Randomly distributed fracture type reservoir forward simulation method, device, equipment and medium

Abstract

The application discloses a random distribution crack type reservoir forward simulation method, device, equipment and medium. Its characteristics include: obtaining the crack complexity parameter and random distribution crack scale of the crack type reservoir; determining the crack simulation model of the crack type reservoir according to the random distribution crack scale and the crack complexity parameter; and performing the seismic response forward of the crack type reservoir according to the crack simulation model. The simulation of the random distribution multi-scale crack type reservoir in the reservoir is realized, the multi-scale crack and the crack complexity are introduced in the crack simulation model, the model simulation is closer to the real reservoir, and the accuracy of the forward simulation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas, in particular to a random distribution fracture reservoir forward modeling method, device, equipment and medium. BACKGROUND

[0002] In oil and gas exploration, rock physics modeling is used to perform forward modeling on reservoirs, and through the rock physics model, the elastic parameters of the rock can be determined, and the technical and data support for the exploration and development of oil and gas reservoirs can be provided. Fractured reservoirs are reservoirs in which fractures are the main storage space and seepage channel. In unconventional oil and gas reservoirs, fractured reservoirs play a very important role, and research on fractured reservoirs is beneficial to improving the production of oil and gas. Since the attenuation properties of seismic wave propagation are mainly affected by the fracture fluid, the rock physics modeling of the fractured reservoir is currently based on the Chapman model. However, the rock physics model established by the Chapman model has certain limitations, as it only considers the distribution of two different sizes of fractures, and it is difficult to simulate actual random distribution of multi-scale fractures. 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

[0003] The present application provides a random distribution fracture reservoir forward modeling method, device, equipment and medium to realize forward modeling of multi-scale fractured reservoirs and improve the accuracy of forward modeling of fractured reservoirs.

[0004] According to an aspect of the present application, a random distribution fracture reservoir forward modeling method is provided, comprising:

[0005] obtaining a fracture complexity parameter and a random distribution fracture size of a fractured reservoir;

[0006] determining a fracture simulation model of the fractured reservoir according to the random distribution fracture size and the fracture complexity parameter;

[0007] performing seismic response forward modeling on the fractured reservoir according to the fracture simulation model.

[0008] According to another aspect of the present application, a random distribution fracture reservoir forward modeling device is provided, comprising:

[0009] a data acquisition module for obtaining a fracture complexity parameter and a random distribution fracture size of a fractured reservoir;

[0010] a fracture model simulation module for determining a fracture simulation model of the fractured reservoir according to the random distribution fracture size and the fracture complexity parameter;

[0011] a seismic response forward modeling module, configured to perform seismic response forward modeling on the fractured reservoir according to the fracture simulation model.

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

[0013] at least one processor; and

[0014] a memory connected with 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 to enable the at least one processor to perform the random distribution fractured reservoir forward modeling method according to any one of the embodiments of the present application.

[0016] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the random distribution fractured reservoir forward modeling method according to any one of the embodiments of the present application when executed by the processor.

[0017] The technical scheme of the embodiments of the present application can obtain the fracture complexity parameter and the random distribution fracture scale of the fractured reservoir, can better simulate the complex fracture distribution in the fractured medium rock according to the fracture complexity parameter, can fit the fracture condition of the real fractured medium rock according to the random distribution fracture scale, and can improve the performance of the rock model; can determine the fracture simulation model of the fractured reservoir according to the random distribution fracture scale and the fracture complexity parameter, can improve the performance of the model and the accuracy of the simulation by adding the random distribution fracture scale and the fracture complexity parameter in the determination of the fractured reservoir model; can perform seismic response forward modeling on the fractured reservoir according to the fracture simulation model, can determine the elastic parameter of the fractured reservoir through the fracture simulation model, can perform seismic response forward modeling on the fractured reservoir according to the elastic parameter, and can solve the technical problem of the idealized multi-scale fractured medium rock simulation model in the prior art, can make the model simulation closer to the real fractured unconventional oil and gas reservoir, and can improve the accuracy of the forward modeling.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0020] Figure 1 is a flow chart of a random distribution fracture type reservoir forward modeling method provided by the first embodiment of the present application;

[0021] Figure 2 is a histogram schematic diagram of fracture radius distribution under fracture complexity provided by the present application;

[0022] Figure 3 is another histogram schematic diagram of fracture radius distribution under fracture complexity provided by the present application;

[0023] Figure 4 is a flow chart of another random distribution fracture type reservoir forward modeling method provided by the second embodiment of the present application;

[0024] Figure 5 is a comparative schematic diagram of qP wave phase velocity under different fracture complexities provided by the present application;

[0025] Figure 6 is a comparative schematic diagram of qP wave attenuation under different fracture complexities provided by the present application;

[0026] Figure 7 is a comparative schematic diagram of qSV wave phase velocity under different fracture complexities provided by the present application;

[0027] Figure 8 is a comparative schematic diagram of qSV wave attenuation under different fracture complexities provided by the present application;

[0028] Figure 9 is a comparative schematic diagram of qSH wave phase velocity under different fracture complexities provided by the present application;

[0029] Figure 10 is a comparative schematic diagram of anisotropy parameter frequency curve under different fracture complexities provided by the present application;

[0030] Figure 11 is a comparative schematic diagram of P-P wave synthetic seismic record under fracture complexity 20% provided by the present application;

[0031] Figure 12 is a comparative schematic diagram of P-P wave synthetic seismic record under fracture complexity 40% provided by the present application;

[0032] Figure 13 A contrast schematic diagram of P-SV wave synthetic seismic records under a fracture complexity of 20% provided by the embodiment of the present application;

[0033] Figure 14 A contrast schematic diagram of P-SV wave synthetic seismic records under a fracture complexity of 40% provided by the embodiment of the present application;

[0034] Figure 15 is a structural schematic diagram of a random distribution fracture type reservoir forward modeling device provided by the third embodiment of the present application;

[0035] Figure 16 is a structural schematic diagram of an electronic device for implementing a random distribution fracture type reservoir forward modeling method of the present application. DETAILED DESCRIPTION

[0036] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0037] Embodiment one

[0038] Figure 1 is a flowchart of a random distribution fracture type reservoir forward modeling method provided by the first embodiment of the present application. The present embodiment can be applicable to the case of establishing a rock physics model for seismic response forward of a multi-scale fracture type reservoir. The method can be executed by a random distribution fracture type reservoir forward modeling device. The random distribution fracture type reservoir forward modeling device can be realized in the form of hardware and / or software and can be configured in an electronic device. As shown in the figure, the method comprises the following steps. Figure 1

[0039] S110, obtaining a fracture complexity parameter and a random distribution fracture scale of a fracture type reservoir.

[0040] The fracture type reservoir can be a reservoir in which fractures are the main storage space and seepage channel.

[0041] The fracture complexity parameter can be a parameter representing the distribution complexity of fractures in the rock of the fracture type reservoir.

[0042] The random distribution fracture scale can be the distribution and scale of the fractures in the rock of the fracture type reservoir. ​

[0043] Specifically, in the petrophysical simulation of the fractured reservoir, a random distribution scale of the rock and a fracture complexity parameter for simulating the complex fracture distribution in the rock of the actual fractured medium are determined.

[0044] S120, determining a fracture simulation model of the fractured reservoir according to the random distribution fracture scale and the fracture complexity parameter.

[0045] The fracture simulation model can be a petrophysical model simulating the rock of the fractured reservoir.

[0046] Specifically, the petrophysical model of the rock of the fractured reservoir is determined by the random distribution fracture scale and the fracture complexity parameter.

[0047] Exemplarily, Figure 2 A histogram diagram of the fracture radius distribution under the fracture complexity provided by the embodiment of the present application is shown in FIG. 2. Figure 3 Another histogram diagram of the fracture radius distribution under the fracture complexity provided by the embodiment of the present application is shown in FIG. 3.The fracture complexity parameter γ can be represented by a fracture radius complexity function, which can be represented by ψ(γ), and the calculation formula is as follows:

[0048]

[0049] Wherein, The smaller γ is, the lower the fracture distribution complexity is, and the closer the randomly distributed fracture radius is; the larger γ is, the higher the fracture distribution complexity is, and the larger the difference of the randomly distributed fracture radius is. The fracture complexity F cmplx (γ) can be represented as:

[0050]

[0051] As shown in FIG. 2, the distribution of the fracture radius when the fracture complexity is 20%; as shown in FIG. 3, the distribution of the fracture radius when the fracture complexity is 40%. Figure 2 Figure 3 S130, performing seismic response forward of the fractured reservoir according to the fracture simulation model.

[0052] Specifically, after the fracture simulation model is obtained, the seismic response forward of the fractured reservoir is performed by the fracture simulation model.

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

[0054] Optionally, in another optional embodiment of the present application, the seismic response forward of the fractured reservoir according to the fracture simulation model comprises:

[0055] predicting elastic parameters of the fractured reservoir according to the fracture simulation model; and performing forward modeling of seismic response of the fractured reservoir according to the elastic parameters.

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

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

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

[0059] obtaining a pressure response function of pore pressure and a plurality of multi-scale fracture group parameters in the fracture simulation model; determining a frequency-dependent equivalent stiffness matrix of the fracture simulation model according to the pressure response function; determining an equivalent medium stiffness matrix of the fracture simulation model according to the plurality of multi-scale fracture group parameters and the frequency-dependent equivalent stiffness matrix; and determining the elastic parameters of the fractured reservoir according to the equivalent medium stiffness matrix.

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

[0061] The multi-scale fracture group parameters can be fracture parameters of each fracture group. For example, the normal vector of the fracture group.

[0062] The frequency-dependent equivalent stiffness matrix can be an equivalent stiffness matrix of each part of the fracture medium rock.

[0063] The equivalent medium stiffness matrix can be a medium stiffness matrix for equivalent description of the fracture medium rock.

[0064] Specifically, the pressure response function of pore pressure and the plurality of multi-scale fracture group parameters in the fracture simulation model are obtained, the frequency-dependent equivalent stiffness matrix in the fracture simulation model is determined according to the pressure response function, and then the equivalent medium stiffness matrix of the fracture simulation model is determined according to the plurality of multi-scale fracture group parameters and the frequency-dependent equivalent stiffness matrix, and finally the elastic parameters of the fractured reservoir are determined according to the equivalent medium stiffness matrix.

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

[0066]

[0067]

[0068] where P represents the pressure function, n is the unit normal vector of the fracture surface, i, j represent the unit normal vector of different directions, and a represents the a-th group of fractures. The larger the value of a is, the more groups of fractures there are, and the greater the fracture density is. f represents the bulk modulus of the fluid in the pore space, which controls 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. a is the mesoscopic relaxation time of the a-th group of fractures. When the relaxation time is defined as infinity, the fluid between the fractures does not flow with the change of frequency, i.e., the fractures are opened or closed. i is the radius of the i-th group of fractures, and r is the pore radius and equal to the particle size.

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

[0070]

[0071] The formula of 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 m-th fracture group, i, j represent the unit normal vector of different directions. S represents the pressure function, K c represents the bulk modulus of the fluid in the pore space, which controls 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 m-th 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, δ is the stress tensor, υ is the 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 fractures can be calculated by the following formula:

[0075]

[0076] Further, the frequency-dependent equivalent stiffness matrix of the fracture simulation model can be represented by the following formula:

[0077]

[0078] where C ijkl (ω) is the frequency-dependent equivalent stiffness matrix; is the isotropic stiffness matrix of the background medium; is the stiffness matrix correction term related to the spherical pore, and the last term is the stiffness matrix correction term related to the M groups of fractures; ω represents the angular frequency, and φ and φ aThese represent the porosity corresponding to pores and crack group a, respectively. The superscript o indicates the background skeleton, the superscript p indicates pores, the superscript fa indicates crack group a, and the subscript ijkl indicates the dimension of the stiffness coefficient matrix.

[0079] After obtaining the frequency-varying equivalent stiffness matrix of the randomly distributed multi-scale fractured reservoir forward modeling device, the azimuth angle θ is defined. a and polarization angle Therefore, the normal vector of the a-th crack group can be expressed as:

[0080]

[0081] Therefore, the local coordinate system Ox1x2x3 of the crack is selected as follows:

[0082]

[0083] x2=(-sinθ a cosθ a ,0)

[0084]

[0085] Where x3 and n a Parallelism indicates that the crack correction term has a VTI form in the crack's local coordinate system. Therefore, the rotation matrix R is calculated, revealing that R is related to 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 as follows:

[0086]

[0087] A porosity correction term is introduced based on the influence of porosity parameters. The expression form is:

[0088]

[0089] The coefficients ξ1 and ξ2 are expressed in the following forms:

[0090]

[0091]

[0092] in, p represents the porosity corresponding to pore size p, υ is Poisson's ratio, λ and μ are Lamé coefficients, and p * σ represents the fluid pressure within the pores, 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] wherein, is a background medium term of the fracture simulation model, is a pore correction term of the fracture simulation model, C ijkl is an equivalent stiffness matrix of the fracture simulation model, a represents that the variable belongs to the mth group, and R is a rotation matrix of the fracture correction term.

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

[0097] forward modeling of seismic response of the fractured reservoir according to the P-wave and S-wave velocities and the density to determine seismic wave velocities and inverse quality factors of the fractured reservoir.

[0098] wherein the P-wave and S-wave velocities can be wave velocities of P-waves and S-waves in seismic waves.

[0099] wherein the density can be a density of the reservoir in the fractured reservoir.

[0100] wherein the seismic wave velocities can be phase velocities of P-waves, SV-waves and SH-waves.

[0101] wherein the inverse quality factors can be attenuation parameters for characterizing propagation of seismic waves in inelastic media.

[0102] Specifically, the P-wave and S-wave velocities and the density of the fractured reservoir are obtained, the forward modeling of seismic response of the fractured reservoir is performed according to the P-wave and S-wave velocities and the density, and the seismic wave velocities and the inverse quality factors of the fractured reservoir are calculated.

[0103] The P-wave and S-wave velocities and the density of the fractured reservoir are determined through a fracture simulation model of the fractured reservoir, an incident angle is defined as θ, and in a VTI medium, wave velocities of P-waves, SV-waves and SH-waves can be represented by the following formulas:

[0104]

[0105]

[0106]

[0107] wherein V P may be a wave velocity of P-waves; V SV may be a wave velocity of SV-waves; V SH may be a wave velocity of SH-waves; ρ is density; and M can be represented by the following formula:

[0108]

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

[0110]

[0111]

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

[0113] The technical scheme of the embodiment of the present application obtains the fracture complexity parameter and the random distribution fracture scale of the fracture type reservoir, can better simulate the complex fracture distribution in the fracture medium rock according to the fracture complexity parameter, can fit the fracture condition of the real fracture medium rock according to the random distribution fracture scale, and improves the performance of the rock model; the fracture simulation model of the fracture type reservoir is determined according to the random distribution fracture scale and the fracture complexity parameter, the random distribution fracture scale and the fracture complexity parameter are added in the determination of the fracture type reservoir model, the performance of the model is improved, and the accuracy of the simulation is improved; the seismic response forward of the fracture type reservoir is carried out according to the fracture simulation model, the elastic parameter of the fracture type reservoir can be determined through the fracture simulation model, the seismic response forward of the fracture type reservoir is carried out according to the elastic parameter, the technical problem that the multi-scale fracture medium rock simulation model is idealized in the prior art is solved, the simulation model is closer to the real reservoir, and the accuracy of the forward simulation is improved.

[0114] Embodiment two

[0115] Figure 4 is a flow chart of another random distribution fracture type reservoir forward simulation method provided by the embodiment two of the present application, and the relationship between the embodiment and the above-mentioned embodiment is the specific method of establishing the fracture simulation model. As shown in Figure 4 , the random distribution fracture type reservoir forward simulation method comprises:

[0116] S210, obtaining the fracture complexity parameter and the random distribution fracture scale of the fracture type reservoir.

[0117] S220, obtaining the matrix elastic modulus and the fluid modeling parameter of the fracture medium rock in the fracture type reservoir.

[0118] The matrix elastic modulus can be the elastic modulus of the matrix in the fracture medium rock.

[0119] The fluid modeling parameter can be the parameter for establishing the rock physics model in the fracture medium rock. For example, the fluid modeling parameter can include the fluid type and the permeability in the fracture medium rock.

[0120] Specifically, in the rock physics modeling, the matrix elastic modulus of each matrix of the fractured matrix rock and the fluid modeling parameter in the rock are obtained.

[0121] Optionally, in another optional embodiment of the present application, the obtaining of the matrix elastic modulus and the fluid modeling parameter of the fractured medium rock in the fractured reservoir comprises: obtaining a rock matrix composition and a matrix content of the fractured medium rock; determining the matrix elastic modulus of the fractured medium rock according to the rock matrix composition and the matrix content; obtaining logging data and a logging curve of the fractured medium rock; and determining the fluid modeling parameter of the fractured medium rock according to the logging data and the logging curve.

[0122] The matrix composition can be a matrix mineral composition of the fractured medium rock.

[0123] The matrix content can be a content of each matrix mineral composition of the fractured medium rock.

[0124] The logging data can be data obtained by a logging device from the reservoir.

[0125] The logging curve can be a curve recorded by the logging device.

[0126] Specifically, the rock matrix composition and the matrix content of the fractured medium rock are obtained by performing composition analysis on the fractured medium rock by using a laboratory rock analysis device; the matrix elastic model of the fractured medium rock is calculated according to the rock matrix composition and the matrix content; the logging data and the logging curve of the reservoir in which the fractured medium rock is located are obtained; and the fluid modeling parameter of the fractured medium rock is determined according to the logging data and the logging curve.

[0127] S230, determining a fracture simulation model of the fractured 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, the fluid modeling parameter, the randomly distributed fracture scale and the fracture complexity parameter are obtained; the rock physics modeling of the fractured medium rock in the fractured reservoir is performed; and the fracture simulation model of the fractured reservoir is determined.

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

[0130] obtaining a porosity of the fractured medium rock;

[0131] determine a physical framework model of the fractured medium rock according to the matrix elastic modulus;

[0132] determine a fluid model of the fractured medium rock according to the fluid modeling parameter and the porosity;

[0133] add multi-scale fractures to the physical framework model according to the randomly distributed fracture scale and the fracture complexity parameter, and combine the fluid model to obtain a fracture simulation model of the fractured reservoir.

[0134] The porosity can be a ratio of a pore space volume to a volume of the fractured medium rock.

[0135] The physical framework model can be a physical framework without pores and fluid in rock physical modeling.

[0136] The fluid model can be a model of fluid in rock physical modeling.

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

[0138] Specifically, the porosity of the fractured medium rock is obtained, the physical framework 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 parameter and the porosity, and then multi-scale fractures are added to the physical framework model of the fractured medium rock according to the randomly distributed fracture scale and the fracture complexity parameter, and the fluid model is combined to obtain the fracture simulation model of the fractured reservoir.

[0139] S240, performing seismic response forward modeling on the fractured reservoir according to the fracture simulation model.

[0140] The technical scheme of the embodiment of the present application obtains the crack complexity parameter and the randomly distributed crack scale of the crack reservoir, obtains the matrix elastic modulus and the fluid modeling parameter of the crack medium rock in the crack reservoir, determines the composition parameter and the fluid parameter of the crack medium rock, and performs physical modeling on the crack medium rock, so that the real rock can be fitted more closely. According to the matrix elastic modulus, the fluid modeling parameter, the randomly distributed crack scale and the crack complexity parameter, the crack simulation model of the crack reservoir is determined. In the rock physical modeling process, the randomly distributed crack scale and the crack complexity parameter are added, so that the performance of the model can be improved, and the accuracy of the simulation can be improved. According to the crack simulation model, the seismic response forward of the crack reservoir is performed. Through the crack simulation model, the elastic parameter of the crack reservoir can be determined, the seismic response forward of the crack reservoir is performed according to the elastic parameter, the technical problem that the multi-scale crack medium rock simulation model in the prior art is idealized is solved, the model simulation is closer to the real reservoir, and the accuracy of the forward simulation is improved.

[0141] Optionally, the embodiment of the present application provides another forward simulation method of a randomly distributed crack reservoir. Wherein, the rock matrix mineral composition and content are obtained by analyzing the randomly distributed crack reservoir, and the skeleton modulus of the randomly distributed crack reservoir is calculated by using rock physical modeling. Secondly, the crack information is introduced into the crack simulation model, and the cracks of the randomly distributed crack reservoir are described in detail by considering multiple parameters such as different fluid types, porosity, permeability, crack radius, crack density, etc. At the same time, in order to simulate the crack situation of the actual reservoir as much as possible, the crack scale is displayed in the form of random distribution, and crack simulation models with different complexities are established, and then the modeling of different fluid types, porosity, permeability, crack radius, crack density and other multiple parameters is effectively realized.

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

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

[0144]

[0145]

[0146] Through the modeling parameters in Table 1, the fluid type in the pore can be controlled from the microscopic point of view, and the opening and closing state of the crack, the crack radius and the crack density can be adjusted, and the adjustment of the relaxation time can simulate the permeability of the target reservoir.

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

[0148]

[0149] It is found by analysis that the average values of the fracture radius of the two distributions are on the order of 0.2 m, but there is a great difference in the dispersion degree of the fracture radius in different distribution cases. The wave velocity, quality factor and anisotropy parameter results obtained by forward modeling according to the random distribution multi-scale fracture model. Figure 5 is a comparative schematic diagram of qP wave phase velocity under different fracture complexities provided by the embodiment of the present application. As shown in Figure 5 : (a) is the qP wave phase velocity under different incident angles and different frequencies in the case of complexity 20%, (b) is the qP wave phase velocity under different incident angles and different frequencies in the case of complexity 40%, (c) is the influence of different incident angles on the qP wave phase velocity in the case of two fracture complexities at 10 Hz, (d) is the influence of different incident angles on the qP wave phase velocity in the case of two fracture complexities at 10 kHz, (e) is the influence of different frequencies on the qP wave phase velocity in the case of two fracture complexities when the incident angle is 0 degree, and (f) is the influence of different frequencies on the qP wave phase velocity in the case of two fracture complexities when the incident angle is 20 degrees.

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

[0151] Figure 7A comparison diagram of qSV wave phase velocity under different fracture complexities is provided for the embodiments of the present application. Figure 7 As shown in the figure: (a) is the qSV wave phase velocity under different incident angles and different frequencies when the complexity is 20%, (b) is the qSV wave phase velocity under different incident angles and different frequencies when the complexity is 40%, (c) is the influence of different incident angles on the qSV wave phase velocity under two fracture complexities when the frequency is 10Hz, (d) is the influence of different incident angles on the qSV wave phase velocity under two fracture complexities when the frequency is 10kHz, (e) is the influence of different frequencies on the qSV wave phase velocity under two fracture complexities when the incident angle is 0 degree, and (f) is the influence of different frequencies on the qSV wave phase velocity under two fracture complexities when the incident angle is 20 degrees.

[0152] Figure 8 A comparison diagram of qSV wave attenuation under different fracture complexities is provided for the embodiments of the present application. Figure 8 As shown in the figure: (a) is the qSV wave inverse quality factor calculation result under different incident angles and different frequencies when the complexity is 20%, (b) is the qSV wave inverse quality factor calculation result under different incident angles and different frequencies when the complexity is 40%, (c) is the influence of different incident angles on the qSV wave inverse quality factor under two fracture complexities when the frequency is 10Hz, (d) is the influence of different incident angles on the qSV wave inverse quality factor under two fracture complexities when the frequency is 10kHz, (e) is the influence of different frequencies on the qSV wave inverse quality factor under two fracture complexities when the incident angle is 0 degree, and (f) is the influence of different frequencies on the qSV wave inverse quality factor under two fracture complexities when the incident angle is 20 degrees.

[0153] Figure 9 A comparison diagram of qSH wave phase velocity under different fracture complexities is provided for the embodiments of the present application. Figure 9 As shown in the figure: (a) is the SH wave phase velocity under different incident angles and different frequencies when the complexity is 20%, (b) is the SH wave phase velocity under different incident angles and different frequencies when the complexity is 40%, (c) is the influence of different incident angles on the SH wave phase velocity under two fracture complexities when the frequency is 10Hz.

[0154] Figure 10 A comparison diagram of frequency curves of anisotropy parameters under different fracture complexities is provided for the embodiments of the present application. Figure 10 As shown in the figure: (a) is the frequency curve of the anisotropy parameter ε T , (b) is the frequency curve of the anisotropy parameter δ T , and (c) is the frequency curve of the anisotropy parameter γ T .

[0155] Further, according to the analysis result, it is shown that the crack radius does not affect the high and low frequency limits of the wave velocity, and only changes the frequency band range of the transition section, that is, the attenuation peak decreases with the decrease of the crack radius. Meanwhile, the crack radius of the model is approximately continuously distributed, and the effects of each group of cracks are superimposed on each other, the wave velocity-frequency curve of the model has a similar form, the center frequency of the transition section corresponds to the average of the crack radius, the frequency band of the transition section is widened, and the widening degree is positively correlated with the dispersion degree of the crack radius distribution. For the attenuation attribute in the simulation result, with the change of the complexity, the dispersion degree 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 value decreases. When the crack radius is continuously distributed, the transition section band of the model is wide and the attenuation peak is low, the center frequency of the transition section depends on the average of the crack radius, and the width of the transition section band depends on the variance of the crack radius distribution.

[0156] Figure 11 A synthetic seismogram of P-P wave with the change of the incident angle under the condition of a crack complexity of 20% is provided for the embodiment of the present application. As shown in Figure 11 (a) is a synthetic seismogram of P-P wave with the change of the incident angle under the condition of a crack complexity of 20%; Figure 12 A synthetic seismogram of P-P wave with the change of the incident angle under the condition of a crack complexity of 40% is provided for the embodiment of the present application. As shown in Figure 12 (b) is a synthetic seismogram of P-P wave with the change of the incident angle under the condition of a crack complexity of 40%. Figure 13 A synthetic seismogram of P-SV wave with the change of the incident angle under the condition of a crack complexity of 20% is provided for the embodiment of the present application. As shown in Figure 13 (a) is a synthetic seismogram of P-SV wave with the change of the incident angle under the condition of a crack complexity of 20%. Figure 14 A synthetic seismogram of P-SV wave with the change of the incident angle under the condition of a crack complexity of 40% is provided for the embodiment of the present application. As shown in Figure 14 (b) is a synthetic seismogram of P-SV wave with the change of the incident angle under the condition of a crack complexity of 40%. Figure 11 and Figure 12As shown, by analysis shows that in the case of the same other parameters of the model, the fracture type reservoir with different complexity has a certain influence on the longitudinal and transverse wave velocity and the peak value of attenuation. Since the average fracture radius of the fracture type reservoir with different complexity remains basically unchanged, the overall waveform of the synthetic seismogram is similar, but due to the influence of different complexity, the amplitude of the synthetic seismogram is changed to different degrees. When the fracture radius variance is small, the fracture complexity is lower, which means that more fractures are in the similar fracture radius scale, so the superposition effect is greater, and the influence on the reservoir is further increased. Therefore, the model can improve the elastic stiffness coefficient matrix of the fracture reservoir by macro-regulating the fracture complexity, and effectively show the random distribution fracture type reservoir forward simulation method with the random distribution fracture characteristics of the actual fracture medium.

[0157] Embodiment three

[0158] Figure 15 is a structure schematic diagram of a random distribution fracture type reservoir forward simulation device provided by the embodiment three of the present application. As shown in the figure, Figure 15 the device comprises a data acquisition module 310, a fracture model simulation module 320 and a seismic response forward module 330, wherein,

[0159] The data acquisition module 310 is used to acquire the fracture complexity parameter and the random distribution fracture scale of the fracture type reservoir.

[0160] The fracture model simulation module 320 is used to determine the fracture simulation model of the fracture type reservoir according to the random distribution fracture scale and the fracture complexity parameter.

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

[0162] The technical scheme of the embodiment of the present application obtains the crack complexity parameter and the random distribution crack scale of the crack type reservoir, can better simulate the complex crack distribution in the crack medium rock according to the crack complexity parameter, can fit the crack condition of the real crack medium rock according to the random distribution crack scale, and improves the performance of the rock model; the crack simulation model of the crack type reservoir is determined according to the random distribution crack scale and the crack complexity parameter, the random distribution crack scale and the crack complexity parameter are added in the determination of the crack type reservoir model, the performance of the model is improved, and the simulation accuracy is improved; the seismic response forward is carried out on the crack type reservoir according to the crack simulation model, the elastic parameter of the crack type reservoir can be determined through the crack simulation model, the seismic response forward is carried out on the crack type reservoir according to the elastic parameter, the technical problem that the multi-scale crack medium rock simulation model is idealized in the prior art is solved, the model simulation is closer to the real reservoir, and the accuracy of the forward simulation is improved.

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

[0164] The matrix elastic modulus and the fluid modeling parameter of the crack medium rock in the crack type reservoir are obtained.

[0165] The crack simulation model of the crack type reservoir is determined according to the matrix elastic modulus, the fluid modeling parameter, the random distribution crack scale and the crack complexity parameter.

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

[0167] The porosity of the crack medium rock is obtained.

[0168] The physical skeleton model of the crack medium rock is determined according to the matrix elastic modulus.

[0169] The fluid model of the crack medium rock is determined according to the fluid modeling parameter and the porosity.

[0170] The multi-scale crack is added to the physical skeleton model according to the random distribution crack scale and the crack complexity parameter, and the fluid model is combined to obtain the crack simulation model of the crack type reservoir.

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

[0172] The rock matrix component and the matrix content of the crack medium rock are obtained.

[0173] The matrix elastic modulus of the crack medium rock is determined according to the rock matrix component and the matrix content.

[0174] The logging data and the logging curve of the crack medium rock are obtained.

[0175] determining fluid modeling parameters of the fractured medium rock according to the logging data and the 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] performing seismic response forward modeling on the fractured reservoir according to the elastic parameters.

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

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

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

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

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

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

[0185] performing seismic response forward modeling on the fractured reservoir according to the P-wave and S-wave velocities and the density to determine seismic wave velocities and inverse quality factors of the fractured reservoir.

[0186] The random distribution fractured reservoir forward modeling device provided in the embodiments of the present application can perform the random distribution fractured reservoir forward modeling method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0187] Embodiment Four

[0188] Figure 16A structural diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0189] As shown in Figure 16 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. 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] Various 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 magnetic 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 can be various general and / or special purpose 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 special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the random distribution fracture type reservoir forward modeling method.

[0192] In some embodiments, the random distribution fracture type reservoir forward modeling method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the above-described random distribution fracture type reservoir forward modeling method can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the random distribution fracture type reservoir forward modeling method by other any suitable means, e.g., with the aid of firmware.

[0193] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0194] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, can implement the functions / acts specified in the flowcharts and / or block diagrams.

[0195] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0196] To provide for interaction with a user, the systems and techniques described here can 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 a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0197] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can 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] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can 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 large management difficulty and weak business scalability in traditional physical host and VPS service.

[0199] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and the present application does not limit this.

[0200] Example five

[0201] The embodiment provides a computer readable storage medium, which stores a computer program, the program is executed by a processor to realize the steps of the random distribution fracture type reservoir forward modeling method provided by any embodiment of the present application, the method comprises:

[0202] Obtaining a fracture complexity parameter and a random distribution fracture size of a fracture type reservoir;

[0203] According to the random distribution fracture size and the fracture complexity parameter, a fracture simulation model of the fracture type reservoir is determined;

[0204] According to the fracture simulation model, a seismic response forward of the fracture type reservoir is performed.

[0205] The computer storage medium of the embodiment of the present application 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 may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more conductive wires, a portable computer diskette, 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, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0206] A computer readable signal medium can include a propagated data signal with computer executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium can be any computer readable medium that can be involved in

[0207] The code can be transmitted in any form, including, but not limited to, radio frequency, electrical, optical, acoustical, or any form that can be used for transferring the code from one place to another.

[0208] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0209] Those skilled in the art will appreciate that the modules or steps of the present application described above can be implemented in a general purpose computer, which can be centralized or distributed over a network of multiple computers, and optionally, they can be implemented in program code executable by a computer, which can be stored in a storage device and executed by the computer, or they can be implemented in individual integrated circuit modules or a combination of some of them in a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0210] It should be understood that the various forms of flow shown in the figures can be re-ordered, added to, or deleted from, without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in different orders, as long as the desired results of the present application are achieved, and the present application is not limited in this regard.

[0211] The above detailed description does not limit the scope of the application. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the application shall be included in the scope of the application.

Claims

1. A forward modeling method for randomly distributed fractured reservoirs, characterized in that, include: Obtain fracture complexity parameters and randomly distributed fracture scale of fractured reservoirs; Based on the randomly distributed fracture scale and the fracture complexity parameter, a fracture simulation model for the fractured reservoir is determined. Seismic response forward modeling was performed on the fractured reservoir based on the fracture simulation model. The step of determining the fracture simulation model of the fractured reservoir based on the randomly distributed fracture scale and the fracture complexity includes: Obtain the matrix elastic modulus and fluid modeling parameters of fractured media rocks in fractured reservoirs; The fracture simulation model of the fractured reservoir is determined based on the matrix elastic modulus, the fluid modeling parameters, the randomly distributed fracture scale, and the fracture complexity parameters. Based on the matrix elastic modulus, the fluid modeling parameters, the randomly distributed fracture scale, and the fracture complexity parameters, the fracture simulation model of the fractured reservoir is determined, including: The porosity of the rock in the fractured medium was obtained; The physical framework model of the fractured medium rock is determined based on the matrix elastic modulus. The fluid model of the fractured rock is determined based on the fluid modeling parameters and the porosity. Multi-scale fractures are added to the physical skeleton model based on the randomly distributed fracture scale and the fracture complexity parameter, and the fluid model is then merged to obtain the fracture simulation model of the fractured reservoir. The acquisition of the matrix elastic modulus and fluid modeling parameters of the fractured medium rock in the fractured reservoir includes: Obtain the matrix composition and matrix content of the rock in the fractured medium; The matrix elastic modulus of the fractured medium rock is determined based on the rock matrix composition and the matrix content. Obtain well logging data and logging curves of the fractured rock medium; Based on the well logging data and the well logging curves, the fluid modeling parameters of the fractured rock medium are determined.

2. The method according to claim 1, characterized in that, The step of performing seismic response forward modeling on the fractured reservoir based on the fracture simulation model includes: Predict the elastic parameters of the fractured reservoir based on the fracture simulation model; Seismic response forward modeling was performed on the fractured reservoir based on the elastic parameters.

3. The method according to claim 2, characterized in that, The step of predicting the elastic parameters of the fractured reservoir based on the fracture simulation model includes: Obtain the pressure response function of pore pressure and multiple multi-scale crack group parameters in the crack simulation model; The frequency-varying equivalent stiffness matrix of the crack simulation model is determined based on the pressure response function. The equivalent medium stiffness matrix of the crack simulation model is determined based on the multiple multi-scale crack group parameters and the frequency-varying equivalent stiffness matrix. The elastic parameters of the fractured reservoir are determined based on the equivalent medium stiffness matrix.

4. The method according to claim 3, characterized in that, The elastic parameters include P-wave and S-wave velocities and densities; the forward seismic response modeling of the fractured reservoir based on the elastic parameters includes: Based on the P-wave and S-wave velocities and densities, a forward seismic response model of the fractured reservoir is performed to determine the seismic wave velocity and inverse quality factor of the fractured reservoir.

5. A forward modeling device for randomly distributed fractured reservoirs, characterized in that, include: The data acquisition module is used to acquire fracture complexity parameters and randomly distributed fracture scale of fractured reservoirs; The fracture model simulation module is used to determine the fracture simulation model of the fractured reservoir based on the randomly distributed fracture scale and the fracture complexity parameter. The seismic response forward modeling module is used to perform seismic response forward modeling on the fractured reservoir based on the fracture simulation model. The crack model simulation module is specifically used for: Obtain the matrix elastic modulus and fluid modeling parameters of fractured media rocks in fractured reservoirs; The fracture simulation model of the fractured reservoir is determined based on the matrix elastic modulus, the fluid modeling parameters, the randomly distributed fracture scale, and the fracture complexity parameters. The crack model simulation module is also specifically used for: The porosity of the rock in the fractured medium was obtained; The physical framework model of the fractured medium rock is determined based on the matrix elastic modulus. The fluid model of the fractured rock is determined based on the fluid modeling parameters and the porosity. Multi-scale fractures are added to the physical skeleton model based on the randomly distributed fracture scale and the fracture complexity parameter, and the fluid model is then merged to obtain the fracture simulation model of the fractured reservoir. The crack model simulation module is also specifically used for: Obtain the matrix composition and matrix content of the rock in the fractured medium; The matrix elastic modulus of the fractured medium rock is determined based on the rock matrix composition and the matrix content. Obtain well logging data and logging curves of the fractured rock medium; Based on the well logging data and the well logging curves, the fluid modeling parameters of the fractured rock medium are determined.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the forward modeling method for randomly distributed fractured reservoirs as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the forward modeling method for randomly distributed fractured reservoirs as described in any one of claims 1-4.

Images