Randomly distributed fractured reservoir forward modeling method, device, equipment and medium

Abstract

The invention discloses a random distribution fracture type reservoir forward modeling method, device and equipment and a medium. The method is characterized by comprising the following steps: acquiring a fracture complexity parameter and a randomly distributed fracture scale of a fractured reservoir; according to the randomly distributed fracture scale and the fracture complexity parameter, determining a fracture simulation model of the fractured reservoir; and carrying out seismic response forward modeling on the fractured reservoir according to the fracture simulation model. Simulation of the multi-scale fracture type reservoir randomly distributed in the reservoir is achieved, the multi-scale fractures and the fracture complexity are introduced into the fracture simulation model, model simulation is closer to the real reservoir, and the accuracy of forward modeling simulation is improved.

Description

Technical Field

[0001] The invention relates to the field of petroleum and natural gas, and in particular to a forward modeling method, device, equipment and medium for a randomly distributed fracture reservoir. Background Art

[0002] In oil and gas exploration, rock physics modeling is used to simulate reservoirs. The elastic parameters of rocks can be determined through rock physics models, and the elastic parameters of rocks provide technical and data support for the exploration and development of oil and gas reservoirs. Fracture reservoirs are reservoirs in which fractures are the main storage space and seepage channels. Fracture reservoirs occupy a very important position in unconventional oil and gas reservoirs. Research on fracture reservoirs is conducive to increasing the production of oil and gas. Since the attenuation properties of seismic wave propagation are mainly affected by fracture fluids, the current rock physics modeling for fractured oil reservoirs is usually based on the Chapman model. However, due to certain limitations of the rock physics model established by the Chapman model, only two different scales of fracture distribution are considered, which makes it difficult to simulate the actual randomly distributed multi-scale fracture reservoirs. The model effect of the rock physics model established according to the Chapman model cannot well simulate the fracture distribution characteristics of multi-scale fracture reservoirs. Summary of the invention

[0003] The present invention provides a forward modeling method, device, equipment and medium for randomly distributed fractured reservoirs, so as to realize forward modeling of multi-scale fractured reservoirs and improve the accuracy of forward modeling of fractured reservoirs.

[0004] According to one aspect of the present invention, a forward modeling method for a randomly distributed fractured reservoir is provided, comprising:

[0005] Obtain the fracture complexity parameters and randomly distributed fracture scales of fractured reservoirs;

[0006] Determining a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter;

[0007] The seismic response forward modeling of the fracture type reservoir is performed according to the fracture simulation model.

[0008] According to another aspect of the present invention, there is provided a forward modeling device for a randomly distributed fractured reservoir, comprising:

[0009] A data acquisition module, used to obtain the fracture complexity parameters and random distribution fracture scale of fractured reservoirs;

[0010] A fracture model simulation module, used to determine a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter;

[0011] A seismic response forward modeling module is used to perform seismic response forward modeling on the fracture-type reservoir according to the fracture simulation model.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the forward modeling method for randomly distributed fractured reservoirs described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the random distributed fracture reservoir forward simulation method described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention obtains the fracture complexity parameters and randomly distributed fracture scales of the fracture type reservoir. According to the fracture complexity parameters, the complex fracture distribution in the fracture medium rock can be better simulated, and the randomly distributed fracture scale can fit the fracture conditions of the actual fracture medium rock, thereby improving the performance of the rock model; according to the randomly distributed fracture scale and the fracture complexity parameters, the fracture simulation model of the fracture type reservoir is determined, and adding the randomly distributed fracture scale and the fracture complexity parameters to the determined fracture type reservoir model can improve the performance of the model and improve the accuracy of the simulation; according to the fracture simulation model, the seismic response of the fracture type reservoir is forward modeled, and the elastic parameters of the fracture type reservoir can be determined through the fracture simulation model, and the seismic response of the fracture type reservoir is forward modeled according to the elastic parameters, thereby solving the technical problem of the multi-scale fracture medium rock simulation model being too idealized in the prior art, making the model simulation closer to the actual fracture type unconventional oil and gas reservoir, thereby improving the accuracy of the forward simulation.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a forward modeling method for a randomly distributed fractured reservoir provided in Example 1 of the present invention;

[0021] Figure 2 A histogram diagram of crack radius distribution under crack complexity provided by an embodiment of the present invention;

[0022] Figure 3 A histogram showing another crack radius distribution under crack complexity provided by an embodiment of the present invention;

[0023] Figure 4 is a flow chart of another forward modeling method for randomly distributed fractured reservoirs provided in the second embodiment of the present invention;

[0024] Figure 5 A schematic diagram of comparison of qP wave phase velocities under different fracture complexities provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram for comparing qP wave attenuation under different crack complexities provided by an embodiment of the present invention;

[0026] Figure 7 A schematic diagram of comparison of qSV wave phase velocities under different crack complexities provided by an embodiment of the present invention;

[0027] Figure 8 A schematic diagram of comparison of qSV wave attenuation under different crack complexities provided by an embodiment of the present invention;

[0028] Fig. 9 A schematic diagram of comparison of qSH wave phase velocities under different crack complexities provided by an embodiment of the present invention;

[0029] Fig.10 A schematic diagram of comparing frequency curves of anisotropic parameters under different fracture complexities provided by an embodiment of the present invention;

[0030] Fig.11 A schematic diagram of comparison of PP wave synthetic seismic records under a fracture complexity of 20% provided by an embodiment of the present invention;

[0031] Fig.12 A schematic diagram of comparison of PP wave synthetic seismic records with a fracture complexity of 40% provided by an embodiment of the present invention;

[0032] Fig.13 A schematic diagram of a comparison of P-SV wave synthetic seismic records with a fracture complexity of 20% provided by an embodiment of the present invention;

[0033] Fig.14 A schematic diagram of a comparison of P-SV wave synthetic seismic records with a fracture complexity of 40% provided by an embodiment of the present invention;

[0034] Fig.15 It is a structural schematic diagram of a forward modeling device for a randomly distributed fractured reservoir provided in the third embodiment of the present invention;

[0035] Fig.16 It is a schematic diagram of the structure of an electronic device for implementing the forward modeling method of a randomly distributed fractured reservoir according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] Embodiment 1

[0038] Figure 1 The first embodiment of the present invention provides a flowchart of a random distribution fracture reservoir forward simulation method. This embodiment is applicable to the case of establishing a rock physics model for a multi-scale fracture reservoir to perform seismic response forward simulation. The method can be executed by a random distribution fracture reservoir forward simulation device. The random distribution fracture reservoir forward simulation device can be implemented in the form of hardware and / or software. The random distribution fracture reservoir forward simulation device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0039] S110. Obtain fracture complexity parameters and randomly distributed fracture scales of fractured reservoirs.

[0040] Among them, the fractured reservoir may be a reservoir in which fractures are the main storage space and seepage channels.

[0041] The fracture complexity parameter may represent the distribution complexity of fractures in fracture-type reservoir rocks.

[0042] The randomly distributed fracture scale may be the distribution of fractures and the fracture scale in fracture-type reservoir rocks.

[0043] Specifically, in the rock physics simulation of the fractured reservoir, the random distribution scale of the rock and the fracture complexity parameter used to simulate the complex fracture distribution in the actual fracture medium rock are determined.

[0044] S120, determining a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter.

[0045] The fracture simulation model may be a rock physics model for simulating fractured reservoir rocks.

[0046] Specifically, the rock physics model of fractured reservoir rocks is determined by randomly distributing fracture scales and fracture complexity parameters.

[0047] For example, Figure 2 A histogram diagram of crack radius distribution under crack complexity provided by an embodiment of the present invention; Figure 3 A histogram of the distribution of crack radius under another crack complexity provided by an embodiment of the present invention. The crack complexity parameter γ can be characterized by a crack radius complexity function, and the crack radius complexity function can be characterized by ψ(γ), and the calculation formula is as follows:

[0048]

[0049] in, Represents the mean value of the radius of all proposed fracture groups. The smaller γ is, the lower the complexity of fracture distribution is, indicating that the fracture radius of random distribution is closer; when γ is larger, the complexity of fracture distribution is higher, indicating that the difference of fracture radius of random distribution is greater. Fracture complexity F cmplx (γ) can be expressed as:

[0050]

[0051] like Figure 2 As shown in Figure 2, the distribution of crack radius when the crack complexity is 20%; Figure 3 As shown, the distribution of crack radius when the crack complexity is 40%.

[0052] S130, performing seismic response forward modeling of the fractured reservoir according to the fracture simulation model.

[0053] Specifically, after the fracture simulation model is obtained, the seismic response forward modeling of the fractured reservoir is performed using the fracture simulation model.

[0054] Optionally, in another optional embodiment of the present invention, performing seismic response forward modeling on the fracture-type reservoir according to the fracture simulation model includes:

[0055] The elastic parameters of the fracture-type reservoir are predicted according to the fracture simulation model; and the seismic response forward modeling of the fracture-type reservoir is performed according to the elastic parameters.

[0056] The elastic parameters may be reservoir elastic parameters obtained by forward modeling of a rock physics model. For example, the elastic parameters may be P- and S-wave velocities and densities of fractured reservoirs.

[0057] Specifically, the elastic parameters of the fractured reservoir are predicted according to the fracture simulation model of the fractured reservoir, and the seismic response forward modeling of the fractured reservoir is performed using the calculated elastic parameters to obtain a simulation result of the seismic response forward modeling.

[0058] Optionally, in another optional embodiment of the present invention, predicting the elastic parameters of the fractured reservoir according to the fracture simulation model includes:

[0059] Obtain a pressure response function of pore pressure in the fracture simulation model and multiple multi-scale fracture group parameters; determine a frequency-dependent equivalent stiffness matrix of the fracture simulation model based on the pressure response function; determine an equivalent medium stiffness matrix of the fracture simulation model based on multiple multi-scale fracture group parameters and the frequency-dependent equivalent stiffness matrix; determine elastic parameters of the fracture-type reservoir based on the equivalent medium stiffness matrix.

[0060] The pressure response function may be a function for calculating the pore pressure of the fracture simulation model.

[0061] The multi-scale fracture group parameter may be a fracture parameter of each fracture group. For example, a normal vector of the fracture group.

[0062] The frequency-dependent equivalent stiffness matrix can be an equivalent stiffness matrix composed of various components in the fracture medium rock.

[0063] The equivalent medium stiffness matrix may be a medium stiffness matrix used to equivalently describe fracture medium rock.

[0064] Specifically, a pressure response function of the pore pressure in the fracture simulation model and multiple multi-scale fracture group parameters are obtained, and a frequency-dependent equivalent stiffness matrix in the fracture simulation model is determined through the pressure response function. Then, based on the multiple multi-scale fracture group parameters and the frequency-dependent equivalent stiffness matrix, an equivalent medium stiffness matrix of the fracture simulation model is determined, and then, based on the equivalent medium stiffness matrix, the elastic parameters of the fracture-type reservoir are determined.

[0065] For example, when calculating the elastic parameters of the crack simulation model, the following two parameters are first defined:

[0066]

[0067]

[0068] Where P represents the pressure function, n is the unit normal vector of the fracture surface, i and j represent the unit normal vectors in different directions, and the superscript a represents the ath group of fractures. The larger the a value, the more groups of fractures there are and the greater the fracture density. f Represents the bulk modulus of the fluid in the pore space, regulating the type of fluid in the pore from a microscopic perspective. Θ is defined as a coefficient related to the mesoscopic relaxation time of the a-th group of fractures; τ a is the mesoscopic relaxation time of the a-th group of cracks. When the relaxation time is defined as infinite, the fluid between the cracks does not flow with the frequency change, that is, the cracks are opened or closed. i is the radius of the i-th group of cracks, r is the pore radius and is equal to the particle size.

[0069] The calculation formula of the mesoscopic relaxation time of the a group of cracks is as follows:

[0070]

[0071] The formula for calculating the pressure response function Γ of the pore pressure is as follows

[0072]

[0073] Where M is the total number of fracture groups, a represents the mth fracture group, i and j represent unit normal vectors in different directions, S represents the pressure function, K c Represents the bulk modulus of the fluid in the pore space, and regulates the type of fluid in the pore from a microscopic perspective. Θ is defined as a coefficient related to the mesoscopic relaxation time of the mth group of fractures. Where Φ represents the porosity corresponding to the pore or fracture group, n is the unit normal vector of the fracture surface, σ and δ are stress tensors, υ is Poisson's ratio, μ is the Lame coefficient, the superscript p represents the corresponding pore, and the subscript a represents the corresponding fracture group.

[0074] The pressure response function of the a-th group of cracks can be calculated by the following formula:

[0075]

[0076] Furthermore, the frequency-dependent equivalent stiffness matrix of the crack simulation model can be expressed by the following formula:

[0077]

[0078] Among them, C ijkl (ω) is the frequency-dependent equivalent stiffness matrix; is the isotropic stiffness matrix of the background medium; The last term is the stiffness matrix correction term related to the M group of cracks; ω represents the angular frequency, φ and φ aThey represent the porosity corresponding to pores and fracture group a respectively, the superscript o represents the background skeleton, the superscript p represents the pores, the superscript fa represents the fracture group a, and the subscript ijkl represents the dimension of the stiffness coefficient matrix.

[0079] After obtaining the frequency-dependent equivalent stiffness matrix of the randomly distributed multi-scale fracture reservoir forward simulation device, the azimuth angle θ is defined as a and polarization angle Then the normal vector of the ath crack group is expressed as:

[0080]

[0081] Then the local coordinate system Ox1x2x3 of the crack is selected as:

[0082]

[0083] x2=(-sinθ a ,cosθ a ,0)

[0084]

[0085] Among them, x3 and n a Parallel, indicating that the crack correction term has a VTI form in the local coordinate system of the crack, and then the rotation matrix R is calculated, and the rotation matrix R is obtained about the azimuth angle θ a and polarization angle The result, that is, the expression of the rotation matrix R corresponding to the a-th group of cracks is:

[0086]

[0087] Introduce pore correction term according to the influence of pore parameters The expression is:

[0088]

[0089] Among them, the expressions of coefficients ξ1 and ξ2 are:

[0090]

[0091]

[0092] in, represents the porosity corresponding to the pore p, υ is the Poisson's ratio, λ and μ are the Lame coefficients, and p * is the fluid pressure in the pore, and σ is the stress tensor.

[0093] Furthermore, the equivalent medium stiffness matrix of the randomly distributed crack simulation model is established as follows:

[0094]

[0095] in, is the background medium term of the fracture simulation model, is the porosity correction term of the fracture simulation model, C ijkl is the equivalent stiffness matrix of the crack simulation model, a represents that the variable belongs to the mth group, and R is the rotation matrix of the crack correction term.

[0096] Optionally, in another optional embodiment of the present invention, the elastic parameters include P- and S-wave velocities and density; and the forward modeling of seismic response of the fractured reservoir according to the elastic parameters includes:

[0097] The seismic response forward modeling of the fractured reservoir is performed according to the longitudinal and transverse wave velocities and densities to determine the seismic wave velocity and inverse quality factor of the fractured reservoir.

[0098] Among them, the longitudinal and shear wave velocities can be the wave velocities of the longitudinal waves and the shear waves in the seismic waves.

[0099] The density may be the density of a reservoir in a fractured reservoir.

[0100] The seismic wave velocity may be the phase velocity of a P wave, a SV wave, or a SH wave.

[0101] The inverse quality factor may be an attenuation parameter that characterizes the propagation of seismic waves in an inelastic medium.

[0102] Specifically, the P- and S-wave velocities and densities of the fractured reservoir are obtained, and the seismic response of the fractured reservoir is forward modeled according to the P- and S-wave velocities and densities, so as to calculate the seismic wave velocity and inverse quality factor of the fractured reservoir.

[0103] The P-wave and S-wave velocities and densities of fractured reservoirs are determined by the fracture simulation model of fractured reservoirs. The incident angle is defined as θ. In VTI media, the velocities of P-wave, SV-wave, and SH-wave can be expressed by the following formulas:

[0104]

[0105]

[0106]

[0107] Among them, V P It can be the velocity of the P wave; V SV It can be the velocity of SV waves; V SH It can be the velocity of SH wave; ρ is the density; M can be expressed by the following formula:

[0108]

[0109] Furthermore, the seismic wave phase velocity and inverse quality factor can be calculated by the following formula:

[0110]

[0111]

[0112] Among them, V ph is the seismic wave phase velocity; Q -1 is the inverse quality factor.

[0113] The technical solution of the embodiment of the present invention obtains the fracture complexity parameters and randomly distributed fracture scales of the fracture type reservoir. According to the fracture complexity parameters, the complex fracture distribution in the fracture medium rock can be better simulated, and the randomly distributed fracture scale can fit the fracture conditions of the actual fracture medium rock, thereby improving the performance of the rock model; according to the randomly distributed fracture scale and the fracture complexity parameters, the fracture simulation model of the fracture type reservoir is determined, and adding the randomly distributed fracture scale and the fracture complexity parameters to the determined fracture type reservoir model can improve the performance of the model and improve the accuracy of the simulation; according to the fracture simulation model, the seismic response of the fracture type reservoir is forward modeled, and the elastic parameters of the fracture type reservoir can be determined through the fracture simulation model, and the seismic response of the fracture type reservoir is forward modeled according to the elastic parameters, thereby solving the technical problem of the multi-scale fracture medium rock simulation model being too idealized in the prior art, making the simulation model closer to the actual reservoir and improving the accuracy of the forward simulation.

[0114] Embodiment 2

[0115] Figure 4 1 is a flow chart of another random distribution fracture reservoir forward simulation method provided by the second embodiment of the present invention. The relationship between this embodiment and the above embodiment is the specific method of establishing a fracture simulation model. Figure 4 As shown, the forward modeling method of the randomly distributed fractured reservoir includes:

[0116] S210. Obtain fracture complexity parameters and randomly distributed fracture scales of fractured reservoirs.

[0117] S220. Obtain matrix elastic modulus and fluid modeling parameters of fracture medium rock in fracture type reservoir.

[0118] The matrix elastic modulus may be the elastic modulus of the matrix in the fractured medium rock.

[0119] The fluid modeling parameters may be parameters used to establish a rock physics model in fractured medium rocks. For example, the fluid modeling parameters may include parameters such as the type and permeability of the fluid in the fractured medium rocks.

[0120] Specifically, when performing rock physics modeling, the matrix elastic modulus of each matrix of the fracture matrix rock and the modeling parameters of the fluid in the rock are obtained.

[0121] Optionally, in another optional embodiment of the present invention, the obtaining of the matrix elastic modulus and fluid modeling parameters of the fracture medium rock in the fracture-type reservoir includes: obtaining the rock matrix composition and matrix content of the fracture medium rock; determining the matrix elastic modulus of the fracture medium rock according to the rock matrix composition and the matrix content; obtaining the logging data and logging curve of the fracture medium rock; and determining the fluid modeling parameters of the fracture medium rock according to the logging data and the logging curve.

[0122] The matrix component may be a matrix mineral component of the fracture medium rock.

[0123] The matrix content may be the content of each matrix mineral component of the fracture medium rock.

[0124] The logging data may be data obtained by logging equipment collecting data from reservoirs.

[0125] The logging curve may be a curve obtained by measuring and recording with logging equipment.

[0126] Specifically, the composition of the fractured medium rock is analyzed by laboratory rock analysis equipment to obtain the rock matrix composition and matrix content of the fractured medium rock, the matrix elastic model of the fractured medium rock is calculated based on the rock matrix composition and matrix content, the logging data and logging curves of the reservoir where the fractured medium rock is located are obtained, and the fluid modeling parameters of the fractured medium rock are determined based on the logging data and logging curves.

[0127] S230, determining a fracture simulation model of the fracture type reservoir according to the matrix elastic modulus, the fluid modeling parameter, the randomly distributed fracture scale and the fracture complexity parameter.

[0128] Specifically, the matrix elastic modulus, fluid modeling parameters, randomly distributed fracture scales and fracture complexity parameters are obtained, rock physics modeling is performed on the fracture medium rock of the fracture type reservoir, and the fracture simulation model of the fracture type reservoir is determined.

[0129] Optionally, in another optional embodiment of the present invention, determining a fracture simulation model of the fracture type reservoir according to the matrix elastic modulus, the fluid modeling parameter, the randomly distributed fracture scale and the fracture complexity parameter includes:

[0130] Obtaining the porosity of the fracture medium rock;

[0131] Determining a physical skeleton model of the fracture medium rock according to the matrix elastic modulus;

[0132] Determine a fluid model of the fracture medium rock according to the fluid modeling parameters and the porosity;

[0133] Multi-scale fractures are added to the physical skeleton model according to the randomly distributed fracture scales and the fracture complexity parameters, and the fluid model is combined to obtain a fracture simulation model of the fracture-type reservoir.

[0134] Among them, porosity can be the ratio of the pore space volume to the fracture medium rock volume.

[0135] The physical skeleton model may be a physical skeleton without pores and fluids in rock physics modeling.

[0136] The fluid model may be a model of fluid in rock physics modeling.

[0137] Optionally, during the rock physics modeling process, a physical skeleton model corresponding to the rock is established, and then the pores and cracks of the rock are added to the physical skeleton model, and then the fluid model corresponding to the rock is filled in to determine the complete rock physics model.

[0138] Specifically, the porosity of the fractured medium rock is obtained, a physical skeleton model of the fractured medium rock is established through the matrix elastic modulus of the fractured medium rock, the fluid model of the fractured medium rock is determined through the fluid modeling parameters and the porosity, and then multi-scale fractures are added to the physical skeleton model of the fractured medium rock according to the randomly distributed fracture scale and fracture complexity parameters, and the fluid model is merged to obtain a fracture simulation model of the fracture-type reservoir.

[0139] S240, performing seismic response forward modeling of the fracture-type reservoir according to the fracture simulation model.

[0140] The technical solution of the embodiment of the present invention obtains the fracture complexity parameters and random distribution fracture scale of the fracture type reservoir; obtains the matrix elastic modulus and fluid modeling parameters of the fracture medium rock in the fracture type reservoir, determines the composition parameters and fluid parameters of the fracture medium rock, and performs physical modeling on the fracture medium rock, which can be more in line with the actual rock; determines the fracture simulation model of the fracture type reservoir according to the matrix elastic modulus, the fluid modeling parameters, the randomly distributed fracture scale and the fracture complexity parameter, and adds the randomly distributed fracture scale and fracture complexity parameter in the rock physical modeling process, which can improve the performance of the model and improve the accuracy of the simulation; performs seismic response forward modeling on the fracture type reservoir according to the fracture simulation model, and the elastic parameters of the fracture type reservoir can be determined through the fracture simulation model, and performs seismic response forward modeling on the fracture type reservoir according to the elastic parameters, which solves the technical problem of the multi-scale fracture medium rock simulation model being too idealized in the prior art, makes the model simulation closer to the actual reservoir, and improves the accuracy of the forward simulation.

[0141] Optionally, an embodiment of the present invention provides another forward modeling method for randomly distributed fractured reservoirs. Among them, the mineral composition and content of the rock matrix are obtained by analyzing the randomly distributed fractured reservoir, and the skeleton modulus of the randomly distributed fractured reservoir is calculated by rock physics modeling. Secondly, the fracture information is introduced into the fracture simulation model, and a detailed description of the fractures of the randomly distributed fractured reservoir is achieved by considering multiple parameters such as different fluid types, porosity, permeability, fracture radius, and fracture density. At the same time, in order to simulate the fracture conditions of the actual reservoir as much as possible, the fracture scale is displayed in the form of random distribution, and fracture simulation models of different complexities are established, thereby effectively realizing the modeling of multiple parameters such as different fluid types, porosity, permeability, fracture radius, and fracture density.

[0142] Furthermore, according to the fracture simulation models with different complexities, the rock physical elastic stiffness coefficient matrix of the multi-scale fracture medium with different complexities is calculated, and then the elastic parameters are calculated.

[0143] Exemplarily, Table 1 is a parameter table of a randomly distributed crack simulation model provided in an embodiment of the present invention.

[0144]

[0145]

[0146] The modeling parameters in Table 1 can be used to control the type of fluid in the pores from a microscopic perspective, while adjusting the opening and closing state of the fractures, the fracture radius and the fracture density. Adjusting the relaxation time can simulate the permeability of the target reservoir.

[0147] Two distribution states of crack radius are set to characterize different crack complexities: complexity 20%, crack radius complexity parameter = 0.04, obeying normal distribution; complexity 40%, crack radius complexity parameter = 0.08, obeying normal distribution, and other parameters of each crack group are the same. The statistical properties of the crack radius randomly generated according to the above crack radius complexity distribution function and the crack radius complexity distribution state are shown in Table 2. Figure 2 and Figure 3 Table 2 is a statistical parameter table of crack radius provided by an embodiment of the present invention. Table 2 is as follows:

[0148]

[0149] The analysis found that the average values ​​of the fracture radius of the two distributions are both at the order of 0.2 m, but the discreteness of the fracture radius in different distributions is very different. The wave velocity, quality factor and anisotropy parameter results obtained by forward modeling of the random distribution multi-scale fracture model. Figure 5 A schematic diagram of comparison of qP wave phase velocity under different fracture complexities provided by an embodiment of the present invention. Figure 5 As shown: (a) is the qP wave phase velocity of different incident angles and different frequencies under the complexity of 20%, (b) is the qP wave phase velocity of different incident angles and different frequencies under the complexity of 40%, (c) is the influence of different incident angles on the qP wave phase velocity of two crack complexity cases under the condition of 10Hz, (d) is the influence of different incident angles on the qP wave phase velocity of two crack complexity cases under the condition of 10kHz, (e) is the influence of different frequencies on the qP wave phase velocity of two crack complexity cases when the incident angle is 0 degrees, and (f) is the influence of different frequencies on the qP wave phase velocity of two crack complexity cases when the incident angle is 20 degrees.

[0150] Figure 6 A comparative schematic diagram of qP wave attenuation under different crack complexities provided by an embodiment of the present invention. Figure 6 As shown: (a) is the calculation results of the qP wave inverse quality factor for different incident angles and different frequencies under the complexity of 20%, (b) is the calculation results of the qP wave inverse quality factor for different incident angles and different frequencies under the complexity of 40%, (c) is the influence of different incident angles on the qP wave inverse quality factor of two crack complexity cases under the 10Hz case, (d) is the influence of different incident angles on the qP wave inverse quality factor of two crack complexity cases under the 10kHz case, (e) is the influence of different frequencies on the qP wave inverse quality factor of two crack complexity cases when the incident angle is 0 degrees, and (f) is the influence of different frequencies on the qP wave inverse quality factor of two crack complexity cases when the incident angle is 20 degrees.

[0151] Figure 7A schematic diagram of comparing qSV wave phase velocities under different crack complexities provided by an embodiment of the present invention. Figure 7 As shown: (a) is the qSV wave phase velocity of different incident angles and different frequencies under the complexity of 20%, (b) is the qSV wave phase velocity of different incident angles and different frequencies under the complexity of 40%, (c) is the influence of different incident angles on the qSV wave phase velocity of two crack complexity cases under the condition of 10Hz, (d) is the influence of different incident angles on the qSV wave phase velocity of two crack complexity cases under the condition of 10kHz, (e) is the influence of different frequencies on the qSV wave phase velocity of two crack complexity cases when the incident angle is 0 degrees, and (f) is the influence of different frequencies on the qSV wave phase velocity of two crack complexity cases when the incident angle is 20 degrees.

[0152] Figure 8 A comparative schematic diagram of qSV wave attenuation under different crack complexities provided by an embodiment of the present invention. Figure 8 As shown: (a) is the calculation results of the qSV wave inverse quality factor for different incident angles and different frequencies under the complexity of 20%, (b) is the calculation results of the qSV wave inverse quality factor for different incident angles and different frequencies under the complexity of 40%, (c) is the influence of different incident angles on the qSV wave inverse quality factor under two crack complexity conditions under the 10Hz condition, (d) is the influence of different incident angles on the qSV wave inverse quality factor under two crack complexity conditions under the 10kHz condition, (e) is the influence of different frequencies on the qSV wave inverse quality factor under two crack complexity conditions when the incident angle is 0 degrees, and (f) is the influence of different frequencies on the qSV wave inverse quality factor under two crack complexity conditions when the incident angle is 20 degrees.

[0153] Fig. 9 A schematic diagram of comparison of qSH wave phase velocity under different crack complexities provided by an embodiment of the present invention. Fig. 9 As shown: (a) is the SH wave phase velocity of different incident angles and frequencies under the complexity of 20%, (b) is the SH wave phase velocity of different incident angles and frequencies under the complexity of 40%, and (c) is the influence of different incident angles on the SH wave phase velocity under two crack complexity conditions at 10 Hz.

[0154] Fig.10 The following is a schematic diagram of comparing the frequency curves of anisotropic parameters under different fracture complexities provided by an embodiment of the present invention. Fig.10 As shown: (a) is the anisotropy parameter ε T The results of frequency variation, (b) is the anisotropy parameter δ T The results of frequency variation, (c) is the anisotropy parameter γ T Results vary with frequency.

[0155] Furthermore, according to the above analysis results, the crack radius does not affect the high and low frequency limits of the wave velocity, but only changes the frequency band range of the transition section, that is, the attenuation peak decreases with the decrease of the crack radius. At the same time, the crack radius of the model is approximately continuously distributed. It can be seen that the effects of each group of cracks are superimposed on each other. The wave velocity-frequency curve shape of the model has a similar existence form. The center frequency of the transition section corresponds to the mean of the crack radius. The frequency band of the transition section is widened, and the degree of widening is positively correlated with the discreteness of the crack radius distribution. For the attenuation properties in the simulation results, with the change of complexity, the discreteness of the crack radius distribution gradually increases. The frequency band width of the attenuation peak is widened like the dispersion band, and the attenuation peak will decrease accordingly. When the crack radius is continuously distributed, the transition band of the model is wider and the attenuation peak is lower. The center frequency of the transition section depends on the mean of the crack radius, and the frequency band width of the transition section depends on the variance of the crack radius distribution.

[0156] Fig.11 A schematic diagram of synthetic seismic recording of PP waves changing with incident angle under the condition of 20% fracture complexity provided by an embodiment of the present invention. Fig.11 As shown: (a) is the synthetic seismic record of PP wave changing with incident angle under the condition of complexity 20%, Fig.12 A schematic diagram of synthetic seismic recording of PP waves changing with incident angle under the condition of 40% fracture complexity provided by an embodiment of the present invention. Fig.12 Shown: (b) is the synthetic seismic record of PP wave changing with incident angle under the complexity of 40%. Fig.13 A schematic diagram of synthetic seismic recording of P-SV waves changing with the incident angle when the fracture complexity is 20% provided by an embodiment of the present invention. Fig.13 Shown: (a) is the synthetic seismic record of P-SV wave changing with incident angle under the complexity of 20%. Fig.14 A schematic diagram of synthetic seismic recording of P-SV waves changing with the incident angle when the fracture complexity is 40% provided by an embodiment of the present invention. Fig.14 As shown in: (b) is the synthetic seismic record of P-SV waves changing with the incident angle under the complexity of 40%. Fig.11 and Fig.12As shown in the figure, the analysis shows that when the other parameters of the model are the same, fracture reservoirs with different complexities have a certain impact on the P-wave and S-wave velocities and attenuation peaks. Since the fracture radius mean values ​​of fracture reservoirs with different complexities are basically consistent, the overall waveforms are similar in the synthetic seismic records, but due to the influence of different complexities, the amplitude strength of the synthetic seismic records has been changed to varying degrees. When the fracture radius variance is small, the lower the fracture complexity presented, which means that more fractures are at a similar fracture radius scale, so the superposition effect has a greater impact, and the impact on the reservoir is further increased. Therefore, this model improves the elastic stiffness coefficient matrix of the fracture reservoir by macro-controlling the fracture complexity, and effectively shows that the actual fracture medium has a randomly distributed fracture characteristic. The forward simulation method of the randomly distributed fracture reservoir.

[0157] Embodiment 3

[0158] Fig.15 Schematic diagram of the structure of a random distribution fracture reservoir forward simulation device provided by the third embodiment of the present invention. Fig.15 As shown, the device includes: a data acquisition module 310, a crack model simulation module 320 and a seismic response forward modeling module 330, wherein:

[0159] The data acquisition module 310 is used to obtain the fracture complexity parameters and the random distribution fracture scale of the fracture type reservoir;

[0160] A fracture model simulation module 320, for determining a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter;

[0161] The seismic response forward modeling module 330 is used to perform seismic response forward modeling on the fracture-type reservoir according to the fracture simulation model.

[0162] The technical solution of the embodiment of the present invention obtains the fracture complexity parameters and randomly distributed fracture scales of the fracture type reservoir. According to the fracture complexity parameters, the complex fracture distribution in the fracture medium rock can be better simulated, and the randomly distributed fracture scale can fit the fracture conditions of the actual fracture medium rock, thereby improving the performance of the rock model; according to the randomly distributed fracture scale and the fracture complexity parameters, the fracture simulation model of the fracture type reservoir is determined, and adding the randomly distributed fracture scale and the fracture complexity parameters to the determined fracture type reservoir model can improve the performance of the model and improve the accuracy of the simulation; according to the fracture simulation model, the seismic response of the fracture type reservoir is forward modeled, and the elastic parameters of the fracture type reservoir can be determined through the fracture simulation model, and the seismic response of the fracture type reservoir is forward modeled according to the elastic parameters, thereby solving the technical problem of the multi-scale fracture medium rock simulation model being too idealized in the prior art, making the model simulation closer to the actual reservoir and improving the accuracy of the forward simulation.

[0163] Optionally, the crack model simulation module is specifically used for:

[0164] Obtain the matrix elastic modulus and fluid modeling parameters of fracture medium rocks in fractured reservoirs;

[0165] A fracture simulation model of the fracture-type reservoir is determined according to the matrix elastic modulus, the fluid modeling parameter, the randomly distributed fracture scale and the fracture complexity parameter.

[0166] Optionally, the crack model simulation module is further used for:

[0167] Obtaining the porosity of the fracture medium rock;

[0168] Determining a physical skeleton model of the fracture medium rock according to the matrix elastic modulus;

[0169] Determine a fluid model of the fracture medium rock according to the fluid modeling parameters and the porosity;

[0170] Multi-scale fractures are added to the physical skeleton model according to the randomly distributed fracture scales and the fracture complexity parameters, and the fluid model is combined to obtain a fracture simulation model of the fracture-type reservoir.

[0171] Optionally, the crack model simulation module is further used for:

[0172] Obtaining rock matrix composition and matrix content of the fracture medium rock;

[0173] Determining the matrix elastic modulus of the fracture medium rock according to the rock matrix composition and the matrix content;

[0174] Acquiring logging data and logging curves of the fracture medium rock;

[0175] The fluid modeling parameters of the fracture medium rock are determined according to the well logging data and the well logging curve.

[0176] Optionally, the seismic response forward modeling module is specifically used for:

[0177] Predicting elastic parameters of the fractured reservoir according to the fracture simulation model;

[0178] The seismic response forward modeling of the fractured reservoir is performed according to the elastic parameters.

[0179] Optionally, the seismic response forward modeling module is further used for:

[0180] Obtaining a pressure response function of pore pressure and multiple multi-scale fracture group parameters in the fracture simulation model;

[0181] Determining a frequency-dependent equivalent stiffness matrix of the crack simulation model according to the pressure response function;

[0182] Determining an equivalent medium stiffness matrix of the crack simulation model according to the plurality of multi-scale crack group parameters and the frequency-dependent equivalent stiffness matrix;

[0183] The elastic parameters of the fractured reservoir are determined according to the equivalent medium stiffness matrix.

[0184] Optionally, the seismic response forward modeling module is further used for:

[0185] The seismic response forward modeling of the fractured reservoir is performed according to the longitudinal and transverse wave velocities and densities to determine the seismic wave velocity and inverse quality factor of the fractured reservoir.

[0186] The randomly distributed fractured reservoir forward simulation device provided in the embodiment of the present invention can execute the randomly distributed fractured reservoir forward simulation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0187] Embodiment 4

[0188] Fig.16A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0189] like Fig.16 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0190] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0191] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a random distribution fracture reservoir forward simulation method.

[0192] In some embodiments, the random distribution fracture type reservoir forward simulation method can be implemented as a computer program, which is tangibly contained in a computer readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the random distribution fracture type reservoir forward simulation method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the random distribution fracture type reservoir forward simulation method in any other appropriate manner (for example, by means of firmware).

[0193] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0194] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0195] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0196] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0197] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0198] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0199] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0200] Embodiment 5

[0201] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the random distribution fracture reservoir forward modeling method provided in any embodiment of the present invention are implemented. The method includes:

[0202] Obtain the fracture complexity parameters and randomly distributed fracture scales of fractured reservoirs;

[0203] Determining a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter;

[0204] The seismic response forward modeling of the fracture type reservoir is performed according to the fracture simulation model.

[0205] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0206] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0207] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0208] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0209] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0210] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0211] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A forward modeling method for a randomly distributed fractured reservoir, characterized in that: include: Obtain the fracture complexity parameters and randomly distributed fracture scales of fractured reservoirs; Determining a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter; The seismic response forward modeling of the fracture type reservoir is performed according to the fracture simulation model.

2. The method according to claim 1, characterized in that Determining the fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity includes: Obtain the matrix elastic modulus and fluid modeling parameters of fracture medium rocks in fractured reservoirs; A fracture simulation model of the fracture-type reservoir is determined according to the matrix elastic modulus, the fluid modeling parameter, the randomly distributed fracture scale and the fracture complexity parameter.

3. The method according to claim 2, characterized in that Determining a fracture simulation model of the fracture type reservoir according to the matrix elastic modulus, the fluid modeling parameter, the randomly distributed fracture scale and the fracture complexity parameter includes: Obtaining the porosity of the fracture medium rock; Determining a physical skeleton model of the fracture medium rock according to the matrix elastic modulus; Determine a fluid model of the fracture medium rock according to the fluid modeling parameters and the porosity; Multi-scale fractures are added to the physical skeleton model according to the randomly distributed fracture scales and the fracture complexity parameters, and the fluid model is combined to obtain a fracture simulation model of the fracture-type reservoir.

4. The method according to claim 2, characterized in that: The method of obtaining the matrix elastic modulus and fluid modeling parameters of the fracture medium rock in the fracture type reservoir includes: Obtaining rock matrix composition and matrix content of the fracture medium rock; Determining the matrix elastic modulus of the fracture medium rock according to the rock matrix composition and the matrix content; Acquiring logging data and logging curves of the fracture medium rock; The fluid modeling parameters of the fracture medium rock are determined according to the well logging data and the well logging curve.

5. The method according to claim 1, characterized in that The forward modeling of seismic response of the fractured reservoir according to the fracture simulation model comprises: Predicting elastic parameters of the fractured reservoir according to the fracture simulation model; The seismic response forward modeling of the fractured reservoir is performed according to the elastic parameters.

6. The method according to claim 5, characterized in that The method of predicting the elastic parameters of the fractured reservoir according to the fracture simulation model includes: Obtaining a pressure response function of pore pressure and multiple multi-scale fracture group parameters in the fracture simulation model; Determining a frequency-dependent equivalent stiffness matrix of the crack simulation model according to the pressure response function; Determining an equivalent medium stiffness matrix of the crack simulation model according to the plurality of multi-scale crack group parameters and the frequency-dependent equivalent stiffness matrix; The elastic parameters of the fractured reservoir are determined according to the equivalent medium stiffness matrix.

7. The method according to claim 6, characterized in that The elastic parameters include longitudinal and transverse wave velocities and density; the forward modeling of seismic response of the fractured reservoir according to the elastic parameters includes: The seismic response forward modeling of the fractured reservoir is performed according to the longitudinal and transverse wave velocities and densities to determine the seismic wave velocity and inverse quality factor of the fractured reservoir.

8. A forward modeling device for a randomly distributed fractured reservoir, characterized in that: include: A data acquisition module, used to obtain the fracture complexity parameters and random distribution fracture scale of fractured reservoirs; A fracture model simulation module, used to determine a fracture simulation model of the fracture type reservoir according to the randomly distributed fracture scale and the fracture complexity parameter; A seismic response forward modeling module is used to perform seismic response forward modeling on the fracture-type reservoir according to the fracture simulation model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the forward modeling method for randomly distributed fractured reservoirs as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the random distribution fracture reservoir forward simulation method described in any one of claims 1-7 when executed.

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