A deep energy reservoir multi-scale thermal-flow-solid coupling simulation method and system

Abstract

The application provides a deep energy reservoir multi-scale heat-flow-solid coupling simulation method and system, and belongs to the deep energy reservoir numerical simulation field. Spatial distribution characteristics and physical property parameters of different lithological reservoir media at a micro and mesoscopic scale are acquired, and a digital structure model of the reservoir medium is constructed. A micro and mesoscopic numerical model is obtained by embedding micro and mesoscopic action mechanisms and flow mechanisms in the model. Model structure parameters and physical property parameters under different heat-flow-solid coupling conditions are solved, and a mapping relationship is established. Based on the mapping relationship, macroscopic geological and engineering data of a target reservoir are combined to establish a macro-scale heat-flow-solid multi-field coupling control equation, and the equation is solved to output a multi-physical field response of the target reservoir under a given working condition. The application realizes dynamic transmission of physical property parameters from the micro and mesoscopic scale to the macroscopic scale and fine simulation of a multi-field coupling evolution process, and significantly improves the accuracy and reliability of deep complex reservoir engineering behavior prediction.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation technology for deep energy reservoirs, and particularly relates to a multi-scale thermal-fluid-solid coupling simulation method and system for deep energy reservoirs. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Deep energy reservoirs (including deep geothermal reservoirs, deep oil and gas reservoirs, and deep underground gas storage facilities) are typically situated in complex geological environments characterized by high temperature, high pressure, and high geostress. Their internal media exhibit strong heterogeneity and distinct multi-scale features. During the development and utilization of deep energy reservoirs, temperature, seepage, and stress fields are coupled and dynamically evolve. This complex interaction of multiple scales and physical fields profoundly affects fluid transport, reservoir deformation, and the transfer and extraction of thermal energy, posing significant challenges to optimizing development plans, improving engineering efficiency, and controlling safety risks. Therefore, establishing numerical models capable of accurately characterizing the multi-scale thermal-fluid-solid coupling evolution of deep reservoirs is an indispensable key technological foundation for the efficient development and utilization of deep underground energy.

[0004] Currently, numerical simulation methods for such complex problems still have significant shortcomings. First, in terms of model construction philosophy, most macroscopic-scale models are based on the assumption of continuous medium and use homogenized or equivalent average parameters to describe reservoirs. This simplification cannot reflect the true spatial distribution of mineral composition, pore-throat structure, and fracture network at the microscopic scale, as well as their control over macroscopic thermal, mechanical, and seepage properties. As a result, the reliability and accuracy of these models in predicting reservoir behavior under extreme deep conditions are fundamentally questionable.

[0005] Secondly, in terms of cross-scale correlation, existing upscaling methods often assume that the size of micro-features is much smaller than that of macro-scales, which artificially separates physical processes between different scales. Microscopic information is oversimplified or even lost during transmission, making it difficult to achieve effective bottom-up transmission of physical mechanisms.

[0006] Finally, there is a particular lack of comprehensive consideration of multi-physics coupling mechanisms. Existing studies either focus on characterizing multi-scale structures while weakening the coupling of the whole field, or only pay attention to the effects of a few fields such as fluid-structure interaction, and generally lack a systematic integration of the key effects of temperature field under high temperature and high pressure environment. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a multi-scale thermo-fluid-solid coupling simulation method and system for deep energy reservoirs. It comprehensively considers the multi-scale structural characteristics, strong heterogeneity, and multi-physics coupling effects of deep energy reservoirs, and realizes the effective transfer of microscopic information to macroscopic simulation. This enables a refined description of the coupling evolution process of temperature field, seepage field, and stress field in deep energy reservoirs, providing reliable technical support for the development and utilization of deep energy reservoirs.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] The first aspect of this invention provides a multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs;

[0010] A multi-scale thermo-fluid-solid coupled simulation method for deep energy reservoirs includes:

[0011] To obtain the spatial distribution characteristics and physical property parameters of different structural components in reservoir media with different lithologies at the micro-scale, and to construct a digital structural model of the reservoir media;

[0012] In the constructed digital structural model, a microscopic numerical model of the reservoir medium is obtained by embedding microscopic action mechanisms and flow mechanisms; by solving the model structural parameters and physical property parameters of the microscopic numerical model under different thermo-fluid-solid coupling conditions, a mapping relationship between the environmental conditions of the reservoir medium and its structural and physical property parameters is established.

[0013] Based on the mapping relationship, and combined with the macroscopic geological and engineering data of the target reservoir, a macroscopic-scale thermal-fluid-solid multi-field coupled control equation is established and solved, and the multi-physics response of the target reservoir under given operating conditions is output.

[0014] As a further technical solution, the spatial distribution characteristics are used to describe the spatial positional relationship, geometric morphology and connectivity characteristics of different mineral components and pore fracture structures at the microscale, so as to characterize the microscale heterogeneous structure characteristics of deep reservoir media.

[0015] The physical properties parameters are used to characterize the thermal and mechanical physical properties of the corresponding mineral components, including thermal conductivity, specific heat capacity, elastic modulus and Poisson's ratio.

[0016] As a further technical solution, the digital structure model is a three-dimensional model reconstructed based on the microstructure characterization results, using a combination of structure mapping and Monte Carlo methods.

[0017] As a further technical solution, the microscopic numerical model is a pore network model, which includes a pore network composed of pore nodes and pore throats, and a fracture network composed of connecting nodes and fracture elements and connected to the pore network through coupling nodes.

[0018] The microscopic mechanisms include thermal-fluid coupling, fluid-structure coupling, and thermal-structure coupling mechanisms. The coupling process is bidirectional and dynamically updated with changes in temperature, pressure, or stress state. The flow mechanisms include one or more of diffusion flow, slip flow, Knudsen flow, and surface diffusion mechanisms, and the corresponding flow mechanism is adaptively selected according to the pore size characteristics and fluid properties.

[0019] As a further technical solution, the mapping relationship between the environmental conditions of the reservoir medium and its structural and physical property parameters is established by constructing a convolutional neural network and an adversarial neural network.

[0020] The input parameters for the mapping relationship include temperature, confining pressure, and injection / production pressure, while the output equivalent parameters are porosity, thermal conductivity, specific heat capacity, permeability, elastic modulus, and Poisson's ratio.

[0021] As a further technical solution, based on the aforementioned mapping relationship and combined with the macroscopic geological and engineering data of the target reservoir, a macroscopic-scale thermo-fluid-solid multi-field coupled control equation is established and solved, outputting the multiphysics response of the target reservoir under given operating conditions, including:

[0022] Based on the geological data and engineering data of the target deep energy reservoir, establish a macroscopic geometric model;

[0023] The mapping relationship is imported into a macroscopic-scale geometric model to establish macroscopic-scale thermo-fluid-solid multi-field coupling control equations.

[0024] For the macroscopic-scale thermo-fluid-solid multi-field coupled control equations, initial and boundary conditions for the temperature field, seepage field, and stress field are set respectively, and multi-physics field coupling is performed to establish a macroscopic-scale thermo-fluid-solid multi-field coupled numerical model.

[0025] The macroscopic thermal-fluid-solid multi-field coupled numerical model is discretized and solved using the finite element method, and the temperature field, seepage field and stress field of the macroscopic reservoir under given operating conditions are output.

[0026] As a further technical solution, the macroscopic-scale thermo-fluid-solid multi-field coupled numerical model is discretized and solved using the finite element method, including:

[0027] The macroscopic geometric model is divided into a finite element mesh, and adaptive mesh refinement is used to ensure that the model can balance computational efficiency and simulation accuracy.

[0028] Implicit time integration is used to perform step calculations on the temperature field, seepage field, and stress field to ensure numerical stability and adapt to the time scale differences of different physical fields.

[0029] Within each time step, the temperature field, seepage field and stress field are fully coupled and solved using the Newton iteration method until the residuals of each physical field are lower than the preset threshold and the preset convergence condition is met.

[0030] After iterative convergence, the equivalent physical property parameters and multi-field coupling response of each macroscopic model unit are calculated and used to update the boundary conditions and model parameters of the next time step. Finally, the multiphysics simulation results of the target deep reservoir under given conditions are output.

[0031] A second aspect of the present invention provides a multi-scale thermal-fluid-solid coupling simulation system for deep energy reservoirs.

[0032] A multi-scale thermal-fluid-solid coupled simulation system for deep energy reservoirs includes:

[0033] The microscale parameter acquisition and geometric modeling module is used to acquire the spatial distribution characteristics and corresponding physical property parameters of different structural components at the microscale of reservoir media of different lithologies, and to construct a digital structural model of the reservoir media.

[0034] The microscale modeling and cross-scale mapping module is used to construct a microscale numerical model of reservoir medium embedding multiple microscale action mechanisms; establish and solve the microscale thermal-fluid-solid multi-field coupled control equations on the microscale numerical model; and construct the mapping relationship between the environmental conditions and structural and physical property parameters of the reservoir medium.

[0035] The macro-scale parameter acquisition and geometric modeling module is used to establish a macro-scale geometric model based on the geological data and engineering data of the target deep reservoir.

[0036] The multiphysics coupling model construction module is used to import the reservoir medium property parameter library obtained under different environmental conditions into the macroscopic-scale geometric model and establish the macroscopic-scale thermal-fluid-solid multi-field coupling control equations;

[0037] The initial boundary condition and multiphysics coupling setting module is used to set initial and boundary conditions for the temperature field, seepage field and stress field in the macroscopic thermal-fluid-solid multi-field coupling model, respectively, and to complete the multiphysics coupling configuration and establish a macroscopic thermal-fluid-solid multi-field coupling numerical model.

[0038] The numerical solution and result output module is used to perform discrete Newton iteration solution on the macroscopic thermal-fluid-solid multi-field coupled numerical model using the finite element method, and output the multiphysics simulation results of the target deep reservoir under given working conditions.

[0039] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in a multi-scale thermal-fluid-structure coupling simulation method for deep energy reservoirs as described in the first aspect of the present invention.

[0040] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in a multi-scale thermo-fluid-structure interaction simulation method for deep energy reservoirs as described in the first aspect of the present invention.

[0041] The above one or more technical solutions have the following beneficial effects:

[0042] (1) This invention proposes a multi-scale thermal-fluid-solid coupling numerical model for deep energy reservoirs, which can be used for deep-earth engineering simulation analysis under complex environments of high temperature, high pressure and high stress. This model breaks through the limitations of the macroscopic continuous medium and homogenization assumptions of traditional models, overcomes the problem of insufficient multi-scale multi-physics coupling in the existing technology, realizes the effective transmission of microscopic information of reservoir medium to macroscopic scale and thermal-fluid-solid coupling analysis that is more in line with the actual complex working conditions, so as to accurately predict the multi-scale multi-physics response of reservoirs under complex environments, and finely characterize the multi-field dynamic coupling evolution law of temperature field, seepage field and stress field in deep reservoirs, providing reliable technical support and decision-making basis for the development and utilization of deep energy reservoirs.

[0043] (2) This invention proposes an upscaling method, which accurately solves the micro-scale multi-field coupling response of reservoir medium under thermal-fluid-solid multi-field coupling by fine modeling of reservoir medium at the micro-scale. The mapping relationship between the environmental conditions of reservoir medium and its equivalent structural parameters and equivalent physical property parameters is established by convolutional neural network and adversarial neural network. The obtained equivalent parameter mapping relationship is input into the constructed macro-scale model of deep energy reservoir, thereby realizing the cross-scale transmission of micro-scale information of deep energy reservoir medium. This enables the constructed multi-scale model to accurately reflect the nonlinear evolution characteristics of reservoir physical properties under complex temperature, pressure and stress state changes. It breaks through the technical bottleneck of existing upscaling methods, which cannot accurately reflect the micro-scale action mechanism of reservoir medium under complex multi-field coupling conditions due to oversimplification, thus causing the model prediction results to fail.

[0044] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0045] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0046] Figure 1 This is a flowchart of the method in the first embodiment.

[0047] Figure 2 A flowchart for iterative solution of a macroscopic thermal-fluid-structure interaction model is provided for an embodiment of the present invention.

[0048] Figure 3 This is a multi-scale thermal-fluid-solid coupling diagram of a deep energy reservoir provided in an embodiment of the present invention.

[0049] Figure 4 This is a system structure diagram of the second embodiment. Detailed Implementation

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0052] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0053] Example 1

[0054] This embodiment discloses a multi-scale thermo-fluid-solid coupled simulation method for deep energy reservoirs. By constructing a microscopic digital structure model and a pore network model of the reservoir medium, a cross-scale mapping relationship between environmental conditions and equivalent macroscopic physical property parameters is established, thereby driving the establishment and solution of a macroscopic scale thermo-fluid-solid fully coupled numerical model. This invention achieves refined simulation of the dynamic transfer of physical property parameters from the microscopic to the macroscopic level and the multi-field coupled evolution process, significantly improving the accuracy and reliability of predicting the engineering behavior of deep and complex reservoirs.

[0055] Specifically, such as Figure 1 As shown, a multi-scale thermo-fluid-solid coupled simulation method for deep energy reservoirs includes:

[0056] Step S1: Obtain the spatial distribution characteristics and physical property parameters of different structural components in reservoir media of different lithologies at the micro-scale, and construct a digital structural model of the reservoir media.

[0057] In this embodiment, the spatial distribution characteristics are used to describe the spatial positional relationship, geometric morphology, and connectivity characteristics of different mineral components and pore / fracture structures at the microscopic scale, so as to characterize the microscopic heterogeneous structural characteristics of deep reservoir media; the physical property parameters are used to characterize the thermal and mechanical physical properties of the corresponding mineral components, including thermal conductivity, specific heat capacity, elastic modulus, and Poisson's ratio; the pore / fracture structure characteristic parameters include pore radius, pore throat radius, pore throat length, fracture length, aperture, orientation, and density.

[0058] The spatial distribution characteristics of different structural components at the microscale are obtained through material microscale characterization techniques such as X-CT scanning, scanning electron microscopy, X-ray diffraction, EDS energy dispersive spectroscopy, and electron backscatter diffraction, achieving fine characterization of different mineral components and pore and fracture structures in reservoir rocks. The physical property parameters are obtained through microscale mechanical tests such as nanoindentation, in-situ SEM / TEM fatigue testing, X-ray diffraction, and electron backscatter diffraction, as well as microscale thermodynamic experiments such as microcalorimetry, infrared imaging technology, and laser flash irradiation, achieving accurate characterization of the physical property parameters of different mineral components in reservoir rocks.

[0059] The digital structure model of the reservoir medium is constructed based on the microstructure characterization results, and the microstructure three-dimensional model is reconstructed by combining structure mapping and Monte Carlo methods. Specifically:

[0060] First of all, X-CT The scanned 3D grayscale image undergoes denoising filtering, employing nonlocal mean or anisotropic diffusion filtering algorithms to suppress noise while preserving pore boundary details. Subsequently, an adaptive thresholding method is used to convert the image into a binary image, where the pixel value is... 1 "Represents the porous phase," 0 "Represents a solid matrix; continuously stacked two-dimensional slices are reconstructed to form a three-dimensional pore space distribution model; finally, image analysis is performed using the refined central axis method and the maximum sphere algorithm to quantitatively extract structural parameters such as pore equivalent radius, pore throat radius, pore throat length, spatial coordinates, and connectivity topology network;

[0061] Will SEM The obtained three-dimensional microscopic images and X-CT The obtained 3D volume data is spatially aligned using an image registration algorithm to establish a unified spatial coordinate system. This image registration algorithm includes rigid or non-rigid registration to eliminate differences in scale, position, and deformation caused by different imaging methods. EDS Energy spectroscopy analysis results and X X-ray diffraction phase quantitative analysis results show that different mineral components are in X-CT The distribution characteristics in the grayscale space are calibrated to determine the typical grayscale intervals corresponding to various mineral components. Based on this, a mineral phase indicator function is constructed. To characterize the distribution of different mineral components in microscopic space, it can be expressed as:

[0062]

[0063] in, for X-CT Image in space x The grayscale value at that location.

[0064] Based on the aforementioned mineral phase indicator function, key statistics for random construction are extracted, and the volume fractions of different structural components are statistically analyzed:

[0065]

[0066] in, Let be the volume fraction of the i-th type of mineral within the computational domain; For volume.

[0067] Furthermore, a two-point spatial correlation function is introduced to characterize structural connectivity:

[0068]

[0069] in, For the first i Spatial two-point correlation function for mineral-like organisms; It is a spatial displacement vector.

[0070] Under the aforementioned statistical characteristic constraints, a Monte Carlo random sampling algorithm is used to generate pore and crack geometric elements in the three-dimensional computational domain. The geometric parameters are obtained by random sampling of the corresponding probability distribution function. Through multiple random generation and iterative updates, the reconstructed structure satisfies the constraints of volume fraction, geometric distribution, and spatial correlation. The objective function is defined as follows:

[0071]

[0072] in, This is the total error function; For the first i Volume fraction of mineral-like structures in simulated digital structures; For the first i The true volume fraction of the minerals measured in the original experimental data; For the first i In the simulated structure, the mineral-like structure is located at a distance Spatial correlation function values ​​between two points at; For the first i In the original experimental data, the mineral-like structures were at the same distance. The spatial correlation function values ​​of the two points are obtained; when the objective function converges to the preset threshold, the construction of the microscopic three-dimensional digital structure model is completed.

[0073] The obtained physical property parameters of different mineral components in the reservoir rocks are mapped to the corresponding regions in the digital structural model to construct a spatially heterogeneous physical property parameter field; any physical property parameter P The spatial distribution can be represented as:

[0074]

[0075] in, Indicates the first i Physical property parameters corresponding to structural components.

[0076] Step S2: In the constructed digital structural model, a microscopic numerical model of the reservoir medium is obtained by embedding microscopic action mechanisms and flow mechanisms; by solving the model structural parameters and physical property parameters of the microscopic numerical model under different thermal-fluid-solid coupling conditions, a mapping relationship between the environmental conditions and structural and physical property parameters of the reservoir medium is established.

[0077] Specifically, in the process of constructing the numerical model of the porous network, the first step is to perform skeletonization processing based on the digital structure model described in step S1, using... 3D The refinement algorithm extracts the centerline skeleton of the pore space;

[0078] Based on the skeleton structure, the maximum sphere algorithm or rolling sphere method is used to identify discrete pores and throats of connected pores. For each node of the skeleton, a maximum inscribed sphere is fitted with that node as the center, and the center of the sphere is defined as the location of the pore node. x p ,radius r p Defined as the equivalent pore radius, then for the skeleton segment connecting two adjacent pore nodes, roll a minimum radius sphere along the path, its minimum radius... r t Defined as throat radius, path length l t The length of the throat is recorded; the volume of each pore is also recorded. V p Surface area A p and coordination number z Therefore, it is possible to construct a system based on... N p pore nodes and N t Discrete network topology diagram composed of throat channels G= ( V,E This yields a porous network model. The vertex set... Represents pores, edge set It refers to the throat.

[0079] Based on the aforementioned pore network model, governing equations reflecting various microscopic mechanisms and flow mechanisms are introduced at pore nodes and throat units. Mass conservation equations, momentum conservation equations, and energy conservation equations are established at each pore node, and coupling is achieved between adjacent pore nodes through throat transport terms. The conservation equations are spatially and variableally discretized to construct a set of linear or nonlinear algebraic equations with pore node state variables as unknowns. The overall linear equation matrix is ​​obtained through linearization, thereby forming a numerical model of the microscopic pore network of the reservoir medium.

[0080] In this embodiment, the various microscopic mechanisms include thermal-fluid coupling, fluid-structure coupling, and thermal-structure coupling mechanisms. The coupling process is bidirectional and dynamically updated with changes in temperature, pressure, or stress state. The flow mechanisms include one or more of diffusion flow, slip flow, Knudsen flow, and surface diffusion mechanisms, and the corresponding flow mechanism is adaptively selected according to the pore size characteristics and fluid properties.

[0081] The micro-flow mechanism and the characteristic length of the micro-pore channels ( ) and molecular mean free path ( Closely related to this, it is characterized by the Knudsen number (Kn), defined as:

[0082]

[0083] Wherein, the molecular mean free path ( Based on dynamic calculations using reservoir temperature and seepage field pressure, the specific expression is as follows:

[0084]

[0085] in, k B Boltzmann's constant, T For reservoir temperature, d m The effective diameter of the fluid molecule. p The pressure is the pressure in the seepage field.

[0086] The model's structural and physical property parameters are used to equivalently characterize the structural, thermal, permeability, and mechanical physical properties of reservoir media of different lithologies, including porosity, thermal conductivity, specific heat capacity, permeability, elastic modulus, and Poisson's ratio.

[0087] The mapping relationship between the environmental conditions and structural and physical properties of the reservoir medium is established by constructing a convolutional neural network and an adversarial neural network. The trained model can efficiently predict macroscopic thermal-fluid-solid coupling parameters and dynamically update them with changes in temperature, pressure and stress conditions, realizing the adaptive transfer of equivalent parameters across scales. The input parameters of the mapping relationship include temperature, confining pressure and injection-production pressure, and the output equivalent parameters are porosity, thermal conductivity, specific heat capacity, permeability, elastic modulus and Poisson's ratio.

[0088] Step S3: Based on the mapping relationship, and combined with the macroscopic geological and engineering data of the target reservoir, establish and solve the macroscopic-scale thermal-fluid-solid multi-field coupled control equations, and output the multi-physics response of the target reservoir under given operating conditions.

[0089] Step S31: Based on the geological data and engineering data of the target deep energy reservoir, obtain the basic parameters related to the formation and fluids of the target deep reservoir area required for macro-scale simulation, including macro-scale reservoir burial depth, fracture density and fracture geometry, fluid density, fluid viscosity, specific heat capacity, thermal conductivity, compressibility, etc., and establish a macro-scale geometric model.

[0090] Step S32: Import the obtained reservoir medium property parameter library under different environmental conditions into the macroscopic-scale geometric model to establish macroscopic-scale thermal-fluid-solid multi-field coupled control equations;

[0091] Step S33: For the macroscopic-scale thermal-fluid-solid multi-field coupling control equation, set the initial conditions and boundary conditions for the temperature field, seepage field and stress field respectively, and perform multi-physics field coupling settings to establish a macroscopic-scale thermal-fluid-solid multi-field coupling numerical model.

[0092] Step S34: The macroscopic thermal-fluid-solid multi-field coupled numerical model is discretized and solved by Newton iteration using the finite element method, and the temperature field, seepage field and stress field of the macroscopic reservoir under given operating conditions are output, providing a decision basis for the development and utilization of the reservoir.

[0093] In this embodiment, the macroscopic-scale thermal-fluid-solid multi-field coupling control equations include the heat conduction control equation, the fluid motion equation, and the mechanical equilibrium equation.

[0094] Furthermore, in this embodiment, the heat conduction control equation is based on the law of conservation of energy, describing the spatiotemporal evolution of the temperature field in the macroscopic reservoir medium, considering the thermal conductivity effects of the matrix and fluid, as well as the convective heat transfer caused by fluid flow, and its expression is:

[0095]

[0096] in, T For reservoir temperature, For equivalent volumetric heat capacity, For fluid density, is the specific heat capacity of the fluid. Equivalent thermal conductivity As the overall equivalent heat source, This is the fluid velocity vector.

[0097] In this embodiment, the effective volumetric heat capacity and effective thermal conductivity are defined as follows:

[0098]

[0099]

[0100]

[0101] in, For equivalent porosity, For solid density, Specific heat capacity of a solid. and These represent the thermal conductivity of solids and the thermal conductivity of fluids, respectively. and These are represented as heat sources for solids and heat sources for fluids, respectively.

[0102] In this embodiment, the fluid motion equation is based on the law of conservation of mass and describes the seepage behavior of macroscopic fluid in the reservoir medium. Its expression is:

[0103]

[0104] in, For fluid source and sink terms.

[0105] In this embodiment, the fluid velocity vector is defined as:

[0106]

[0107] in, For equivalent penetration rate, p For seepage field pressure, Let be the dynamic viscosity of the gas.

[0108] In this embodiment, the mechanical equilibrium equation is based on the law of conservation of momentum in continuum mechanics and is used to describe the deformation and stress evolution process of the reservoir under the combined action of geostress, pore pressure and temperature changes.

[0109] In this embodiment, the strain-displacement relationship of the reservoir medium is described based on Cauchy's theorem, and can be expressed as:

[0110]

[0111] in, Let be the components of the total strain tensor, and (i=x, y, z) represents the displacement component in the i-th direction.

[0112] In this embodiment, the equilibrium equation satisfies the force equilibrium condition of the continuous medium under the action of body force and surface force, which can be expressed as the equilibrium relationship between the stress tensor divergence and the body force term, as follows:

[0113]

[0114] in, For Cauchy stress tensor, Let i be the volume force in the direction of i (i = x, y, z).

[0115] In this embodiment, the strain-displacement relationship of the reservoir medium can be corrected by introducing additional strain induced by various physical fields to reflect the combined effects of adsorption, temperature changes, and pore pressure changes on the reservoir deformation behavior; the stress-strain relationship can be further expressed as:

[0116]

[0117] in, For the equivalent elastic stiffness tensor, For the total strain tensor, , and These represent the adsorption strain caused by fluid adsorption-desorption, the thermal expansion strain caused by temperature changes, and the strain induced by fluid pressure changes, respectively.

[0118] In some embodiments, the thermal expansion strain is an isotropic volumetric strain, the value of which is determined by the equivalent thermal expansion coefficient and the temperature change, and can be expressed as:

[0119]

[0120] in, The coefficient of thermal expansion is This represents the initial temperature of the reservoir.

[0121] In one embodiment, the pressure-induced strain can be further described by introducing the effective stress principle, and can be expressed as:

[0122]

[0123] in, This is the Biot coefficient.

[0124] Preferably, based on Cauchy's law, equilibrium equations, and various strains, the expression for the mechanical equilibrium equations can be derived as follows:

[0125]

[0126] in, This is the shear stress term; V is the shear modulus; v is Poisson's ratio; For the displacement divergence in the th... i Gradient of direction; Biot coefficient; For pore fluid pressure (in i The role of direction); Bulk modulus; The adsorption strain caused by fluid adsorption-desorption in the i-direction (i=x, y, z); The thermal expansion strain caused by temperature change in the i-direction (i=x, y, z); Let i be the volume force in the direction of i (i = x, y, z).

[0127] Furthermore, in step S34, as Figure 2 As shown, the steps for discretizing and iteratively solving the macroscopic-scale thermo-fluid-solid multi-field coupled numerical model using the finite element method include the following:

[0128] (1) Divide the macroscopic geometric model of the target deep energy reservoir into a finite element mesh, and ensure that the model can balance computational efficiency and simulation accuracy through adaptive mesh refinement; the finite element mesh type may include tetrahedron, hexahedron or other finite element types suitable for reservoir geometry complexity;

[0129] (2) Discretize the entire simulation time into several time steps, and perform implicit time integration step calculations on the temperature field, seepage field and stress field in each time step to ensure numerical stability and adapt to the time scale differences of different physical fields. The solution time step can be adaptively adjusted according to the numerical stability conditions and reservoir response characteristics;

[0130] (3) Within each time step, perform an iterative solution loop;

[0131] (4) After the iteration converges, the equivalent physical property parameters and multi-field coupling response of each macroscopic unit are calculated and used for the boundary conditions and parameter updates of the next time step, so as to realize the continuous transmission of macroscopic multi-physics coupling information. Finally, the temperature distribution, pressure distribution and stress field of the reservoir under given operating conditions are output to guide the decision-making and optimization of reservoir development schemes.

[0132] In this embodiment, step (3) of the iterative solution loop mainly includes the following steps:

[0133] 1) The temperature field, seepage field, and stress field are solved in a fully coupled manner using the Newton-Raphson iteration method;

[0134] 2) In each iteration, the equivalent physical property parameters of the macroscopic units are dynamically updated to reflect the influence of changes in temperature, pressure, and stress state on reservoir properties;

[0135] 3) Determine whether the residuals of each physical field are lower than the preset threshold. If they are, exit the iteration step and proceed to the next time step; otherwise, continue iterating until convergence.

[0136] 4) After each time step converges, calculate the equivalent physical property parameters and multi-field coupling response of each macroscopic unit, and use them for the boundary conditions and parameter updates of the next time step to realize the continuous transmission of macroscopic multi-physics coupling information. Finally, output the temperature distribution, pressure distribution and stress distribution of the reservoir under given operating conditions to guide the decision-making and optimization of reservoir development schemes.

[0137] In this embodiment, the finite element grid can be a structured or unstructured grid to adapt to reservoirs with different geological complexities.

[0138] Example 2

[0139] This embodiment discloses a multi-scale thermal-fluid-structure interaction simulation system for deep energy reservoirs;

[0140] like Figure 4 As shown, a multi-scale thermal-fluid-solid coupling simulation system for deep energy reservoirs includes:

[0141] The microscale parameter acquisition and geometric modeling module is used to acquire the spatial distribution characteristics and corresponding physical property parameters of different structural components at the microscale of reservoir media of different lithologies, and to construct a digital structural model of the reservoir media.

[0142] The microscale modeling and cross-scale mapping module is used to construct a microscale numerical model of reservoir medium embedding multiple microscale action mechanisms; establish and solve the microscale thermal-fluid-solid multi-field coupled control equations on the microscale numerical model; and construct the mapping relationship between the environmental conditions and structural and physical property parameters of the reservoir medium.

[0143] The macro-scale parameter acquisition and geometric modeling module is used to establish a macro-scale geometric model based on the geological data and engineering data of the target deep reservoir.

[0144] The multiphysics coupling model construction module is used to import the reservoir medium property parameter library obtained under different environmental conditions into the macroscopic-scale geometric model and establish the macroscopic-scale thermal-fluid-solid multi-field coupling control equations;

[0145] The initial boundary condition and multiphysics coupling setting module is used to set initial and boundary conditions for the temperature field, seepage field and stress field in the macroscopic thermal-fluid-solid multi-field coupling model, respectively, and to complete the multiphysics coupling configuration and establish a macroscopic thermal-fluid-solid multi-field coupling numerical model.

[0146] The numerical solution and result output module is used to perform discrete Newton iteration solution on the macroscopic thermal-fluid-solid multi-field coupled numerical model using the finite element method, and output the multiphysics simulation results of the target deep reservoir under given working conditions.

[0147] Example 3

[0148] The purpose of this embodiment is to provide a computer-readable storage medium.

[0149] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in Example 1.

[0150] Example 4

[0151] The purpose of this embodiment is to provide an electronic device.

[0152] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in a multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in Example 1.

[0153] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0154] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0155] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs, characterized in that, include: To obtain the spatial distribution characteristics and physical property parameters of different structural components in reservoir media with different lithologies at the micro-scale, and to construct a digital structural model of the reservoir media; In the constructed digital structural model, a microscopic numerical model of the reservoir medium is obtained by embedding microscopic action mechanisms and flow mechanisms; by solving the model structural parameters and physical property parameters of the microscopic numerical model under different thermo-fluid-solid coupling conditions, a mapping relationship between the environmental conditions of the reservoir medium and its structural and physical property parameters is established. Based on the mapping relationship, and combined with the macroscopic geological and engineering data of the target reservoir, a macroscopic-scale thermal-fluid-solid multi-field coupled control equation is established and solved, outputting the multiphysics response of the target reservoir under given operating conditions. Specifically: a macroscopic-scale geometric model is established based on the geological and engineering data of the target deep energy reservoir; the mapping relationship is imported into the macroscopic-scale geometric model to establish a macroscopic-scale thermal-fluid-solid multi-field coupled control equation; initial and boundary conditions for the temperature field, seepage field, and stress field are set for the macroscopic-scale thermal-fluid-solid multi-field coupled control equation, and multiphysics coupling settings are performed to establish a macroscopic-scale thermal-fluid-solid multi-field coupled numerical model. The macroscopic thermal-fluid-solid multi-field coupled numerical model is discretized and solved using the finite element method, and the temperature field, seepage field and stress field of the macroscopic reservoir under given operating conditions are output.

2. The multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in claim 1, characterized in that, The spatial distribution characteristics are used to describe the spatial positional relationship, geometric morphology and connectivity characteristics of different mineral components and pore fracture structures at the microscale, so as to characterize the microscale heterogeneous structure characteristics of deep reservoir media. The physical properties parameters are used to characterize the thermal and mechanical physical properties of the corresponding mineral components, including thermal conductivity, specific heat capacity, elastic modulus and Poisson's ratio.

3. The multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in claim 1, characterized in that, The digital structural model is a three-dimensional model reconstructed based on the microstructure characterization results, using a combination of structural mapping and Monte Carlo methods.

4. The multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in claim 1, characterized in that, The micro-scale numerical model is a pore network model, which includes a pore network composed of pore nodes and pore throats, and a fracture network composed of connecting nodes and fracture elements and connected to the pore network through coupling nodes. The microscopic mechanisms include thermal-fluid coupling, fluid-structure coupling, and thermal-structure coupling mechanisms. The coupling process is bidirectional and dynamically updated with changes in temperature, pressure, or stress state. The flow mechanisms include one or more of diffusion flow, slip flow, Knudsen flow, and surface diffusion mechanisms, and the corresponding flow mechanism is adaptively selected according to the pore size characteristics and fluid properties.

5. The multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in claim 1, characterized in that, The mapping relationship between the environmental conditions of the reservoir medium and its structural and physical properties is established by using convolutional neural networks and adversarial neural networks. The input parameters for the mapping relationship include temperature, confining pressure, and injection / production pressure, while the output equivalent parameters are porosity, thermal conductivity, specific heat capacity, permeability, elastic modulus, and Poisson's ratio.

6. The multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in claim 1, characterized in that, The macroscopic-scale thermo-fluid-solid multi-field coupled numerical model is discretized and solved using the finite element method, including: The macroscopic geometric model is divided into a finite element mesh, and adaptive mesh refinement is used to ensure that the model can balance computational efficiency and simulation accuracy. Implicit time integration is used to perform step calculations on the temperature field, seepage field, and stress field to ensure numerical stability and adapt to the time scale differences of different physical fields. Within each time step, the temperature field, seepage field and stress field are fully coupled and solved using the Newton iteration method until the residuals of each physical field are lower than the preset threshold and the preset convergence condition is met. After iterative convergence, the equivalent physical property parameters and multi-field coupling response of each macroscopic model unit are calculated and used to update the boundary conditions and model parameters of the next time step. Finally, the multiphysics simulation results of the target deep reservoir under given conditions are output.

7. A simulation system employing the multi-scale thermo-fluid-solid coupling simulation method for deep energy reservoirs as described in any one of claims 1-6, characterized in that, include: The microscale parameter acquisition and geometric modeling module is used to acquire the spatial distribution characteristics and corresponding physical property parameters of different structural components at the microscale of reservoir media of different lithologies, and to construct a digital structural model of the reservoir media. The microscale modeling and cross-scale mapping module is used to construct a microscale numerical model of reservoir medium embedding multiple microscale action mechanisms; establish and solve the microscale thermal-fluid-solid multi-field coupled control equations on the microscale numerical model; and construct the mapping relationship between the environmental conditions and structural and physical property parameters of the reservoir medium. The macro-scale parameter acquisition and geometric modeling module is used to establish a macro-scale geometric model based on the geological data and engineering data of the target deep reservoir. The multiphysics coupling model construction module is used to import the reservoir medium physical property parameter library obtained under different environmental conditions into the macroscopic geometric model and establish the macroscopic thermal-fluid-solid multi-field coupling control equations. The initial boundary condition and multiphysics coupling setting module is used to set initial and boundary conditions for the temperature field, seepage field and stress field in the macroscopic thermal-fluid-solid multi-field coupling model, respectively, and to complete the multiphysics coupling configuration and establish a macroscopic thermal-fluid-solid multi-field coupling numerical model. The numerical solution and result output module is used to perform discrete Newton iteration solution on the macroscopic thermal-fluid-solid multi-field coupled numerical model using the finite element method, and output the multiphysics simulation results of the target deep reservoir under given working conditions.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the multi-scale thermo-fluid-structure interaction simulation method for deep energy reservoirs as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the multi-scale thermo-fluid-structure interaction simulation method for deep energy reservoirs as described in any one of claims 1-6.

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