Micropore structure parameter quantitative prediction method and device, electronic equipment and medium

Abstract

The invention discloses a micro-pore structure parameter quantitative prediction method and device, electronic equipment and a medium. The method comprises the following steps: acquiring a variable fluid type of a porous rock sample in a pore fissure medium and a longitudinal and transverse wave velocity and a porosity parameter under a variable pressure condition; based on the relationship between the porosity and the pressure of the rock sample during gas saturation, calculating the fracture porosity corresponding to the rock sample; establishing an improved EIAS model by improving P1 and Q1; analyzing the influence of the aspect ratio of the hard pores in the rock on the elastic modulus of the rock, and determining the aspect ratio of the hard pores; and based on the aspect ratio of the hard pores, substituting the fracture porosity into the improved EIAS model to obtain rock micro-pore structure parameters. According to the method, the pore-fracture parameters of the pore-fracture medium are accurately solved, and a theoretical basis is provided for fine characterization of a pore-fracture medium reservoir.

Description

Technical Field

[0001] The present invention relates to the field of rock physics research, and more specifically, to a method, device, electronic device and medium for quantitatively predicting microscopic pore structure parameters. Background Art

[0002] The microscopic pore structure characteristics of complex reservoirs are not only closely related to the macroscopic physical characteristics of rocks, but also the main controlling factors for the distribution of oil and gas in reservoirs. Therefore, studying the microscopic pore structure characteristics in rocks can not only explain the enrichment law of oil and gas in reservoirs, but also clarify the basic characteristics and reservoir properties of reservoirs. The pore shape, the fluid contained in the pores, and the porosity of rocks will affect the propagation of longitudinal and transverse waves in rocks, and the elastic parameters of rocks are more affected by soft pores than hard pores. Pang Shuo et al. (2017) pointed out that the aspect ratio parameter of fractures in shale significantly affects the longitudinal and transverse wave velocities in rocks. Pressure has a significant impact on both the properties of internal fractures in rocks and the properties of fluids contained in pores. Shapiro (2003) divided the total pores in rocks into soft pores and hard pores, and further established a linear relationship between the pressure-dependent compressibility in dry rocks and soft and hard pores. Pervukhina (2010) conducted ultrasonic experimental measurements on dry sandstones, verified the exponential relationship between soft pores and pressure in rocks, obtained the soft porosity by fitting the elastic compressibility, and compared the results of static experiments to demonstrate the effectiveness of the proposed pressure sensitivity model. Fortin et al. (2007) used an equivalent medium model to simulate the variation characteristics of fracture density and fracture aspect ratio with effective pressure in dry and fluid-saturated rock samples. The results showed that when the critical effective pressure was exceeded, the fracture density was higher in the case of fully saturated fluids. And for fluid-saturated rocks, when the effective pressure increased from 0 MPa to 60 MPa, the average fracture aspect ratio increased exponentially from 0.02 to 0.5, which was interpreted as the closure of the thinnest fractures (soft pores). When the critical effective pressure was reached, due to particle breakage and the formation of new fractures inside the rock, the average fracture aspect ratio decreased rapidly. As the confining pressure increased, fractures with lower pore aspect ratios closed first, and fractures with higher aspect ratios thinned until they closed (Zhang et al., 2019). Izumotani et al. (2013) found that the aspect ratio of soft pores (fractures) in rocks was less than that of rocks under atmospheric pressure at high confining pressures. et al. (1976) pointed out that when pressure is applied, soft pores will become thinner, while the volume of hard pores will decrease. Sun et al. (1997) studied the dynamic deformation process of pores and fractures in Casco granite with increasing confining pressure: first, the increase in confining pressure will cause the original fractures to collapse and the fractures with smaller aspect ratios to close (the average fracture aspect ratio increases at this time), followed by the thinning of some pores in the rock, followed by the complete closure of the original fractures, and finally the formation of new fractures (all three steps lead to a decrease in the average fracture aspect ratio). In summary, the microscopic pore structure characteristics in media with pores and fractures (porous fracture media) are the key research directions of current oil and gas exploration, and the laws of influence by pressure and fluid in the pores still need to be further studied. Based on the study of the influence of confining pressure on bulk modulus in dry rocks, Walsh (1965) found that fractures have a more significant effect on the elastic modulus than intergranular pores (hard pores), and quantitatively predicted the fracture porosity parameters. Cheng and (1979) used the KT equivalent medium model to study the microscopic pore structure characteristics of rocks and found that the porosity of rocks with different pore aspect ratios was also different. Tran et al. (2008) improved Cheng and others by introducing the DEM equivalent medium model. The method has multiple solutions because the simulation results depend on the input prior model. The present invention intends to analyze the influence of the aspect ratio of hard pores (intergranular pores) in rocks on the elastic modulus of rocks based on the improved equivalent embedded volume stress average (EIAS) model to determine the appropriate aspect ratio of hard pores (intergranular pores). In addition, based on rock physics experiments under variable pressure conditions, the fracture porosity parameters of rocks are obtained and substituted into the model to effectively reduce the multiple solutions of the prediction of microscopic pore structure parameters, and finally obtain reliable rock microscopic pore structure parameters, providing theoretical support for the fine prediction of porous fracture medium reservoirs.

[0003] There are many rock physics models and theories that analyze the relationship between the microscopic pore structure properties of rocks and elastic parameters. In many studies, the microscopic pore structure in porous and fractured media is divided into hard pores (such as intergranular pores) and soft pores (microcracks). However, there is a lack of discussion on the influence of the aspect ratio of hard pores on the elastic properties of rocks during the simulation process, and the simulation results have the problem of multiple solutions.

[0004] At present, a quantitative prediction method for microscopic pore structure parameters needs to be developed.

[0005] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a method, apparatus, electronic device and medium for quantitatively predicting microscopic pore structure parameters, accurately obtaining pore-fracture parameters of pore-fracture media, and providing a theoretical basis for the fine characterization of pore-fracture media reservoirs.

[0007] In a first aspect, an embodiment of the present disclosure provides a method for quantitatively predicting microscopic pore structure parameters, including: obtaining the longitudinal and transverse wave velocities and porosity parameters of a porous rock sample in pore-fracture media under variable fluid types and variable pressure conditions; calculating the fracture porosity corresponding to the rock sample based on the relationship between the porosity and pressure of the rock sample when saturated with gas; establishing an improved EIAS model by improving the spherical hard pore shape factor; analyzing the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of the hard pores; and substituting the fracture porosity into the improved EIAS model based on the aspect ratio of the hard pores to obtain the microscopic pore structure parameters of the rock.

[0008] In a second aspect, an embodiment of the present disclosure further provides an apparatus for quantitatively predicting microscopic pore structure parameters, including: a parameter acquisition module that acquires the longitudinal and transverse wave velocities and porosity parameters of a porous rock sample in pore-fracture media under variable fluid types and variable pressure conditions; a fracture porosity calculation module that calculates the fracture porosity corresponding to the rock sample based on the relationship between the porosity and pressure of the rock sample when saturated with gas; an improved modeling module that establishes an improved EIAS model by improving 1 P 1 and Q; an aspect ratio calculation module that analyzes the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of the hard pores; and a structure parameter calculation module that substitutes the fracture porosity into the improved EIAS model based on the aspect ratio of the hard pores to obtain the microscopic pore structure parameters of the rock.

[0009] In a third aspect, an embodiment of the present disclosure further provides an electronic device, which includes:

[0010] a memory storing executable instructions;

[0011] a processor that runs the executable instructions in the memory to implement the method for quantitatively predicting microscopic pore structure parameters.

[0012] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, it implements the method for quantitatively predicting microscopic pore structure parameters.

[0013] The methods and apparatuses of the present invention have other characteristics and advantages, which will be apparent from the accompanying drawings incorporated herein and the subsequent detailed description, or will be described in detail in the accompanying drawings incorporated herein and the subsequent detailed description. These accompanying drawings and detailed description together are used to explain the specific principles of the present invention. Description of the Drawings

[0014] The above and other objects, features, and advantages of the present invention will become more apparent by describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, in which, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0015] Figure 1 A flowchart showing the steps of a method for quantitatively predicting micro-pore structure parameters according to an embodiment of the present invention is shown.

[0016] Figure 2 A schematic diagram showing the relationship between the total porosity, hard porosity, fracture porosity, and pressure difference in sample SA4 according to an embodiment of the present invention is shown.

[0017] Figure 3 A crossplot showing the relationship between the fracture porosity and pressure difference of a fractured medium porous dense sandstone sample according to an embodiment of the present invention is shown.

[0018] Figure 4 A schematic diagram showing the relationship between the bulk modulus and shear modulus of sample SA4 and pressure difference of a rock under saturated water conditions according to an embodiment of the present invention is shown.

[0019] Figure 5 A schematic diagram showing the relationship between (a) fracture aspect ratio, (b) fracture porosity, (c) fracture density, and total porosity of a pore-fracture medium porous dense sandstone according to an embodiment of the present invention is shown.

[0020] Figure 6 A block diagram showing a device for quantitatively predicting micro-pore structure parameters according to an embodiment of the present invention is shown.

[0021] Description of the Reference Numerals in the Drawings:

[0022] 201, parameter acquisition module; 202, fracture porosity calculation module; 203, improved modeling module; 204, aspect ratio calculation module; 205, structure parameter calculation module. Detailed Embodiments

[0023] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0024] To facilitate the understanding of the solutions and their effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are only for facilitating the understanding of the present invention, and no specific details are intended to limit the present invention in any way.

[0025] Example 1

[0026] Figure 1 The flowchart showing the steps of the method for quantitatively predicting the microscopic pore structure parameters according to an embodiment of the present invention is presented.

[0027] As Figure 1 shown, the method for quantitatively predicting the microscopic pore structure parameters includes: Step 101, obtaining the P-wave and S-wave velocities and porosity parameters of a porous rock sample in a pore-fracture medium under variable fluid types and variable pressure conditions; Step 102, calculating the fracture porosity corresponding to the rock sample based on the relationship between the porosity and pressure of the rock sample when it is saturated with air; Step 103, establishing an improved EIAS model by improving P 1 and Q 1 ; Step 104, analyzing the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of the hard pores; Step 105, substituting the fracture porosity into the improved EIAS model based on the aspect ratio of the hard pores to obtain the microscopic pore structure parameters of the rock.

[0028] In one example, the improved spherical hard pore shape factor is:

[0029]

[0030]

[0031] where P 1 , Q 1 are the spherical hard pore shape factors, F 6 =1 + A[1 + ff - R(ff + θ)] + B(1 - θ)(3 - 4R), F 9 =A[(R - 1)ff - Rθ] + Bθ(3 - 4R),

[0032] In one example, the improved EIAS model is:

[0033]

[0034]

[0035]

[0036]

[0037] Among them, P 2 and Q 2 are coin-shaped crack shape factors, γ = (1 - c)P 1 + cP 2 , χ = (1 - c)Q 1 + cQ 2 , χ 0 = (1 - c)Q 01 + cQ 02 , Q 01 = Q 1 , P 1 and Q 1 correspond to spherical hard pores, and P 2 and Q 2 correspond to coin-shaped cracks.

[0038] In one example, to analyze the influence of the aspect ratio of hard pores in rock on the elastic modulus of the rock, determining the aspect ratio of hard pores includes:

[0039] Obtaining the relationship between the crack aspect ratio and the crack volume ratio and pressure, and then analyzing the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of hard pores. Among them, the relationship between the crack aspect ratio and the crack volume ratio and pressure is:

[0040] a = a 0 exp[-(p - p 0 ) / p a

[0041] c = c 0 exp[-(p - p 0 ) / p c

[0042] Among them, p is the pressure difference, the difference between the confining pressure and the pore pressure, a is the aspect ratio of hard pores, c is the crack volume ratio, a 0 , c 0 are the values of a and c when p = p 0 , and p a and p c are empirical parameters.

[0043] In one example, the microscopic pore structure parameters of the rock include the crack density and the crack aspect ratio.​​

[0044] In one example, obtaining the microscopic pore structure parameters of the rock includes:

[0045] Substitute the fracture porosity into the improved EIAS model, use the fracture aspect ratio as a free parameter, fit the low-frequency bulk modulus and the high-frequency bulk and shear moduli, and obtain the optimal fracture aspect ratio through an optimization method.

[0046] In one example, the objective function of the optimization method is:

[0047]

[0048] where ε is the error, and respectively represent the experimentally measured bulk and shear moduli, and are respectively the bulk and shear moduli at the main frequency of 0.5 MHz and the bulk modulus at 0 Hz predicted by using the improved EIAS model, is the low-frequency bulk modulus predicted by using the Gassmann theory.

[0049] Specifically, (1) Based on rock physics experiments, obtain the P-wave and S-wave velocities and porosity parameters of the porous rock samples in the pore-fracture medium under the conditions of variable fluid types (gas-saturated and water-saturated) and variable pressures.

[0050] The present invention uses a gas adsorption state ultrasonic velocity test system, the test method is the ultrasonic pulse transmission method, the saturated fluids are gas-saturated and water-saturated respectively, the temperature is 20 °C, the confining pressures are different values of 5, 10, 20, 30, 40, 50 and 60 MPa respectively (where the pore pressure is 15 MPa, however, due to the small pressure, the pore pressure of 15 MPa cannot be set for the confining pressures of 5 and 10 MPa, so the pore pressure is set to 4 MPa), the P-wave and S-wave waveforms of each sample are collected at the main frequency of 0.5 MHz, and the P-wave and S-wave velocities are calculated by extracting the first arrivals of the waveforms. In addition, the corresponding porosities and permeabilities are also obtained through a confining pressure porosity and permeability measuring instrument when the confining pressures are 20, 30, 40, 50 and 60 MPa (the pore pressure is 15 MPa).

[0051] (2) Based on the relationship between the porosity and pressure of the rock samples when gas-saturated, obtain the corresponding fracture porosity of the rock samples.

[0052] With the increase of confining pressure, fractures (thin fractures with a small aspect ratio) first start to deform and close until all tend to close. At this time, when the pressure continues to increase, the decrease in rock porosity mainly comes from the compression of hard pores. The relationship between hard porosity and confining pressure is obtained by linearly extrapolating the relationship between total porosity and confining pressure at high pressure. Finally, the fracture porosity can be estimated by the difference between the total porosity and the hard porosity. Based on the above analysis idea, the relationship between the fracture porosity and confining pressure of each sample is obtained. The fracture porosity decreases rapidly at low pressure and slowly in the high-pressure section.

[0053] (3) Improve the parameters that characterize the geometric shape of hard pores in the improved equivalent inclusion average stress (EIAS) model, so that the EIAS model can consider the case of hard pores with any pore aspect ratio in the rock, in order to obtain the improved EIAS model.

[0054] The EIAS model approximates the microscopic pore structure of porous rock as a combination of spherical hard pores and coin-shaped fractures, and its porosity is φ (φ = φ s + φ c ), φ s is the porosity of hard pores, and φ c is the porosity of soft pores). And the aspect ratio of the fracture is a. In addition, c is the fracture volume ratio (c = φ c / φ), and this parameter is related to the properties of soft pores and hard pores. The high-frequency bulk and shear moduli in the rock are:

[0055]

[0056]

[0057] where γ = (1 - c)P 1 + cP 2 , χ = (1 - c)Q 1 + cQ 2 , (3)

[0058]

[0059]

[0060]

[0061] where P 1 and Q 1 correspond to spherical hard pores, while P 2 and Q 2 correspond to coin-shaped fractures.

[0062] When the fluid pressure reaches equilibrium in the entire pore space, the low-frequency effective modulus is:

[0063]

[0064]

[0065] Here, γ 0 =(1 - c)P 01 + cP 02 , χ 0 =(1 - c)Q 01 + cQ 02 , (9)

[0066]

[0067]

[0068] The present invention considers improving the 1 values of P 1 and Q 1 so that they are applicable to oblate spheroids with any aspect ratio. The expressions for P 1 and Q

[0069]

[0070] are as follows: iijj and T ijij The expressions for T

[0071]

[0072] are as follows: F 6 = 1 + A[1 + ff - R(ff + θ)] + B(1 - θ)(3 - 4R), F 9 = A[(R - 1)ff - Rθ] + Bθ(3 - 4R)

[0073] where A, B, and R are

[0074] Here, the solid is regarded as a mixture of N phases, where i refers to the i-th material. The functions θ and ff for oblate spheroids with a < 1 are expressed as follows:

[0075]

[0076] Thus, the present invention improves the parameters characterizing the hard pore geometry in the equivalent embedded inclusion stress averaging (EIAS) model, enabling the EIAS model to consider the case of hard pores with any pore aspect ratio in rocks, so as to obtain an improved EIAS model.

[0077] (4) Analyze the influence law of the aspect ratio of hard pores (intergranular pores) in the rock on the elastic modulus of the rock to determine the appropriate aspect ratio of hard pores (intergranular pores).

[0078] The fracture density will decrease exponentially with the increase of the pressure difference:

[0079]

[0080] where Γ 0 is the initial fracture density when the pressure difference is 0, is the compressibility (the unit of the pressure difference is MPa).

[0081] The relationship between the fracture porosity and the pressure is as follows:

[0082] φ c = φ c0 exp(-θ c C drs P), (18)

[0083]

[0084] where φ c is the fracture porosity, φ c0 is the fracture porosity when the pressure is 0, C dr = 1 / K dr is the volume compressibility of the dry rock skeleton, K dr is the bulk modulus of the dry rock skeleton, C drs is the volume compressibility of the dry rock when the soft pores are completely closed, and P refers to the pressure (the unit is MPa). Therefore, the relationship between the fracture volume ratio and the pressure can be further obtained as follows:

[0085]

[0086] Since

[0087] So,

[0088] Substitute Equation (20) and Equation (21) into Equation (22) to obtain:

[0089]

[0090] As shown by the above formula, both a and c have an exponential relationship with the pressure. When P in the above formula is regarded as a variable and other parameters are regarded as constants, the present invention gives a new set of expressions for the exponential relationship between a, c and the pressure, as follows:

[0091] a = a 0exp[-(p - p 0 ) / pa], c = c 0 exp[-(p - p 0 ) / pc], (24)

[0092] At this time, p is the differential pressure (the difference between the confining pressure and the pore pressure), a 0 and c 0 are the values of a and c when p = p 0 (p 0 in this invention is 5 MPa), p a and p c are empirical parameters.

[0093] Under high - frequency (i.e., non - relaxation) conditions, through experimental tests, the bulk modulus and shear modulus of the rock are obtained, and the calculation formulas are as follows:

[0094]

[0095] First, consider the case of p = p 0 = 5 MPa, and further obtain a = a 0 and c = c 0 . According to the basic properties of the rock, a reasonable range of fracture geometry parameters is given: a = [0, 0.01] and c = [0, 0.3], and further satisfy:

[0096]

[0097] Here, ε 0 is the error, and are the bulk and shear moduli predicted by the improved EIAS model, and K exp (p 0 ) and μ exp (p 0 ) are the bulk and shear moduli obtained from experimental tests when the pressure is p 0 .

[0098] Then, substitute the obtained a 0 and c 0 into formula (24), and by assuming that p a and p c are in the range of [0, 200] MPa to satisfy the following optimization formula, further obtain p a and p c :

[0099]

[0100] Here, ε is the error, and are the volume and shear modulus under each pressure state predicted by the EIAS model, where i takes values in the range from 0 to 5, and the corresponding pressures are 5, 15, 25, 35, 45 MPa, and K exp (p i ) and μ exp (p i ) refer to the volume and shear modulus measured experimentally at pressure p i . Thus, a and c can be obtained from formula (24).

[0101] Assume that the hard pores are spherical, i.e., a s = 1, and the cases of a s = 0.8, 0.5, 0.4, 0.3, 0.22, and 0.2 are also analyzed. Figure 5 Shows the comparison results between the experimental test values (solid dots) and the model prediction values (solid lines) of the water-saturated SA4 samples. Generally, the relationship between the volume and shear modulus and the pressure difference shows that when 0.3 ≤ a s ≤ 1, the model prediction results are very similar and show good agreement with the experimental data. For a s = 0.22 and 0.2, the model prediction curves deviate significantly from the experimental test values. The above results show that the spherical assumption of hard pores in the pore-fracture medium is reasonable and acceptable.

[0102] (5) Substitute the fracture porosity obtained from the experiment into the improved EIAS model to reduce the non-uniqueness problem of the prediction of microscopic pore structure parameters, so as to obtain reliable rock microscopic pore structure parameters (fracture density and fracture aspect ratio).

[0103] In order to reduce the non-uniqueness problem of the prediction of microscopic pore structure parameters and obtain reliable rock microscopic pore structure parameters (fracture density and fracture aspect ratio) in the pore-fracture medium reservoir, based on step (4), this invention demonstrates that the spherical assumption of hard pores in the pore-fracture medium is reasonable. Therefore, in the subsequent simulation process, the fracture aspect ratio of the hard pores is set to 1. Substitute the fracture porosity obtained by the linear extrapolation method in step (2) into the model. At this time, with the fracture aspect ratio a as the free parameter, fit the low-frequency bulk modulus and the high-frequency volume and shear modulus, and thus the optimal fracture aspect ratio can be obtained. Among them, the volume and shear modulus of the gas-saturated and water-saturated rocks at high frequencies are measured by rock physics experiments, and the low-frequency bulk modulus of the water-saturated rock is predicted based on the measured values of the gas-saturated rock by introducing the Gassmann fluid substitution equation (Gassmann, 1951). The objective function of the optimization problem is:

[0104]

[0105] where ε is the error, and respectively represent the experimentally measured volume and shear modulus, and are respectively the volume and shear modulus at the main frequency of 0.5 MHz and the bulk modulus at 0 Hz predicted by the improved EIAS model, and is the low-frequency bulk modulus predicted by the Gassmann theory.

[0106] The present invention improves the equivalent embedded body stress averaging (EIAS) model to analyze the influence law of the aspect ratio of hard pores (intergranular pores) in rocks on the elastic modulus of rocks, so as to determine the appropriate aspect ratio of hard pores (intergranular pores). In addition, based on the rock physics experiment under variable pressure conditions, the fracture porosity parameter of the rock is obtained and substituted into the model to effectively reduce the multi-solution problem of the prediction of microscopic pore structure parameters, and finally reliable microscopic pore structure parameters of the rock are obtained.

[0107] Example 2

[0108] The present invention also provides a device for quantitatively predicting microscopic pore structure parameters, including:

[0109] a parameter acquisition module, which acquires the P-wave and S-wave velocities and porosity parameters of a porous rock sample in a pore-fracture medium under variable fluid types and variable pressure conditions;

[0110] a fracture porosity calculation module, which calculates the fracture porosity corresponding to the rock sample based on the relationship between the porosity and pressure of the rock sample when saturated with gas;

[0111] an improved modeling module, which establishes an improved EIAS model by improving P 1 and Q 1 ;

[0112] an aspect ratio calculation module, which analyzes the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock and determines the aspect ratio of the hard pores;

[0113] a structure parameter calculation module, which substitutes the fracture porosity into the improved EIAS model based on the aspect ratio of the hard pores to obtain the microscopic pore structure parameters of the rock.

[0114] In one example, the improved spherical hard pore shape factor is:

[0115]

[0116]

[0117] In one example, the improved EIAS model is:

[0118]

[0119]

[0120]

[0121]

[0122] In one example, analyzing the aspect ratio of hard pores in a rock on the elastic modulus of the rock, determining the aspect ratio of the hard pores includes:

[0123] Obtaining the relationship between the fracture aspect ratio and the fracture volume ratio and pressure, and then analyzing the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of the hard pores, where the relationship between the fracture aspect ratio and the fracture volume ratio and pressure is:

[0124] a = a 0 exp[-(p - p 0 ) / p a

[0125] c = c 0 exp[-(p - p 0 ) / p c

[0126] where p is the pressure difference, the difference between the confining pressure and the pore pressure, a is the aspect ratio of the hard pores, c is the fracture volume ratio, a 0 , c 0 are the values of a and c when p = p 0 , and p a and p c are empirical parameters.

[0127] In one example, the rock micro-pore structure parameters include the fracture density and the fracture aspect ratio.

[0128] In one example, obtaining the rock micro-pore structure parameters includes:

[0129] Substituting the fracture porosity into the improved EIAS model, using the fracture aspect ratio as a free parameter, fitting the low-frequency bulk modulus and the high-frequency bulk and shear moduli, and obtaining the optimal fracture aspect ratio through an optimization method.

[0130] In one example, the objective function of the optimization method is:

[0131]

[0132] where ε is the error, and respectively represent the experimentally measured bulk and shear moduli, and ​​They are the volume, shear modulus at the main frequency of 0.5 MHz, and bulk modulus at 0 Hz predicted by using the improved EIAS model respectively. It is the low-frequency bulk modulus predicted by using the Gassmann theory.

[0133] Specifically, (1) Based on rock physics experiments, the P-wave velocity, S-wave velocity, and porosity parameters of porous rock samples in pore-fracture media are obtained under the conditions of varying fluid types (gas-saturated and water-saturated) and varying pressures.

[0134] The present invention uses a gas adsorption state ultrasonic velocity test system. The test method is the ultrasonic pulse transmission method. The saturated fluids are gas-saturated and water-saturated respectively, the temperature is 20 °C, and the confining pressures are different values of 5, 10, 20, 30, 40, 50, and 60 MPa (where the pore pressure is 15 MPa. However, due to the small pressure, the pore pressure of 15 MPa cannot be set for the confining pressures of 5 and 10 MPa, so the pore pressure is set to 4 MPa). The P-wave and S-wave waveforms of each sample are collected at the main frequency of 0.5 MHz, and the P-wave velocity and S-wave velocity are calculated by extracting the first arrival of the waveforms. In addition, the corresponding porosity and permeability are also obtained through a confining pressure porosity and permeability measuring instrument when the confining pressures are 20, 30, 40, 50, and 60 MPa (pore pressure is 15 MPa).

[0135] (2) Based on the relationship between the porosity and pressure of the rock sample when it is gas-saturated, the corresponding fracture porosity of the rock sample is obtained.

[0136] As the confining pressure increases, the fractures (thin fractures with a small aspect ratio) first start to deform and close until all tend to close. At this time, when the pressure continues to increase, the decrease in the rock porosity mainly comes from the compression of the hard pores. The relationship between the hard porosity and the confining pressure is obtained by linearly extrapolating the relationship between the total porosity and the confining pressure at high pressures. Finally, the fracture porosity can be estimated by the difference between the total porosity and the hard porosity. Based on the above analysis idea, the relationship between the fracture porosity and the confining pressure of each sample is obtained. The fracture porosity decreases rapidly at low pressures and slowly in the high-pressure section.

[0137] (3) Modify the parameters in the improved equivalent inclusion stress averaging (EIAS) model that characterize the geometry of the hard pores, so that the EIAS model can consider the case of hard pores with any pore aspect ratio in the rock to obtain the improved EIAS model.

[0138] The EIAS model approximates the microscopic pore structure of porous rock as a combination of spherical hard pores and coin-shaped fractures, and its porosity is φ (φ = φ s + φ c ), φ s is the porosity of the hard pores, φ cis the porosity of the soft pores). And the aspect ratio of the crack is a. In addition, c is the crack volume ratio (c = φ c / φ), and this parameter is related to the properties of soft pores and hard pores. The high-frequency bulk and shear moduli of the rock are:

[0139]

[0140]

[0141] where γ = (1 - c)P 1 + cP 2 , χ = (1 - c)Q 1 + cQ 2 , (3)

[0142]

[0143]

[0144]

[0145] where P 1 and Q 1 correspond to spherical hard pores, while P 2 and Q 2 correspond to coin-shaped cracks.

[0146] When the fluid pressure reaches equilibrium in the entire pore space, the low-frequency effective modulus is:

[0147]

[0148]

[0149] Here, γ 0 = (1 - c)P 01 + cP 02 , χ 0 = (1 - c)Q 01 + cQ 02 , (9)

[0150]

[0151] Q 01 = Q 1 ,

[0152] The present invention considers improving the P 1 and Q 1 values to make them applicable to oblate spheres with any aspect ratio. The expressions of P 1 and Q 1 are as follows:

[0153]

[0154] wherein T iijj and T ijij are expressed as follows:

[0155]

[0156] wherein, F 6 = 1 + A[1 + ff - R(ff + θ)] + B(1 - θ)(3 - 4R), F 9 = A[(R - 1)ff - Rθ] + Bθ(3 - 4R)

[0157] where A, B and R are

[0158] Here, the solid is regarded as a mixture of N phases, where i refers to the i-th material. The functions θ and ff for oblate spheroids with a < 1 are expressed as follows:

[0159]

[0160] Thus, the present invention improves the parameters characterizing the hard pore geometry in the equivalent inclusion stress averaging (EIAS) model, enabling the EIAS model to consider the case of hard pores with arbitrary pore aspect ratios in rocks, so as to obtain an improved EIAS model.

[0161] (4) Analyze the influence law of the aspect ratio of hard pores (intergranular pores) in rocks on the elastic modulus of rocks to determine an appropriate aspect ratio of hard pores (intergranular pores).

[0162] The fracture density decreases exponentially with the increase of the pressure difference:

[0163]

[0164] wherein, Γ 0 is the initial fracture density when the pressure difference is 0, is the compressibility (the unit of the pressure difference is MPa).

[0165] The relationship between the fracture porosity and the pressure is as follows:

[0166] φ c = φ c0 exp(-θ c C drs P), (18)

[0167]

[0168] where φ c is the fracture porosity, φ c0 is the fracture porosity at zero pressure, C dr = 1 / K dr is the bulk compressibility of the dry rock skeleton, K dr is the bulk modulus of the dry rock skeleton, C drs is the bulk compressibility of the dry rock when the soft pores are completely closed, and P refers to the pressure (in MPa). Therefore, the relationship between the fracture volume ratio and the pressure can be further obtained as follows:

[0169]

[0170] Since

[0171] So,

[0172] Substituting equations (20) and (21) into equation (22) gives:

[0173]

[0174] The above equations show that both a and c have an exponential relationship with the pressure. When P in the above equations is regarded as a variable and other parameters as constants, the present invention gives a new set of expressions for the exponential relationship between a, c and the pressure, as follows:

[0175] a = a 0 exp[-(p - p 0 ) / pa], c = c 0 exp[-(p - p 0 ) / pc], (24)

[0176] At this time, p is the differential pressure (the difference between the confining pressure and the pore pressure), a 0 and c 0 are the values of a and c when p = p 0 (p 0 in the present invention is 5 MPa), p a and p c are empirical parameters.

[0177] Under high-frequency (i.e., non-relaxation) conditions, through experimental tests, the bulk modulus and shear modulus of the rock are obtained, and the calculation formulas are as follows:

[0178]

[0179] First, consider p = p 0For the case of = 5 MPa, a = a is further obtained 0 and c = c 0 , based on the basic properties of the rock, a reasonable range of fracture geometric parameters is given: a = [0, 0.01] and c = [0, 0.3], and further satisfy:

[0180]

[0181] Here, ε 0 is the error, and are the volume and shear moduli predicted by the improved EIAS model, and K exp (p 0 ) and μ exp (p 0 ) are the volume and shear moduli obtained from experimental tests at a pressure of p 0 respectively.

[0182] Then, the obtained a 0 and c 0 are substituted into formula (24). By assuming that p a and p c are in the range of [0, 200] MPa to satisfy the following optimization formula, p a and p c are further obtained:

[0183]

[0184] Here, ε is the error, and are the volume and shear moduli at each pressure state predicted by the EIAS model. At this time, i takes values in the range from 0 to 5, and the corresponding pressures are 5, 15, 25, 35, 45 MPa, and K exp (p i ) and μ exp (p i ) refer to the volume and shear moduli measured experimentally at a pressure of p i . Thus, a and c can be obtained from formula (24).

[0185] Assume that the hard pores are spherical, i.e., a s = 1, and the cases of a s = 0.8, 0.5, 0.4, 0.3, 0.22, and 0.2 are also analyzed. Figure 5 Shows the comparison results between the experimental test values (solid dots) and the model prediction values (solid lines) of the water-saturated SA4 samples. Overall, the relationship between the volume and shear moduli and the pressure difference is that at 0.3 ≤ a sWhen ≤1, the results predicted by the model are very similar and show good agreement with the experimental data. For a s = 0.22 and 0.2, the curves predicted by the model deviate significantly from the experimental test values. The above results show that the spherical assumption of hard pores in the pore-fracture medium is reasonable and acceptable.

[0186] (5) Substitute the fracture porosity obtained from the experiment into the improved EIAS model to reduce the problem of multiple solutions in the prediction of microscopic pore structure parameters, so as to obtain reliable rock microscopic pore structure parameters (fracture density and fracture aspect ratio).

[0187] In order to reduce the problem of multiple solutions in the prediction of microscopic pore structure parameters and obtain reliable rock microscopic pore structure parameters (fracture density and fracture aspect ratio) in the pore-fracture medium reservoir, based on step (4), the present invention demonstrates that the spherical assumption of hard pores in the pore-fracture medium is reasonable. Therefore, in the subsequent simulation process, the fracture aspect ratio of the hard pores is set to 1. Substitute the fracture porosity obtained by the linear extrapolation method in step (2) into the model. At this time, with the fracture aspect ratio a as a free parameter, fit the low-frequency bulk modulus and the high-frequency bulk and shear moduli. Thus, the optimal fracture aspect ratio can be obtained. Among them, the bulk and shear moduli of the gas-saturated and water-saturated rocks at high frequencies are measured through rock physics experiments, and the bulk modulus of the water-saturated rock at low frequencies is predicted by introducing the Gassmann fluid substitution equation (Gassmann, 1951) based on the measured values of the gas-saturated rock. The objective function of the optimization problem is:

[0188]

[0189] where ε is the error, and respectively represent the experimentally measured bulk and shear moduli, and are the bulk and shear moduli at the main frequency of 0.5 MHz and the bulk modulus at 0 Hz predicted by using the improved EIAS model respectively, and is the low-frequency bulk modulus predicted by using the Gassmann theory.

[0190] Example 3

[0191] In this invention, five pieces of porous tight sandstone from the Yanchang Formation in a certain basin were selected to conduct ultrasonic rock physics experiments. A gas adsorption state ultrasonic velocity test system was used, and the test method was the ultrasonic pulse transmission method. The saturated fluids were air-saturated and water-saturated respectively, the temperature was 20 °C, and the confining pressures were different values of 5, 10, 20, 30, 40, 50, and 60 MPa (where the pore pressure was 15 MPa. However, since the confining pressures of 5 and 10 MPa were too low to set a pore pressure of 15 MPa, the pore pressure was set to 4 MPa). At a main frequency of 0.5 MHz, the longitudinal and transverse wave waveforms of each sample were collected, and the longitudinal and transverse wave velocities were calculated by extracting the first arrivals of the waveforms. In addition, the corresponding porosity and permeability were also obtained through a confining pressure porosity and permeability measuring instrument at confining pressures of 20, 30, 40, 50, and 60 MPa (with a pore pressure of 15 MPa).

[0192] Figure 2 FIG. shows a schematic diagram of the relationship between the total porosity, hard porosity, fracture porosity, and pressure difference in sample SA4 according to an embodiment of the present invention.

[0193] Figure 3 FIG. shows a crossplot of the relationship between the fracture porosity and pressure difference of a fractured medium porous tight sandstone sample according to an embodiment of the present invention.

[0194] As the confining pressure increases, fractures (thin fractures with a small aspect ratio) first begin to deform and close until all tend to close. At this time, when the pressure continues to increase, the decrease in rock porosity mainly comes from the compression of hard pores. The relationship between the hard porosity and the confining pressure is obtained by linearly extrapolating the relationship between the total porosity and the confining pressure at high pressures. Finally, the fracture porosity can be estimated by the difference between the total porosity and the hard porosity. Figure 2 shows the relationship between the total porosity, hard porosity, and fracture porosity of sample SA4 and the confining pressure. Based on the above analysis idea, the relationship between the fracture porosity and the confining pressure of each sample is obtained, and the results are as Figure 3 shown. At low pressures, the fracture porosity decreases rapidly, and at high pressure sections, the decrease rate is gentle.

[0195] Improve the parameter that describes the geometric shape of hard pores in the improved equivalent inclusion stress averaging (EIAS) model, so that the EIAS model can consider the case of hard pores with any pore aspect ratio in the rock, in order to obtain an improved EIAS model.

[0196] This invention improves the parameter that describes the geometric shape of hard pores in the improved equivalent inclusion stress averaging (EIAS) model, so that the EIAS model can consider the case of hard pores with any pore aspect ratio in the rock, in order to obtain an improved EIAS model.

[0197] Analyze the influence law of the aspect ratio of hard pores (intergranular pores) in rocks on the elastic modulus of rocks to determine the appropriate aspect ratio of hard pores (intergranular pores).

[0198] Figure 4 The schematic diagram shows the relationship between the bulk modulus and shear modulus of the SA4 sample of rock under saturated water conditions and the pressure difference according to an embodiment of the present invention.

[0199] When determining the aspect ratio of hard pores, it is assumed that the hard pores are spherical, that is, a s = 1, and then establish the relationship between the aspect ratio of fractures and the fracture volume ratio and pressure. In this way, the aspect ratio of fractures and the fracture volume ratio at each pressure can be obtained. Substitute the aspect ratio of hard pores and the aspect ratio of fractures and the fracture volume ratio at each pressure obtained here into the model to calculate the volume shear modulus predicted by the model and compare it with the experimental test value. In this way, the cases where the aspect ratio of hard pores is 1, 0.8, 0.5, 0.4, 0.3, 0.22, and 0.2 are repeatedly analyzed. Figure 4 The comparison results between the experimental test values (solid dots) and the model prediction values (solid lines) of the saturated water SA4 sample are shown. Generally speaking, for the relationship between the volume and shear modulus and the pressure difference, when 0.3 ≤ a s ≤ 1, the results predicted by the model are very similar and show good consistency with the experimental data. For a s = 0.22 and 0.2, the curves predicted by the model deviate significantly from the experimental test values. The above results show that the spherical assumption of hard pores in the pore-fracture medium is reasonable and acceptable.

[0200] Substitute the fracture porosity obtained from the experiment into the improved EIAS model to reduce the problem of multiple solutions in the prediction of microscopic pore structure parameters, so as to obtain reliable microscopic pore structure parameters of rocks (fracture density and fracture aspect ratio).

[0201] In order to reduce the problem of multiple solutions in the prediction of microscopic pore structure parameters and obtain reliable microscopic pore structure parameters of rocks (fracture density and fracture aspect ratio) in the pore-fracture medium reservoir, the present invention demonstrates that the spherical assumption of hard pores in the pore-fracture medium is reasonable. Therefore, in the next simulation process, the fracture aspect ratio of hard pores is set to 1. Substitute the fracture porosity obtained by the linear extrapolation method into the model. At this time, with the fracture aspect ratio a as a free parameter, fit the low-frequency bulk modulus and the high-frequency volume and shear modulus. Thus, the optimal fracture aspect ratio can be obtained. Among them, the volume and shear moduli of saturated gas and saturated water rocks at high frequencies are measured by rock physics experiments, and the bulk modulus of saturated water rocks at low frequencies is predicted by introducing the Gassmann fluid substitution equation based on the measured values of saturated gas rocks.

[0202] Figure 5Schematic diagram showing the relationship between (a) fracture aspect ratio, (b) fracture porosity, (c) fracture density, and total porosity of a porous dense sandstone in a pore-fracture medium according to an embodiment of the present invention.

[0203] Based on the above method, the present invention simulated the fracture aspect ratio and fracture density ρ of 5 samples in a saturated water state under a confining pressure of 20 MPa and a pore pressure of 15 MPa. c = 3φc / 4πa. The results are as Figure 5 shown. Both the fracture aspect ratio and fracture porosity show an overall downward trend as the total porosity increases. The greater the pressure, the smaller the fracture aspect ratio, fracture porosity, and total porosity. Also, the better the fitting relationship when the pressure is smaller, which may be because the soft pores in the rock are still relatively developed at lower pressures, and the soft pores gradually close as the pressure increases. The fracture density shows an overall increasing trend as the total porosity increases, but the fitting coefficient is very low, indicating that the relationship between fracture density and total porosity is not obvious.

[0204] Example 4

[0205] Figure 6 Block diagram showing a device for quantitatively predicting microscopic pore structure parameters according to an embodiment of the present invention.

[0206] As Figure 6 shown, the device for quantitatively predicting microscopic pore structure parameters includes:

[0207] A parameter acquisition module 201 for acquiring the P-wave and S-wave velocities and porosity parameters of a porous rock sample in a pore-fracture medium under variable fluid types and variable pressure conditions;

[0208] A fracture porosity calculation module 202 for calculating the fracture porosity corresponding to the rock sample based on the relationship between the porosity and pressure of the rock sample when it is saturated with gas;

[0209] An improved modeling module 203 for establishing an improved EIAS model by improving P 1 and Q 1 ;

[0210] An aspect ratio calculation module 204 for analyzing the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock and determining the aspect ratio of the hard pores;

[0211] A structure parameter calculation module 205 for substituting the fracture porosity into the improved EIAS model based on the aspect ratio of the hard pores to obtain the microscopic pore structure parameters of the rock.

[0212] As an alternative, the improved spherical hard pore shape factor is:

[0213]

[0214]

[0215] As an alternative, the EIAS model is improved as follows:

[0216]

[0217]

[0218]

[0219]

[0220] As an alternative, analyze the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock, and determine that the aspect ratio of hard pores includes:

[0221] Obtain the relationship between the fracture volume ratio and the pressure, and further obtain the exponential relationship between the aspect ratio of hard pores and the pressure as:

[0222] a = a 0 exp[-(p - p 0 ) / p a

[0223] where a is the aspect ratio of hard pores, p is the pressure difference, a 0 is the aspect ratio of hard pores when p = p 0 , and p a is an empirical parameter.

[0224] As an alternative, the rock micro-pore structure parameters include fracture density and fracture aspect ratio.

[0225] As an alternative, obtain the rock micro-pore structure parameters including:

[0226] Substitute the fracture porosity into the improved EIAS model, take the fracture aspect ratio as a free parameter, fit the low-frequency bulk modulus and the high-frequency bulk and shear moduli, and obtain the optimal fracture aspect ratio through an optimization method.

[0227] As an alternative, the objective function of the optimization method is:

[0228]

[0229] where ε is the error, and respectively represent the experimentally measured bulk and shear moduli, and are respectively the bulk and shear moduli at the main frequency of 0.5 MHz and the bulk modulus at 0 Hz predicted by using the improved EIAS model, ​is the low-frequency bulk modulus predicted using the Gassmann theory.

[0230] Example 5

[0231] This embodiment provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above-mentioned method for quantitatively predicting microscopic pore structure parameters.

[0232] The electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0233] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0234] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0235] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses, interfaces, etc., and these well-known structures should also be included in the protection scope of the present disclosure.

[0236] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0237] Example 6

[0238] This embodiment provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the method for quantitatively predicting microscopic pore structure parameters described above.

[0239] The computer-readable storage medium according to an embodiment of the present disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present disclosure are executed.

[0240] The above computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or removable hard disks), media with built-in rewritable non-volatile memories (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).

[0241] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0242] The various embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for quantitatively predicting micro-pore structure parameters, characterized in that, it includes: Obtain the P-wave and S-wave velocities and porosity parameters of a porous rock sample in a pore-fracture medium under variable fluid types and variable pressure conditions; Calculate the corresponding fracture porosity of the rock sample based on the relationship between the porosity and pressure of the rock sample when it is saturated with gas; Establish an improved EIAS model by improving the spherical hard pore shape factor; Analyze the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of hard pores; Based on the aspect ratio of hard pores, substitute the fracture porosity into the improved EIAS model to obtain the micro-pore structure parameters of the rock.

2. The method for quantitatively predicting micro-pore structure parameters according to claim 1, wherein, the improved spherical hard pore shape factor is: Among them, P 1 , Q 1 are spherical hard pore shape factors, F 6 = 1 + A[1 + ff - R(ff + θ)] + B(1 - θ)(3 - 4R), F 9 = A[(R - 1)ff - Rθ] + Bθ(3 - 4R), 3. The method for quantitatively predicting micro-pore structure parameters according to claim 2, wherein, the improved EIAS model is: Among them, P 2 and Q 2 are coin-shaped crack shape factors, γ = (1 - c)P 1 + cP 2 , χ = (1 - c)Q 1 + cQ 2 , γ 0 = (1 - c)P 01 + cP 02 , χ 0 = (1 - c)Q 01 + cQ 02 , Q 01 = Q 1 , P 1 and Q 1 correspond to spherical hard pores, P 2 and Q 2 correspond to coin-shaped cracks.

4. The method for quantitatively predicting micro-pore structure parameters according to claim 1, wherein, analyzing the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of hard pores includes: Obtain the relationship between the fracture aspect ratio and the fracture volume ratio and pressure, and then analyze the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of hard pores, wherein the relationship between the fracture aspect ratio and the fracture volume ratio and pressure is: a = a 0 exp[-(p - p 0 ) / p a ) c = c 0 exp[-(p - p 0 ) / p c ) Among them, p is the pressure difference, the difference between the confining pressure and the pore pressure, a is the aspect ratio of the hard pores, c is the crack volume ratio, a 0 , c 0 are the values of a and c when p = p 0 , p a and p c are empirical parameters.

5. The method for quantitatively predicting micro-pore structure parameters according to claim 1, wherein, the micro-pore structure parameters of the rock include fracture density and fracture aspect ratio.

6. The method for quantitatively predicting micro-pore structure parameters according to claim 1, wherein, obtaining the micro-pore structure parameters of the rock includes: Substitute the fracture porosity into the improved EIAS model, use the fracture aspect ratio as a free parameter, fit the low-frequency bulk modulus and the high-frequency bulk and shear moduli, and obtain the optimal fracture aspect ratio through an optimization method.

7. The method for quantitatively predicting micro-pore structure parameters according to claim 6, wherein, the objective function of the optimization method is: where ε is the error, and represent the volume and shear modulus measured experimentally, and are the volume and shear modulus at the main frequency of 0.5 MHz and the volume modulus at 0 Hz predicted by the improved EIAS model, respectively, is the low-frequency volume modulus predicted by the Gassmann theory.

8. A device for quantitatively predicting micro-pore structure parameters, characterized in that, it includes: A parameter acquisition module that acquires the P-wave and S-wave velocities and porosity parameters of a porous rock sample in a pore-fracture medium under variable fluid types and variable pressure conditions; A fracture porosity calculation module that calculates the corresponding fracture porosity of the rock sample based on the relationship between the porosity and pressure of the rock sample when it is saturated with gas; Improved modeling module, by improving P 1 and Q 1 , establish the improved EIAS model; An aspect ratio calculation module that analyzes the influence of the aspect ratio of hard pores in the rock on the elastic modulus of the rock to determine the aspect ratio of hard pores; A structure parameter calculation module that, based on the aspect ratio of hard pores, substitutes the fracture porosity into the improved EIAS model to obtain the micro-pore structure parameters of the rock.

9. An electronic device, characterized in that, the electronic device includes: A memory that stores executable instructions; A processor that runs the executable instructions in the memory to implement the method for quantitatively predicting micro-pore structure parameters according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method for quantitatively predicting microscopic pore structure parameters described in any one of claims 1-7.

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