obtain the methane maximum adsorption amount data corresponding to each target pore structure model under different temperature and pressure conditions; calculating the absolute adsorption amount data of methane corresponding to each target pore structure model based on the methane maximum adsorption amount data corresponding to each target pore structure model.

[0127] In some embodiments of this specification, the absolute adsorption amount data is calculated by the following formula:

[0128]

[0129] Where Y A is the absolute adsorption amount, mmol / g; Y0 is the maximum adsorption amount, mmol / g; x p is the fugacity of the gas, MPa; is the saturated gas pressure, MPa; D is a parameter related to the affinity between the adsorption matrix and the gas; G C is the pore shape factor.

[0130] In some embodiments of this specification, when the second model construction module 804 constructs the adsorption amount model based on the adsorption simulation data, it is specifically used for: establishing a multiple nonlinear regression model of temperature, pressure, pore shape factor, and methane absolute adsorption amount, where the pore shape factor includes the pore cross-sectional perimeter and the pore cross-sectional area; fitting the multiple nonlinear regression model based on the temperature data, pressure data, pore shape factor data, and the corresponding methane absolute adsorption amount data in the adsorption simulation data, and calculating the value of the coefficient of determination based on the fitted model; adjusting the multiple nonlinear regression model based on the value of the coefficient of determination until the value of the coefficient of determination meets the preset conditions to obtain the adsorption amount model.

[0131] In some embodiments of this specification, the adsorption amount model is represented by the following formula:

[0132]

[0133] Where Y represents the methane absolute adsorption amount, x1 represents the temperature, x2 represents the pressure, x3 represents the pore shape factor, and a, b, c, e, f, τ represent coefficient constants.

[0134] The descriptions and functions of the above modules can be understood by referring to the content of the part on the determination method of pore adsorption amount, and will not be elaborated here.

[0135] This application embodiment also provides an electronic device, such asFigure 9 As shown, the electronic device may include a processor 901 and a memory 902. The processor 901 and the memory 902 may be connected by a bus or other means. Figure 9 Here, the case of connection by a bus is taken as an example.

[0136] The processor 901 may be a central processing unit (CPU). The processor 901 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above various types of chips.

[0137] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the method for determining the pore adsorption amount in the embodiments of the present invention (for example, Figure 8 the data acquisition module 801, the first model construction module 802, the adsorption simulation module 803, and the second model construction module 804 in the figure). The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, that is, implements the method for determining the pore adsorption amount in the above method embodiments.

[0138] The memory 902 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor 901, etc. In addition, the memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 902 may optionally include a memory remotely set relative to the processor 901, and these remote memories may be connected to the processor 901 through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0139] The one or more modules are stored in the memory 902 and, when executed by the processor 901, execute the following method for determining the pore adsorption amount:

[0140] Obtain the pore shape characteristics of the target reservoir, where the pore shape characteristics are used to characterize the shape characteristics of various pores in the target reservoir; based on the pore shape characteristics, establish multiple target pore structure models, and different pore structure models correspond to the shape characteristics of different pores; respectively perform adsorption simulations on multiple target pore structure models under different environments to obtain adsorption simulation data, where the adsorption simulation data includes the adsorption amounts of pores under different environmental data and different pore shape characteristics; construct an adsorption amount model based on the adsorption simulation data to determine the adsorption amount of the pores in the target reservoir based on the adsorption amount model; the adsorption amount model is used to characterize the mapping relationship between the adsorption amount of the pores and the environment and pore shape characteristics.

[0141] For the specific details of the above electronic device, reference can be made to the corresponding relevant descriptions and effects in the above method embodiments for understanding, and details will not be elaborated here.

[0142] This specification also provides a computer storage medium, which stores computer program instructions, and when the computer program instructions are executed, the steps of the above method for determining pore adsorption amount are implemented.

[0143] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (abbreviation: HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0144] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the differences between each embodiment and other embodiments are emphasized.

[0145] The systems, devices, modules, or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions.

[0146] For the convenience of description, when describing the above device, various units are described separately according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0147] As can be seen from the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this application, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of certain parts of each embodiment of this application.

[0148] This application can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0149] This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0150] Although this application has been depicted through embodiments, those of ordinary skill in the art know that this application has many variations and changes without departing from the spirit of this application. It is hoped that the appended claims will cover these variations and changes without departing from the spirit of this application.

Claims

1. A method for determining pore adsorption, characterized in that: include: Acquire pore shape characteristics of a target reservoir, wherein the pore shape characteristics are used to characterize shape characteristics of multiple pores of the target reservoir; Based on the pore shape characteristics, multiple target pore structure models are established, and different pore structure models correspond to different pore shape characteristics; Performing adsorption simulations on multiple target pore structure models under different environments to obtain adsorption simulation data, wherein the adsorption simulation data includes adsorption amounts of pores under different environmental data and different pore shape characteristics; constructing an adsorption amount model based on the adsorption simulation data to determine the adsorption amount of the pores of the target reservoir based on the adsorption amount model; The adsorption amount model is used to characterize the mapping relationship between the adsorption amount of the pores and the environment and pore shape characteristics.

2. The method for determining pore adsorption according to claim 1, characterized in that: Obtain the pore shape characteristics of the target reservoir, including: Obtaining a shale sample from the target reservoir; Scanning the shale sample using a scanning electron microscope, and obtaining a scanned image of the shale sample; Performing an isothermal adsorption experiment on the shale sample, and determining the pore size distribution of the shale sample based on the experimental results; Based on the scan image and the pore size distribution, the pore shape characteristics are determined, and the pore shape characteristics include shape information and size information of the pores.

3. The method for determining pore adsorption according to claim 1, characterized in that: Based on the pore shape characteristics, multiple target pore structure models are established, including: Construct a periodic simulation box with a cube whose side length is a preset length; Based on the pore shape characteristics, determining size parameters of multiple columnar pores with the same volume and having a preset length as the side length and different shapes as the cross-section, and constructing multiple pore boundaries based on the size parameters of the multiple columnar pores; Kerogen molecules are uniformly filled between each pore boundary and the simulation box to construct multiple target pore structure models.

4. The method for determining pore adsorption according to claim 3, characterized in that: Kerogen molecules are uniformly filled between each pore boundary and the simulation box to construct multiple target pore structure models, including: Kerogen molecules are uniformly filled between each pore boundary and the simulation box to obtain multiple initial pore structure models; The canonical ensemble is used to simulate the geometry optimization of each initial pore structure model, and multiple pore structure models in equilibrium are obtained; An isothermal and isobaric ensemble is used to perform annealing treatment on each pore structure model in equilibrium. Based on the relationship between the density and temperature of the kerogen periodic system in the pore model determined by analysis during the annealing process, the annealing treatment is stopped when it is determined that the kerogen molecular density meets the preset density threshold, and the pore structure model corresponding to the last frame of the annealing treatment is used as the target pore structure model corresponding to each pore structure model in equilibrium.

5. The method for determining pore adsorption according to claim 1, characterized in that: Adsorption simulations were performed on multiple target pore structure models under different environments to obtain adsorption simulation data, including: Set the force field, charge distribution method, electrostatic interaction, van der Waals interaction method and methane fugacity for adsorption simulation; Performing a grand canonical Monte Carlo simulation of methane adsorption on each target pore structure model to obtain maximum methane adsorption data corresponding to each target pore structure model under different temperature and pressure conditions; Based on the maximum methane adsorption data corresponding to each target pore structure model, the absolute methane adsorption data corresponding to each target pore structure model is calculated.

6. The method for determining pore adsorption according to claim 5, characterized in that: The absolute adsorption data is calculated by the following formula: Among them, Y A is the absolute adsorption data, mmol / g; Y0 is the maximum adsorption, mmol / g; x p is the fugacity of the gas, MPa; is the saturated gas pressure, MPa; D is the parameter related to the affinity between the adsorption matrix and the gas; G C is the pore shape factor.

7. The method for determining pore adsorption according to claim 1 or 5, characterized in that: Constructing an adsorption amount model based on the adsorption simulation data includes: Establishing a multivariate nonlinear regression model of temperature, pressure, pore shape factor and adsorption amount, wherein the pore shape factor includes the pore cross-sectional perimeter and the pore cross-sectional area; Fitting the multivariate nonlinear regression model based on the temperature data, pressure data, pore shape factor data and corresponding methane absolute adsorption data in the adsorption simulation data, and calculating the value of the determination coefficient based on the fitted model; The multivariate nonlinear regression model is adjusted based on the value of the determination coefficient until the value of the determination coefficient meets the preset conditions, thereby obtaining the adsorption amount model.

8. The method for determining pore adsorption according to claim 1, characterized in that: The adsorption capacity model is expressed by the following formula: Where Y represents the absolute amount of methane adsorption, x1 represents temperature, x2 represents pressure, x3 represents pore shape factor, and a, b, c, e, f, and τ represent coefficient constants.

9. A device for determining pore adsorption, characterized in that: include: A data acquisition module, used for acquiring pore shape characteristics of a target reservoir, wherein the pore shape characteristics are used for characterizing shape characteristics of multiple pores of the target reservoir; A first model building module is used to establish multiple target pore structure models based on the pore shape characteristics, and different pore structure models correspond to different pore shape characteristics; An adsorption simulation module is used to perform adsorption simulation on multiple target pore structure models under different environments to obtain adsorption simulation data, wherein the adsorption simulation data includes the adsorption amount of pores under different environmental data and different pore shape characteristics; A second model building module, used to build an adsorption amount model based on the adsorption simulation data, so as to determine the adsorption amount of the pores of the target reservoir based on the adsorption amount model; The adsorption amount model is used to characterize the mapping relationship between the adsorption amount of the pores and the environment and pore shape characteristics.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor implements the steps of the method according to any one of claims 1 to 8 by executing the computer instructions.

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