Coal seam water injection difficulty degree evaluation method based on water injection seepage model

Abstract

The application discloses a coal seam water injection difficulty degree evaluation method based on a water injection seepage model, and comprises the following steps: S1, constructing a coal body permeation flow model; according to the Darcy law, a flow formula is established, the flow formula is corrected and integrated, and the coal body permeation flow model is obtained; S2, constructing a coal seam water injection difficulty degree evaluation system and classifying water injection difficulty degrees; S21, selecting parameters with greater influence as main control factors through a Sobol sensitivity analysis method; S22, based on a set pair analysis method and a multivariate stepwise regression analysis method, a coal seam water injection difficulty degree evaluation system is established, and then water injection difficulty degrees are classified; and S3, verifying whether the coal body water injection difficulty degree evaluation system is reliable. Sensitivities of the main control factors are calculated by using the Sobol sensitivity analysis method, then an injection difficulty degree evaluation index system is constructed by combining a set pair analysis theory and a multivariate linear regression analysis method, and thus more scientific and reasonable classification of the coal seam water injection difficulty degree standard is realized.

Description

Technical Field

[0001] This invention belongs to the technical fields of coal mine gas disaster prevention and control, gas extraction and management, and coalbed methane production prediction. Specifically, it relates to a method for evaluating the ease of coal seam water injection based on a water injection seepage model. Background Technology

[0002] With the continuous advancement of coal seam water injection technology, the evaluation of the ease or difficulty of coal seam water injection has become increasingly important. The effect of coal seam water injection is closely related to the macroscopic physical structure of water injection and the microscopic chemical properties of wetting. Water injection refers to the penetration of water into the pores and fissures of the coal body, and its effect is mainly affected by the microscopic pore characteristics of the coal. During the water injection seepage process, water will wet the pores and fissures of the coal body. Wetting refers to the ability of coal to adsorb water, and its effect is affected by the microscopic wetting properties such as composition, chemical elements, and functional groups.

[0003] Currently, scholars have conducted many studies on the ease and effectiveness of water injection into coal seams. However, the selected parameters are often too limited or too closely correlated with the established models, making it impossible to scientifically and reasonably classify the ease of water injection into coal seams. Summary of the Invention

[0004] This invention proposes a coal seam redemption seepage model, uses Sobol sensitivity analysis to calculate the sensitivity of each controlling factor, and then couples the seepage model with the evaluation method using set pair analysis theory to obtain the optimal standard correlation degree of the evaluation index. By introducing multiple linear regression analysis, an evaluation index system for the difficulty of water injection is constructed, thereby achieving a more scientific and reasonable division of the difficulty of coal seam water injection.

[0005] Therefore, the technical solution adopted in this invention is: a method for evaluating the ease of coal seam water injection based on a water injection seepage model, comprising the following steps:

[0006] S1: Construct a coal seam permeability flow model;

[0007] S11: Establish the relationship between fracture aperture, roughness, and permeability coefficient;

[0008] S12: Establish the relationship between fracture aperture and pressure;

[0009] S13: Construct a model of the actual length of the fluid path;

[0010] S14: Based on Darcy's law, establish the flow rate formula for fluid passing through a single capillary in a porous medium. Then, after correcting and integrating the flow rate formula, the coal body permeation flow rate model is obtained.

[0011] S2: Construct an evaluation system for the difficulty of water injection into coal seams and classify the difficulty of water injection.

[0012] S21: Conduct Sobol sensitivity analysis on each influencing factor in the coal seepage flow model, study the influence of each influencing factor on the seepage flow, and select the parameters with greater influence in the coal seepage flow model as the main control factors;

[0013] S22: Using set pair analysis, determine the optimal standard correlation degree of each main control factor, and then establish the functional relationship between each main control factor and the optimal standard correlation degree through multivariate stepwise regression analysis, thereby obtaining the evaluation system for the difficulty of coal seam water injection, and then classifying the difficulty of water injection.

[0014] S3: Verify the reliability of the coal seam water injection difficulty evaluation system; conduct seepage experiments on coal samples to obtain measured permeability values, and then classify the measured permeability values ​​according to the oil and gas reservoir permeability evaluation standard; at the same time, take the main control factors obtained from the experiment for the same coal sample and input them into the coal seam water injection difficulty evaluation system to obtain theoretical calculation values; finally, compare the classification results obtained from the measured permeability values ​​with the classification results obtained from the evaluation system to determine whether the two have a high degree of consistency.

[0015] As a preferred embodiment of the above scheme, in step S11, the undulations of the coal sample fracture surface exhibit strong randomness. The fracture aperture and roughness affect the fluid flow behavior within the fractures, and both influence the ease of water injection by affecting permeability. The permeability coefficient is related to permeability, liquid density, and gravitational acceleration, and exhibits the following relationship:

[0016] 1

[0017] In the formula For penetration rate, For the density of the liquid, It is the acceleration due to gravity. Permeability coefficient, Let be the fluid viscosity; permeability represents the inherent hydraulic conductivity of the fracture and is proportional to the square of the fracture width. According to the cubic law for smooth fractures, permeability and fracture aperture have the following relationship:

[0018] 2

[0019] In the formula Let fracturing aperture be the factor. Since the ratio of fracturing protrusion to fracturing width affects fracturing permeability, the cubic law is further refined to:

[0020] 3

[0021] In the formula, is The surface roughness of the crack, by substituting Formula 3 into Formula 1, yields...

[0022] 4

[0023] From the above formula, we can see that the permeability coefficient is directly proportional to the fracture aperture and inversely proportional to the fracture surface roughness.

[0024] Further optimized, in step S12, the fracture aperture and permeability continuously decrease with increasing hydrostatic pressure, exhibiting a negative exponential relationship with hydrostatic pressure, as follows:

[0025] 5

[0026] In the formula This represents the hydrostatic pressure, which is calculated using the following formula:

[0027] 6

[0028] In the formula, The contact area between the fluid and the flow through the fissure. For the pressure difference, substituting formula 6 into step 5, we can obtain... 7.

[0029] Further preferred, in step S13, the fractal scaling law of capillary pore size distribution is as follows:

[0030] 8

[0031] In the formula, The number of pores whose characteristic length is greater than the capillary radius. The maximum throat radius, The volume fractal dimension, Let be the capillary radius; since the curvature of the capillary bundle also conforms to fractal characteristics, the relationship between the actual length of the capillary and the straight length along the pressure gradient direction is expressed as:

[0032] 9

[0033] In the formula, This is the actual length of the fluid path. The characteristic length of the capillary. Let be the fractal dimension of capillary tortuosity, where the formula for calculating the characteristic length of the capillary is:

[0034] 10

[0035] In the formula, To determine the porosity, substituting Equation 10 into Equation 9, we obtain the actual length of the fluid path as:

[0036] 11

[0037] Due to the complex shape of capillaries, fluid flow in coal and rock masses does not proceed in a straight line, but rather in a tortuous manner. The pore and fracture structure inside coal and rock masses is mostly composed of bundles of tortuous capillaries. Therefore, when fluid flows through these complex pores, the average capillary radius is generally used to represent the capillary radius, and its expression is:

[0038] 12

[0039] In the formula, The average capillary radius, To find the minimum orifice throat radius, substituting Equation 12 into Equation 11 yields the corrected expression for the actual length of the fluid path:

[0040] 13.

[0041] Further preferred, in step S14, based on Darcy's law, the flow rate formula for fluid passing through a single capillary in a porous medium is:

[0042] 14

[0043] In the formula, For fluid viscosity, For pressure difference, Permeability coefficient, The area of ​​the seepage channel in a single capillary tube. The characteristic length of the capillary. Let be the capillary radius. The flow rate formula is modified by replacing the characteristic length of the capillary with the actual length of the fluid path, and representing the capillary radius with the average capillary radius. The modified flow rate formula is as follows:

[0044] 15

[0045] Integrating the flow rate formula yields:

[0046]

[0047]

[0048]

[0049] In the formula, This represents the total seepage flow rate.

[0050] The beneficial effects of this invention are:

[0051] 1) Based on CT three-dimensional microscopic reconstruction, the effective pore structure of coal body is visualized, and the effective porosity and three-dimensional effective seepage structure parameters are extracted. Darcy's law, the permeability coefficient expression and fractal theory are combined and modified to construct a theoretical model of micropore seepage in coal seam water injection. The multi-parameter comprehensive characterization of the effective interconnected pore fracture structure characteristics of coal body is achieved.

[0052] 2) Based on the theoretical model of micropore seepage in coal seam water injection, the Sobol sensitivity analysis method was introduced to achieve quantitative screening of the main influencing factors of coal seam water injection capacity and further simplification of the micropore seepage model of coal seam water injection. At the same time, based on numerical analysis methods, the law of action of the main influencing factors of coal seam water injection capacity was analyzed, which effectively solved the problem of model being limited by the range and quantity of coal samples.

[0053] 3) By coupling the coal seam water injection seepage model with set pair analysis and multiple linear regression analysis, the relationship between the microscopic seepage model and the mathematical evaluation model was established, and an evaluation index system for the difficulty of water injection was constructed. The system was then modified in combination with the actual water injection situation, thus realizing a more scientific and reasonable classification of the difficulty of coal seam water injection. Attached Figure Description

[0054] Figure 1 This is a flowchart of the present invention.

[0055] Figure 2 This is a screenshot of the Sobol sensitivity analysis performed in the software according to the present invention.

[0056] Figure 3 This is a screenshot of the software performing set pair analysis according to the present invention.

[0057] Figure 4 This is a three-dimensional diagram of the total pore fracture structure and the interconnecting pore fracture structure obtained after CT scanning of each coal sample in this embodiment.

[0058] Figure 5 This is a schematic diagram of the software operation for performing seepage experiments on various coal samples in this embodiment. Detailed Implementation

[0059] The present invention will be further described below with reference to the embodiments and accompanying drawings:

[0060] like Figure 1-5 As shown, a method for evaluating the ease of water injection into coal seams based on a water injection seepage model includes the following steps:

[0061] Step 1: Construct a coal seam permeability flow model.

[0062] 1) Establish the relationship between fracture aperture, roughness and permeability coefficient.

[0063] Because the surface undulations of coal sample fractures exhibit strong randomness, fracture aperture and roughness affect fluid flow behavior within the fractures. These factors influence the ease of water injection by affecting permeability. The permeability coefficient is related to permeability, liquid density, and gravitational acceleration, and exhibits the following relationship:

[0064] (1)

[0065] In the formula For penetration rate, For the density of the liquid, It is the acceleration due to gravity. Permeability coefficient, This represents the fluid viscosity.

[0066] Permeability represents the inherent hydraulic conductivity of a fracture and is directly proportional to the square of the fracture width. According to the cubic law for smooth fractures, permeability and fracture aperture have the following relationship:

[0067] (2)

[0068] In the formula This refers to the crack aperture.

[0069] Since the ratio of the degree of fissure protrusion to the fissure width affects the fissure permeability, the cubic law is further refined to:

[0070] (3)

[0071] In the formula, is The surface roughness of the crack, by substituting Formula 3 into Formula 1, yields...

[0072] (4)

[0073] From the above formula, we can see that the permeability coefficient is directly proportional to the fracture aperture and inversely proportional to the fracture surface roughness.

[0074] 2) Establish the relationship between fracture aperture, roughness and permeability coefficient.

[0075] Fracture aperture and permeability decrease continuously with increasing hydrostatic pressure, exhibiting a negative exponential relationship with hydrostatic pressure, as follows:

[0076] (5)

[0077] In the formula This represents the hydrostatic pressure, which is calculated using the following formula:

[0078] (6)

[0079] In the formula, The contact area between the fluid and the flow through the fissure. For the pressure difference, substituting formula 6 into formula 5, we can obtain...

[0080] (7).

[0081] 3) Construct a model of the actual length of the fluid path

[0082] Based on existing technologies, fractal geometry theory is used to analyze the pore structure characteristics of coal and study its seepage properties. The fractal scaling law for capillary pore size distribution is as follows:

[0083] (8)

[0084] In the formula, The number of pores whose characteristic length is greater than the capillary radius. The maximum throat radius, The volume fractal dimension, Where is the capillary radius.

[0085] Since the curvature of the capillary bundle also conforms to fractal characteristics, the relationship between the actual length of the capillary and the straight length along the pressure gradient direction can be expressed as:

[0086] (9)

[0087] In the formula, This is the actual length of the fluid path. The characteristic length of the capillary. Let be the fractal dimension of capillary tortuosity, where the formula for calculating the characteristic length of the capillary is:

[0088] (10)

[0089] In the formula, To determine the porosity, substituting Equation 10 into Equation 9, we obtain the actual length of the fluid path as:

[0090] (11)

[0091] Due to the complex shape of capillaries, fluid flow in coal and rock masses does not proceed in a straight line, but rather in a tortuous manner. The pore and fracture structure inside coal and rock masses is mostly composed of bundles of tortuous capillaries. Therefore, when fluid flows through these complex pores, the average capillary radius is generally used to represent the capillary radius, and its expression is:

[0092] (12)

[0093] In the formula, The average capillary radius, To find the minimum orifice throat radius, substituting Equation 12 into Equation 11 yields the corrected expression for the actual length of the fluid path:

[0094] (13).

[0095] 4) Based on Darcy's law, establish the flow rate formula for fluid passing through a single capillary in a porous medium. Then, after correcting and integrating the flow rate formula, obtain the coal body permeation flow rate model.

[0096] Based on Darcy's law, the flow rate formula for fluid passing through a single capillary tube in a porous medium is:

[0097] (14)

[0098] In the formula, For fluid viscosity, For pressure difference, Permeability coefficient, The area of ​​the seepage channel in a single capillary tube. The characteristic length of the capillary. Where is the capillary radius.

[0099] The adsorption of water by coal is caused by a combination of capillary forces and intermolecular forces. Capillary forces can introduce the water phase into the interior of the coal and into the cracks and pores. The smaller the pore size, the greater the capillary force; the larger the pore size, the smaller the capillary force.

[0100] When water seeps through coal, it is affected by gravity and capillary force. The flow rate formula is modified by replacing the characteristic length of the capillary with the actual length of the fluid path and representing the capillary radius with the average capillary radius. The modified flow rate formula is as follows:

[0101] (15)

[0102] Integrating the flow rate formula yields:

[0103]

[0104]

[0105] (17)

[0106] In the formula, This represents the total seepage flow rate.

[0107] The second step is to construct an evaluation system for the difficulty of water injection into coal seams and to classify the difficulty of water injection.

[0108] 1) Conduct Sobol sensitivity analysis on each influencing factor in the coal seepage flow model to study the influence of each influencing factor on the seepage flow and select the parameters with greater influence in the coal seepage flow model as the main control factors.

[0109] The Sobol sensitivity analysis method involves the model. The model is decomposed into a combination of single-parameter and multi-parameter functions. The impact of parameter variations on the model response is analyzed by calculating the influence of the sample variance of the parameters on the total variance of the model response. The steps are as follows:

[0110] definition , For input parameters The value space will It can be decomposed into a combination of single-parameter functions and multi-parameter functions, that is:

[0111]

[0112] The total variance of the parameter sampling samples represents the degree of influence of all parameter changes on the model response. The total variance is:

[0113]

[0114] definition You can get:

[0115]

[0116] In the formula, For parameters The first-order sensitivity data represents The main impact on the output, For parameters and The second-order sensitivity data represent the cross-effect of the two parameters. The overall sensitivity is The sum of the sensitivities of each order of this parameter ,in For the parameter The sensitivity of all external design parameters to the model response is used to determine the main controlling factors.

[0117] Based on the coal seam seepage flow model, such as Figure 2 As shown, using MATLAB software, the main controlling factors were calculated to be porosity, fissure aperture, roughness, and maximum pore throat radius.

[0118] 2) Using set pair analysis, determine the optimal standard correlation degree of each main control factor. Then, through multivariate stepwise regression analysis, establish the functional relationship between each main control factor and the optimal standard correlation degree, thereby obtaining the evaluation system for the difficulty of coal seam water injection, and then classify the difficulty of water injection.

[0119] In S22, the method for determining each controlling factor is as follows:

[0120] Let the evaluation set to be injected be... ,have There are [number] objects to be evaluated, and each object has [number] evaluation items. There are several evaluation indicators, and the value of each indicator is set as follows: Using the set to be evaluated as a reference, the optimal indicators from the set are selected to form the set of the highest evaluation standards, denoted as... Similarly, the worst-case indicators constitute the set of worst-case evaluation criteria, which are denoted as... , Comparative set constituting coal seams , As an indicator The standard range; the indicator weight is denoted as... The evaluation index of coal samples Form a set pair with the evaluation standard interval ,in For the above set of indicators to be evaluated , If the set is a comparison set composed of evaluation criteria, then the set pair In the interval The degree of connection is:

[0121]

[0122] In the formula, To gather connections, The object to be evaluated For the set to be of the same degree, For set-pair difference degree, The set-to-dissimilarity coefficient ranges from 100 to 100. The degree of opposition coefficient is -1.

[0123] Using set pair analysis, the optimal standard correlation degree between each main control factor and water injection capacity is determined. The main control factors are arranged in a matrix to obtain multiple coal sample data. The similarity and opposition of each main control factor are calculated, and then the optimal standard correlation degree is obtained.

[0124] Determine the range of values ​​for each controlling factor, such as Figure 3As shown, using the software SPPS, four main controlling factors—porosity, fracture aperture, roughness, and maximum pore throat radius—were arranged in a square matrix to obtain multiple sets of coal sample data. The similarity and contrast of each main controlling factor were calculated, and the optimal standard correlation degree was obtained. Then, the optimal regression equation was obtained through multiple linear regression analysis:

[0125]

[0126] Based on experience, the evaluation index system for the difficulty of water injection is divided as follows:

[0127] .

[0128] 3) Verify the reliability of the coal seam water injection difficulty evaluation system. Seepage experiments were conducted on coal samples to obtain measured permeability values. These measured permeability values ​​were then classified according to the oil and gas reservoir permeability evaluation standards. Simultaneously, the main controlling factors obtained from the experiments for the same coal samples were incorporated into the coal seam water injection difficulty evaluation system to obtain theoretically calculated values. Finally, the classification results obtained from the measured permeability values ​​were compared with those obtained from the evaluation system to determine whether the two have a high degree of consistency.

[0129] To verify the accuracy of this evaluation index system for the difficulty of water injection, coal samples from six different locations were selected for CT scanning and seepage experiments.

[0130] The specific experiment is as follows:

[0131] Coal samples selected from the working face were sealed and sent to the laboratory. A core extractor was used to prepare samples of three sizes: 2mm, 5mm, and 9mm in diameter, each 30mm in height. The samples were then subjected to CT scanning using a high-precision X-ray microscope in a coal triaxial flow testing system, yielding CT images of 2μm, 8.5μm, and 9.5μm. For each coal sample, 300 consecutive CT images of the less damaged central portion were selected. Median filtering was used to reduce noise. To distinguish the coal matrix and fractures, threshold segmentation was used to binarize the images. Three-dimensional reconstruction was then performed on the thresholded CT images. The processed two-dimensional images were spatially superimposed to obtain the three-dimensional pore structure. Connected pores were separated and extracted. The AVIZO analysis module was used to extract the pore structure parameters of the reconstructed coal body, such as... Figure 4As shown, this embodiment obtains the three-dimensional total pore fracture structure and interconnected pore fracture structure of the coal samples, i.e., each main controlling factor of each coal sample is obtained. These factors are then incorporated into the evaluation index system for the ease of water injection. It is found that coal samples TC and SM belong to relatively easy coal seams for water injection, coal samples YC1 and NM belong to difficult coal seams for water injection, and coal samples ZG and YC2 belong to non-water-injectable coal seams. The ease of water injection for the six coal samples, from high to low, is as follows: TC>SM>YC1>NM>ZG>YC2.

[0132] Seepage tests were conducted on six coal samples. Specifically, coal samples selected from the working face were sealed and sent to the laboratory. A core extractor was used to prepare the samples into cylinders 30 mm high and 5 mm in diameter. The coal cores were then loaded into a triaxial seepage testing system, with custom-designed auxiliary plugs placed above and below them. The system was then pressurized, with the water pressure initially reduced to 0 MPa. All pressure protection settings were set higher than the experimental pressure. The pressure reduction operation could be performed on the control screen and computer system. First, the first axial pressure was increased to 1 MPa, and the ring pressure to 5 MPa to prevent excessive ring pressure from closing internal pores and fractures in the coal body. Then, the first axial pressure was increased to 3 MPa, the water pressure was reduced to 0 MPa, and then increased to 0.5 MPa. The temperature was set to 25°C. After setting the pressure, the protection pressure was set to 0.5-1 MPa higher than the set pressure. The system's built-in system could calculate the seepage flow rate. During the seepage test, the software operation was as follows: Figure 5 As shown.

[0133] Based on the evaluation criteria for oil and gas reservoir permeability, the measured permeability values ​​were divided into categories. Coal samples TC and SM were identified as medium-permeability coal seams, YC1 and NM as low-permeability coal seams, and ZG and YC2 as relatively low-permeability coal seams. Furthermore, the ease of water injection for the six coal samples, from highest to lowest, was: TC > SM > YC1 > NM > ZG > YC2. Therefore, the evaluation index system classification results in this application show a high degree of consistency with the experimental permeability classification results.

Claims

1. A method for evaluating the ease of water injection into coal seams based on a water injection seepage model, characterized in that, Includes the following steps: S1: Construct a coal seam permeability flow model; S11: Establish the relationship between fracture aperture, roughness, and permeability coefficient; S12: Establish the relationship between fracture aperture and pressure; S13: Construct a model of the actual length of the fluid path; S14: Based on Darcy's law, establish the flow rate formula of fluid in porous media through a single capillary tube. Then, after correcting the flow rate formula using the formulas in S11, S12, and S13 and integrating, obtain the coal body permeation flow rate model. S2: Construct an evaluation system for the difficulty of water injection into coal seams and classify the difficulty of water injection. S21: Conduct Sobol sensitivity analysis on each influencing factor in the coal seepage flow model, study the influence of each influencing factor on the seepage flow, and select the parameters with greater influence in the coal seepage flow model as the main control factors; S22: Using set pair analysis, determine the optimal standard correlation degree of each main control factor, and then establish the functional relationship between each main control factor and the optimal standard correlation degree through multivariate stepwise regression analysis, thereby obtaining the evaluation system for the difficulty of coal seam water injection, and then classifying the difficulty of water injection. S3: Verify the reliability of the coal seam water injection difficulty evaluation system; conduct seepage experiments on coal samples to obtain measured permeability values, and then classify the measured permeability values ​​according to the oil and gas reservoir permeability evaluation standard; at the same time, take the main control factors obtained from the experiment for the same coal sample and input them into the coal seam water injection difficulty evaluation system to obtain theoretical calculation values; finally, compare the classification results obtained from the measured permeability values ​​with the classification results obtained from the evaluation system to determine whether the two have a high degree of consistency.

2. The method for evaluating the ease of coal seam water injection based on a water injection seepage model as described in claim 1, characterized in that, In step S11, the undulations of the coal sample fracture surface exhibit strong randomness. The fracture aperture and roughness affect the fluid flow behavior within the fractures, and both influence the ease of water injection by affecting permeability. The permeability coefficient is related to permeability, liquid density, and gravitational acceleration, and exhibits the following relationship: 1 In the formula For penetration rate, For the density of the liquid, It is the acceleration due to gravity. Permeability coefficient, Let be the fluid viscosity; permeability represents the inherent hydraulic conductivity of the fracture and is proportional to the square of the fracture width. According to the cubic law for smooth fractures, permeability and fracture aperture have the following relationship: 2 In the formula Let fracturing aperture be the factor. Since the ratio of fracturing protrusion to fracturing width affects fracturing permeability, the cubic law is further refined to: 3 In the formula, is The surface roughness of the crack, by substituting Formula 3 into Formula 1, yields... 4 From the above formula, we can see that the permeability coefficient is directly proportional to the fracture aperture and inversely proportional to the fracture surface roughness.

3. The method for evaluating the ease of coal seam water injection based on a water injection seepage model as described in claim 2, characterized in that, In step S12, the fracture aperture and permeability decrease continuously with increasing hydrostatic pressure, exhibiting a negative exponential relationship with hydrostatic pressure: 5 In the formula This represents the hydrostatic pressure, which is calculated using the following formula: 6 In the formula, The contact area between the fluid and the flow through the fissure. For the pressure difference, substituting Equation 6 into Equation 5, we can obtain... 7。 4. The method for evaluating the ease of coal seam water injection based on a water injection seepage model as described in claim 3, characterized in that, In step S13, the fractal scaling law for the capillary pore size distribution is as follows: 8 In the formula, The number of pores whose characteristic length is greater than the capillary radius. The maximum throat radius, The volume fractal dimension, Let be the capillary radius; since the curvature of the capillary bundle also conforms to fractal characteristics, the relationship between the actual length of the capillary and the straight length along the pressure gradient direction is expressed as: 9 In the formula, This is the actual length of the fluid path. The characteristic length of the capillary. Let be the fractal dimension of capillary tortuosity, where the formula for calculating the characteristic length of the capillary is: 10 In the formula, To determine the porosity, substituting Equation 10 into Equation 9, we obtain the actual length of the fluid path as: 11 Due to the complex shape of capillaries, fluid flow in coal and rock masses does not proceed in a straight line, but rather in a tortuous manner. The pore and fracture structure inside coal and rock masses is mostly composed of bundles of tortuous capillaries. Therefore, when fluid flows through these complex pores, the average capillary radius is generally used to represent the capillary radius, and its expression is: 12 In the formula, The average capillary radius, To find the minimum orifice throat radius, substituting Equation 12 into Equation 11 yields the corrected expression for the actual length of the fluid path: 13 。 5. The method for evaluating the ease of coal seam water injection based on a water injection seepage model as described in claim 4, characterized in that, In step S14, based on Darcy's law, the flow rate formula for fluid passing through a single capillary in a porous medium is: 14 In the formula, For fluid viscosity, For pressure difference, Permeability coefficient, The area of ​​the seepage channel in a single capillary tube. The characteristic length of the capillary. Let be the capillary radius. The flow rate formula is modified by replacing the characteristic length of the capillary with the actual length of the fluid path, and representing the capillary radius with the average capillary radius. The modified flow rate formula is as follows: 15 Integrating the flow rate formula yields: In the formula, This represents the total seepage flow rate.

6. The method for evaluating the ease of coal seam water injection based on a water injection seepage model as described in claim 1, characterized in that, In step S21, the Sobol sensitivity analysis method is to use the model The model is decomposed into a combination of single-parameter and multi-parameter functions. The impact of parameter variations on the model response is analyzed by calculating the influence of the sample variance of the parameters on the total variance of the model response. The steps are as follows: definition , For input parameters The value space will It can be decomposed into a combination of single-parameter functions and multi-parameter functions, that is: The total variance of the parameter sampling samples represents the degree of influence of all parameter changes on the model response. The total variance is: definition You can get: In the formula, For parameters The first-order sensitivity data represents The main impact on the output, For parameters and The second-order sensitivity data represent the cross-effect of the two parameters. The overall sensitivity is The sum of the sensitivities of each order of this parameter ,in For the parameter The sensitivity of all external design parameters to the model response.

7. The method for evaluating the ease of coal seam water injection based on a water injection seepage model as described in claim 1, characterized in that, In step S22, the method for determining each controlling factor is as follows: Let the evaluation set to be injected be... ,have There are [number] objects to be evaluated, and each object has [number] evaluation items. There are several evaluation indicators, and the value of each indicator is set as follows: ; Using the set to be evaluated as a reference, the optimal indicators from the set are selected to form the set of the highest evaluation standards, denoted as . ; Similarly, the worst-case indicators constitute the set of worst-case evaluation criteria, which are denoted as... , Comparative set constituting coal seams , As an indicator The standard range; the indicator weight is denoted as... The evaluation index of coal samples Form a set pair with the evaluation standard interval ,in For the above set of indicators to be evaluated , If the set is a comparison set composed of evaluation criteria, then the set pair In the interval The degree of connection is: In the formula, To gather connections, The object to be evaluated For the set to be of the same degree, For set-pair difference degree, The set-to-dissimilarity coefficient ranges from 100 to 100. The degree of opposition coefficient is -1.

Images