Hot dry rock stratum temporary plugging material selection method based on force chain network analysis and dissipation dynamics

Abstract

The invention relates to the technical field of oil exploration, and discloses a hot dry rock stratum temporary plugging material selection method based on force chain network analysis and dissipation dynamics. The method specifically comprises the following steps: S1, testing alternative materials, specifically testing mechanical and thermal characteristics of granular materials; s2, force chain network analysis is carried out, the force chain network of the particle system is the core of material micromechanical behaviors, and dynamic distribution of force chains can be described through tensors; s3, performing energy dissipation and entropy generation analysis; s4, constructing and solving an optimization model, and constructing an optimization objective function by combining the force chain network and the energy dissipation parameters; and step S5, on the basis of constructing the optimization objective function S, by calculating the S value of the material and analyzing the comprehensive performance of physical properties such as a force chain network, energy dissipation and entropy generation of the material, the properties of different materials can be comprehensively evaluated and sorted, and it is assumed that a plurality of materials are compared. Therefore, the overall performance of the material can be judged according to the value of the optimized objective function S, and the larger the value is, the better the comprehensive performance of the material is.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum exploration, in particular to a method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics. Background Art

[0002] As a geothermal resource with great potential, hot dry rock is usually hidden thousands of meters underground. Its special geological conditions are particularly complex compared with oil and gas and medium and low temperature geothermal reservoirs. Its notable characteristics are "four highs": high temperature (over 180°C), high hardness, high stress and high density. These characteristics make hot dry rock geothermal exploitation face major technical challenges in key links such as drilling and well construction, fracturing and reservoir creation, and flow heat extraction. In particular, drilling into geothermal reservoirs and forming stable wellbores are prerequisites for realizing deep geothermal resource exploitation. However, due to the high temperature, high strength and abrasive resistance of the rock mass of deep geothermal reservoirs, the rheology and stability of the drilling fluid system will be significantly deteriorated in high temperature environments. In addition, the development of cracks and faults in the formation can easily lead to serious leakage of drilling fluid, increasing the risk of underground safety accidents. Therefore, how to overcome these technical difficulties is an urgent problem to be solved in the field of hot dry rock geothermal exploitation.

[0003] For hot dry rock formations, the pressure-bearing stability of the microscopic force chain network of the plugging layer is the key factor controlling the pressure-bearing capacity of the formation, and often determines the success or failure of the plugging operation. Cracks are the main channels for drilling fluid leakage, and effective plugging of cracks is the key to plugging. The selection of existing temporary plugging materials is mainly based on the macroscopic physical properties of particles (such as compressive strength, particle size distribution, etc.), without in-depth analysis of the microscopic mechanical behavior and thermodynamic properties of the particle system. The force chain network is the main path for bearing loads in granular materials, and its stability is crucial to the macroscopic performance of the plugging layer. In addition, the energy dissipation behavior and thermodynamic entropy generation of the material under dynamic loading and thermal action determine the long-term stability of the plugging layer. Therefore, there is an urgent need for a temporary plugging material optimization method that can effectively improve the stability of the plugging layer in hot dry rock formations and combine the mechanical behavior of particles and the law of energy dissipation. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and provide a method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics, specifically comprising the following steps:

[0007] Step S1, testing the candidate materials, specifically testing the mechanical and thermal properties of the granular materials;

[0008] Step S2, force chain network analysis. The force chain network of the particle system is the core of the microscopic mechanical behavior of the material. The distribution state of the force chain network in the particle system can be directly observed through the photoelastic test technology. The photoelastic test uses optical imaging technology and the optical deformation of particles under different stresses to visualize the dynamic distribution and evolution process of the force chain in the particle system, and finally obtains the force chain network image at the microscopic scale. These images serve as the basis for force chain network analysis. The dynamic distribution of the force chain can be described by tensors.

[0009] Step S3: energy dissipation and entropy generation analysis;

[0010] Step S4: Construction and solution of the optimization model, comprehensive force chain network and energy dissipation parameters, and construction of the optimization objective function:

[0011] S=ω1·tr(T)+ω2·tr(C)-ω3·λ-ω4·ρ diss -ω5·Φ entropy +ω6·f(C u ,σ c ,α);

[0012]

[0013] Where tr(T) is the trace of the force chain network tensor; tr(C) is the trace of the network connectivity tensor; λ is the anisotropy coefficient of the force chain network; ρ diss is the energy density dissipated per unit volume; Φ entropy is the entropy generation rate; f(C u ,σ c ,α) is the comprehensive physical properties of the material;ω is the optimization weight coefficient;

[0014] Step S5, on the basis of constructing the above-mentioned optimization objective function S, by calculating the S value of the material and analyzing the comprehensive performance of its physical properties such as force chain network, energy dissipation, and entropy generation, the performance of different materials can be comprehensively evaluated and ranked. Assuming that we have multiple materials for comparison, we can judge the overall performance of the material based on the value of the optimization objective function S. The larger the value, the better the comprehensive performance of the material.

[0015] Furthermore, the step S1 specifically includes:

[0016] Step S11, particle size distribution test,

[0017] Use a particle size analyzer to test the particle size distribution of the temporary plugging material and record the following parameters:

[0018] The average particle size of the particles avg ;

[0019] The maximum particle size d max ;

[0020] Particle size uniformity coefficient C u , the calculation formula is:

[0021]

[0022] Among them, d 60 and d 10 are the particle sizes corresponding to 60% and 10% passing in the cumulative particle size distribution;

[0023] Step S12: compressive strength and stiffness test,

[0024] The compressive strength of the particles was tested using a material mechanics testing machine. c ;

[0025] Step S13: thermal expansion and thermal conductivity test,

[0026] The thermal expansion coefficient α, thermal conductivity k and specific heat capacity c of the granular material were determined by thermal expansion experiments p .

[0027] Furthermore, the step S2 specifically includes:

[0028] Step S21, through the photoelastic test technology, the force chain network images at the microscopic scale are obtained. These images serve as the basis for force chain network analysis. The dynamic distribution tensor of the force chain is described by the force chain tensor T, and the total force chain tensor of the particle system is defined as:

[0029]

[0030] Among them, T αβ is the force chain tensor component between the α direction and the β direction; f i α is the component force of the i-th force chain in the α direction; r i β is the position vector of the i-th force chain in the β direction; N is the total number of force chains in the particle system;

[0031] The trace of the force chain tensor tr(T) reflects the total force chain strength of the particle system:

[0032] tr(T)=T xx +T yy +T zz ;

[0033] Among them, T xx is the force chain tensor component in the x direction, T yy is the force chain tensor component in the y direction, T zz is the force chain tensor component in the z direction;

[0034] Step S22, anisotropy coefficient λ,

[0035] The anisotropy coefficient of the particle system is calculated by the eigenvalue of the force chain tensor:

[0036]

[0037] Where: max is the maximum eigenvalue of the force chain tensor; min is the minimum eigenvalue of the force chain tensor; λ represents the degree of anisotropy of the force chain network distribution.

[0038] Step S23: Network connectivity tensor C

[0039] Define the connectivity tensor of the strong chain to quantify the network connectivity between particles:

[0040]

[0041] Where: C αβ is the connectivity tensor component between the α direction and the direction; N 链 is the total number of strong force chains in the system; f i α is the component force of the i-th force chain in the α direction;

[0042] The trace of the connectivity tensor tr(C) reflects the overall stability of the strong chain network:

[0043] tr(C)=C xx +C yy +C zz ;

[0044] Among them, C xx is the component of the force chain connectivity tensor in the x direction, C yy is the component of the force chain connectivity tensor in the y direction, C zz is the component of the force chain connectivity tensor in the z direction.

[0045] Furthermore, the step S3 specifically includes:

[0046] Step S31, the energy distribution formula of the particle system is:

[0047] E in =E el +E diss+ E thern ;

[0048] Among them, E in is the total energy input into the system; E el is the energy stored as elastic potential energy; E dissis the dissipated energy caused by friction and rupture; E thern is the heat dissipation energy caused by heat conduction;

[0049] Step S32, dissipated energy density per unit volume:

[0050]

[0051] Where: diss is the dissipated energy density per unit volume; η is the energy dissipation coefficient; σ ij Stress tensor components; ∈ ij Strain rate tensor component; V is the volume of the particle system;

[0052] Step S33: Entropy generation rate

[0053]

[0054] Where: Φ entropy is the entropy generation rate of the system; T is the absolute temperature.

[0055] Furthermore, the step S5 specifically includes:

[0056] Step S51: for each candidate material, calculate the optimization objective function S according to its force chain network, energy dissipation, entropy generation and other physical quantities.

[0057] Step S52, comparing the S values ​​of different materials and selecting the material with the best comprehensive performance;

[0058] Step S53: sort the materials according to the S value to help select the most suitable material for design or application.

[0059] In the optimization of temporary plugging materials for hot dry rock formations, the priorities of various indicators are as follows: the strength of the force chain network is the most critical, because it directly determines the bearing capacity of the particle system and the stability of plugging cracks; the second is network connectivity, which reflects the synergy and tightness between particles and directly affects the overall pressure-bearing performance; the energy dissipation density ranks third, and the smaller the dissipation, the higher the thermodynamic stability, which can ensure the long-term performance of the material under high temperature and high pressure; the anisotropy coefficient of the force chain network ranks second, and the higher the uniformity, the more stable the force, which helps to enhance the overall structure of the plugging layer; the entropy generation rate has the lowest priority, and its influence is mainly reflected in the long-term thermodynamic behavior, which is weaker than other indicators. This ranking combines the immediate performance and long-term stability of the material to ensure the applicability of the plugging layer in complex formations. Through the comprehensive evaluation and weighting of the above parameters, the optimization process not only considers the mechanical properties (strength and connectivity) of the material, but also takes into account the thermodynamic characteristics (energy dissipation and entropy generation). Finally, we choose materials with high force chain strength and connectivity, as well as low anisotropy coefficient, energy dissipation, and entropy generation rate, to meet the plugging needs of complex dry hot rock formations.

[0060] On the basis of constructing the above-mentioned optimization objective function S, the performance of different materials can be comprehensively evaluated and ranked by calculating the S value of the material and analyzing the comprehensive performance of its physical properties such as force chain network, energy dissipation, and entropy generation.

[0061] The beneficial effects of the present invention are as follows: the present invention has a reasonable design, a simple preparation method, and has the following advantages:

[0062] (1) Based on force chain network analysis and dissipative dynamics theory, the present invention scientifically quantifies the performance of temporary plugging materials from the perspective of micromechanics and thermodynamics; introduces parameters such as force chain tensor, connectivity tensor and dissipated energy density to comprehensively evaluate the mechanical behavior and stability of granular materials, making up for the deficiency of traditional temporary plugging material evaluation methods that only focus on macroscopic performance, making material selection more accurate and scientific;

[0063] (2) The present invention optimizes the formula design of temporary plugging materials by optimizing the strength, connectivity and anisotropy coefficient of the force chain network, combined with the constraints of energy dissipation behavior and entropy generation rate. The optimized material can form a more stable force chain network, improve the pressure bearing capacity and long-term stability of the plugging layer, and significantly improve the applicability of the material in the complex formation environment of dry hot rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 It is the overall flow chart of the present invention;

[0066] Figure 2 This is a microscopic force chain network structure diagram of the temporary plugging material plugging crack plugging layer in Example 1;

[0067] Figure 3 This is a microscopic force chain and strong force chain extraction diagram of the crack plugging layer of the rigid temporary plugging material of Material 1 in Example 1;

[0068] Figure 4 This is a microscopic force chain and strength chain extraction diagram of the crack plugging layer of the composite temporary plugging material of Material 2 in Example 1. DETAILED DESCRIPTION

[0069] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0070] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form also includes the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this description, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0071] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] like Figure 1 As shown, a method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics specifically includes the following steps:

[0073] Step S1, testing the candidate materials, specifically testing the mechanical and thermal properties of the granular materials;

[0074] Step S2, force chain network analysis. The force chain network of the particle system is the core of the microscopic mechanical behavior of the material. The distribution state of the force chain network in the particle system can be directly observed through the photoelastic test technology. The photoelastic test uses optical imaging technology and the optical deformation of particles under different stresses to visualize the dynamic distribution and evolution process of the force chain in the particle system, and finally obtains the force chain network image at the microscopic scale. These images serve as the basis for force chain network analysis. The dynamic distribution of the force chain can be described by tensors.

[0075] Step S3: energy dissipation and entropy generation analysis;

[0076] Step S4: construct and solve the optimization model, integrate the force chain network and energy dissipation parameters, and construct the optimization objective function:

[0077] S=ω1·tr(T)+ω2·tr(C)-ω3·λ-ω4·ρ diss -ω5·Φ entropy +ω6·f(C u ,σ c ,α);

[0078]

[0079] Where tr(T) is the trace of the force chain network tensor; tr(C) is the trace of the network connectivity tensor; λ is the anisotropy coefficient of the force chain network; ρ diss is the energy density dissipated per unit volume; Φ entropy is the entropy generation rate; f(C u ,σ c ,α) is the comprehensive physical properties of the material;ω is the optimization weight coefficient;

[0080] Step S5, on the basis of constructing the above-mentioned optimization objective function S, by calculating the S value of the material and analyzing the comprehensive performance of its physical properties such as force chain network, energy dissipation, and entropy generation, the performance of different materials can be comprehensively evaluated and ranked. Assuming that we have multiple materials for comparison, we can judge the overall performance of the material based on the value of the optimization objective function S. The larger the value, the better the comprehensive performance of the material.

[0081] Step S1 specifically includes:

[0082] Step S11, particle size distribution test,

[0083] Use a particle size analyzer to test the particle size distribution of the temporary plugging material and record the following parameters:

[0084] The average particle size of the particles avg ;

[0085] The maximum particle size d max ;

[0086] Particle size uniformity coefficient C u , the calculation formula is:

[0087]

[0088] Among them, d 60 and d 10 are the particle sizes corresponding to 60% and 10% passing in the cumulative particle size distribution;

[0089] Step S12: compressive strength and stiffness test,

[0090] The compressive strength of the particles was tested using a material mechanics testing machine. c ;

[0091] Step S13: thermal expansion and thermal conductivity test,

[0092] The thermal expansion coefficient α, thermal conductivity k and specific heat capacity c of the granular material were determined by thermal expansion experiments p .

[0093] Step S2 specifically includes:

[0094] Step S21, through the photoelastic test technology, the force chain network images at the microscopic scale are obtained. These images serve as the basis for force chain network analysis. The dynamic distribution tensor of the force chain is described by the force chain tensor T, and the total force chain tensor of the particle system is defined as:

[0095]

[0096] Among them, T αβ is the force chain tensor component between the α direction and the β direction; f i α is the component force of the i-th force chain in the α direction; r i β is the position vector of the i-th force chain in the β direction; N is the total number of force chains in the particle system;

[0097] The trace of the force chain tensor tr(T) reflects the total force chain strength of the particle system:

[0098] tr(T)=T xx +T yy +T zz ;

[0099] Among them, T xx is the force chain tensor component in the x direction, T yy is the force chain tensor component in the y direction, T zz is the force chain tensor component in the z direction;

[0100] Step S22, anisotropy coefficient λ,

[0101] The anisotropy coefficient of the particle system is calculated by the eigenvalue of the force chain tensor:

[0102]

[0103] Where: eax is the maximum eigenvalue of the force chain tensor; min is the minimum eigenvalue of the force chain tensor; λ represents the degree of anisotropy of the force chain network distribution.

[0104] Step S23: Network connectivity tensor C

[0105] Define the connectivity tensor of the strong chain to quantify the network connectivity between particles:

[0106]

[0107] Where: C αβ is the connectivity tensor component between the α direction and the direction; N 链 is the total number of strong force chains in the system; f i α is the component force of the i-th force chain in the α direction;

[0108] The trace of the connectivity tensor tr(C) reflects the overall stability of the strong chain network:

[0109] tr(C)=C xx +C yy +C zz ;

[0110] Among them, C xx is the component of the force chain connectivity tensor in the x direction, C yy is the component of the force chain connectivity tensor in the y direction, C zz is the component of the force chain connectivity tensor in the z direction.

[0111] Step S3 specifically includes:

[0112] Step S31, the energy distribution formula of the particle system is:

[0113] E in =E el +E diss+ E thern ;

[0114] Among them, E in is the total energy input into the system; E el is the energy stored as elastic potential energy; E diss is the dissipated energy caused by friction and rupture; E thern is the heat dissipation energy caused by heat conduction;

[0115] Step S32, dissipated energy density per unit volume:

[0116]

[0117] Where: diss is the dissipated energy density per unit volume; η is the energy dissipation coefficient; σ ij Stress tensor components; ∈ ij Strain rate tensor component; V is the volume of the particle system;

[0118] Step S33: Entropy generation rate

[0119]

[0120] Where: Φ entropy is the entropy generation rate of the system; T is the absolute temperature.

[0121] Furthermore, the step S5 specifically includes:

[0122] Step S51: for each candidate material, calculate the optimization objective function S according to its force chain network, energy dissipation, entropy generation and other physical quantities.

[0123] Step S52, comparing the S values ​​of different materials and selecting the material with the best comprehensive performance;

[0124] Step S53: sort the materials according to the S value to help select the most suitable material for design or application.

[0125] Example 1

[0126] During the construction of a hot dry rock formation wellbore, serious fluid leakage occurred, and it was necessary to select appropriate temporary plugging materials for fissure plugging. The formation temperature is 300°C, the pressure is 40MPa, and the fissure width ranges from 0.1mm to 2mm. In order to adapt to this complex environment, the method of the present invention is used to optimize the temporary plugging material to ensure the long-term stability of the plugging layer. The specific operation steps are as follows:

[0127] (1) Screening of temporary plugging materials

[0128] Two candidate temporary plugging materials are selected, namely:

[0129] Material 1: Based on rigid granular material;

[0130] Material 2: Composite material of high temperature expansion particles and fiber reinforcement;

[0131] The particle size distribution of the two materials was tested by a particle size analyzer:

[0132] The particle size distribution range of material 1 is 0.2 mm to 1.5 mm, and the uniformity coefficient Cu = 1.8;

[0133] The particle size distribution range of material 2 is 0.1 mm to 2.0 mm, and the uniformity coefficient Cu = 2.2;

[0134] The compressive strength, thermal expansion coefficient and thermal conductivity of the test material are as follows:

[0135] Material Compressive strength(MPa) <![CDATA[Coefficient of thermal expansion (10 -6 °C)]]> Thermal conductivity (W / m·K) 1 50 3.5 1.2 2 42 2.5 0.9

[0136] (2) Force chain network test

[0137] The photoelastic test was used to simulate the loading of the particle system of two materials. Figure 2 This is a picture of the microscopic force chain network structure of the temporary plugging material plugging crack sealing layer directly observed through the photoelastic test, which is used to directly present the test results. This picture is a representative of the measurement results of the photoelastic test. Figure 2 The photoelastic test results were analyzed to obtain Figure 3 and Figure 4 The extracted force chain network structures are as follows: Figure 3 and Figure 4 The force chain network parameters are calculated, including the force chain tensor, connectivity tensor and anisotropy coefficient.

[0138] Force chain tensor trace (tr(T)): 120 for material 1 and 140 for material 2

[0139] Connectivity tensor trace (tr(C)): 0.85 for material 1 and 0.92 for material 2

[0140] Anisotropy coefficient (λ): 0.25 for material 1 and 0.18 for material 2

[0141] From the results, it can be seen that the force chain network of material 2 has higher strength, better connectivity, and smaller anisotropy.

[0142] (3) Energy dissipation and entropy generation test

[0143] The energy dissipation density and entropy generation rate of the material were tested under a high temperature and high pressure environment of 300°C and 40MPa.

[0144] Energy dissipation density per unit volume: Material 1: 2.0MJ / m 3 , Material 2: 1.2 MJ / m 3

[0145] Entropy generation rate: Material 1: 0.015 J / (K·s), Material 2: 0.010 J / (K·s)

[0146] Material 2 shows better stability in energy dissipation and entropy generation, with low dissipated energy density and small entropy generation, indicating that it has better long-term stability under high temperature and high pressure environment.

[0147] (4) Optimizing model calculation

[0148] Substitute the above parameters into the optimization objective function

[0149] The comprehensive scoring results of the materials are: Material 1: S = 48.18, Material 2: S = 56.23

[0150] (5) Leakage plugging performance verification

[0151] The optimized materials 1 and 2 were applied to the simulated fracture device to test their plugging performance:

[0152] (1) Pressure bearing capacity of the plugging layer: Material 1 can only bear 20 MPa, while Material 2 can bear 48 MPa

[0153] (2) Long-term stability: Compared with material 1, material 2 maintains a stable structure in a high temperature (300°C) environment.

[0154] In summary, the present invention has a reasonable design, a simple preparation method, and has the following advantages:

[0155] (1) Based on force chain network analysis and dissipative dynamics theory, the present invention scientifically quantifies the performance of temporary plugging materials from the perspective of micromechanics and thermodynamics; introduces parameters such as force chain tensor, connectivity tensor and dissipated energy density to comprehensively evaluate the mechanical behavior and stability of granular materials, making up for the deficiency of traditional temporary plugging material evaluation methods that only focus on macroscopic performance, making material selection more accurate and scientific;

[0156] (2) The present invention optimizes the formula design of temporary plugging materials by optimizing the strength, connectivity and anisotropy coefficient of the force chain network, combined with the constraints of energy dissipation behavior and entropy generation rate. The optimized material can form a more stable force chain network, improve the pressure bearing capacity and long-term stability of the plugging layer, and significantly improve the applicability of the material in the complex formation environment of dry hot rock.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics, characterized in that: The specific steps include: Step S1, testing the candidate materials, specifically testing the mechanical and thermal properties of the granular materials; Step S2, force chain network analysis. The force chain network of the particle system is the core of the microscopic mechanical behavior of the material. The distribution state of the force chain network in the particle system can be directly observed through the photoelastic test technology. The photoelastic test uses optical imaging technology and the optical deformation of particles under different stresses to visualize the dynamic distribution and evolution process of the force chain in the particle system, and finally obtains the force chain network image at the microscopic scale. These images serve as the basis for force chain network analysis. The dynamic distribution of the force chain can be described by tensors. Step S3: energy dissipation and entropy generation analysis; Step S4: construct and solve the optimization model, integrate the force chain network and energy dissipation parameters, and construct the optimization objective function: S=ω1·tr(T)+ω2·tr(C)-ω3·λ-ω4·ρ diss -ω5·Φ entropy +ω6·f(C u ,s c ,a); Where tr(T) is the trace of the force chain network tensor; tr(C) is the trace of the network connectivity tensor; λ is the anisotropy coefficient of the force chain network; ρ diss is the energy density dissipated per unit volume; Φ entropy is the entropy generation rate; f(C u , σ c , α) is the comprehensive physical properties of the material; ω is the optimization weight coefficient; Step S5, on the basis of constructing the above-mentioned optimization objective function S, by calculating the S value of the material and analyzing the comprehensive performance of its physical properties such as force chain network, energy dissipation, and entropy generation, the performance of different materials can be comprehensively evaluated and ranked. Assuming that we have multiple materials for comparison, we can judge the overall performance of the material based on the value of the optimization objective function S. The larger the value, the better the comprehensive performance of the material.

2. The method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics according to claim 1, characterized in that: The step S1 specifically includes: Step S11, particle size distribution test, Use a particle size analyzer to test the particle size distribution of the temporary plugging material and record the following parameters: The average particle size of the particles avg ; The maximum particle size d max ; Particle size uniformity coefficient C u , the calculation formula is: Among them, d 60 and d 10 are the particle sizes corresponding to 60% and 10% passing in the cumulative particle size distribution; Step S12: compressive strength and stiffness test, The compressive strength of particles was tested by a material mechanics testing machine. c ; Step S13: thermal expansion and thermal conductivity test, The thermal expansion coefficient α, thermal conductivity k and specific heat capacity c of the granular material were determined by thermal expansion experiments p .

3. The method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics according to claim 1, characterized in that: The step S2 specifically includes: Step S21: Obtain force chain network images at a microscopic scale through photoelastic test technology. These images serve as the basis for force chain network analysis. The dynamic distribution tensor of the force chain is described by the force chain tensor T. Define the total force chain tensor of the particle system: Among them, T αβ is the force chain tensor component between the α direction and the β direction; f i α is the component force of the i-th force chain in the α direction; is the position vector of the i-th force chain in the β direction; N is the total number of force chains in the particle system; The trace of the force chain tensor tr(T) reflects the total force chain strength of the particle system: tr(T)=T xx +T yy +T zz ; Among them, T xx is the force chain tensor component in the x direction, T yy is the force chain tensor component in the y direction, T zz is the force chain tensor component in the z direction; Step S22, anisotropy coefficient λ, The anisotropy coefficient of the particle system is calculated by the eigenvalue of the force chain tensor: Where: max is the maximum eigenvalue of the force chain tensor; min is the minimum eigenvalue of the force chain tensor; λ represents the degree of anisotropy of the force chain network distribution. Step S23: Network connectivity tensor C Define the connectivity tensor of the strong chain to quantify the network connectivity between particles: Where: C αβ is the connectivity tensor component between the α direction and the direction; N 链 is the total number of strong force chains in the system; f i α is the component force of the i-th force chain in the α direction; The trace of the connectivity tensor tr(C) reflects the overall stability of the strong chain network: tr(C)=C xx +C yy +C zz ; Among them, C xx is the component of the force chain connectivity tensor in the x direction, C yy is the component of the force chain connectivity tensor in the y direction, C zz is the component of the force chain connectivity tensor in the z direction.

4. The method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics according to claim 1, characterized in that: The step S3 specifically includes: Step S31, the energy distribution formula of the particle system is: AND in =And el +E diss +E thern ; Among them, E in is the total energy input into the system; E el is the energy stored as elastic potential energy; E diss is the dissipated energy caused by friction and fracture; E thern is the heat dissipation energy caused by heat conduction; Step S32, dissipated energy density per unit volume: Where: diss is the dissipated energy density per unit volume; η is the energy dissipation coefficient; σ ij Stress tensor component; ε ij Strain rate tensor component; V is the volume of the particle system; Step S33: Entropy generation rate Where: Φ entropy is the entropy generation rate of the system; T is the absolute temperature.

5. The method for selecting temporary plugging materials for hot dry rock formations based on force chain network analysis and dissipative dynamics according to claim 1, characterized in that: The step S5 specifically includes: Step S51: for each candidate material, calculate the optimization objective function S according to its force chain network, energy dissipation, entropy generation and other physical quantities. Step S52, comparing the S values ​​of different materials and selecting the material with the best comprehensive performance; Step S53: sort the materials according to the S value to help select the most suitable material for design or application.

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