Wave propagation prediction method and device under multiphase flow condition, equipment and medium

Abstract

This application provides a method, apparatus, device, and medium for predicting wave propagation under multiphase flow conditions. The method includes: acquiring the fluid parameters of each phase fluid in an unconventional oil and gas reservoir containing multiphase fluids; determining the pore fluid properties under multiphase flow conditions based on the fluid parameters of the multiphase fluids; constructing a unified multi-physics mechanism wave propagation model for unconventional oil and gas reservoirs based on the pore fluid properties and solid skeleton properties; the multi-physics mechanism includes a non-Darcy flow mechanism, a fractional-order viscoelastic mechanism, and a multiphase flow coexistence mechanism; and performing plane wave analysis on the unconventional oil and gas reservoir wave propagation model to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions. The model constructed in this way can more reasonably characterize the changes in fluid compressibility, flow resistance, and inertial characteristics under multiphase flow conditions, thereby more accurately characterizing the influence of multiphase fluids on wave propagation characteristics in actual unconventional oil and gas reservoirs.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and more specifically, to a method, apparatus, equipment, and medium for predicting wave propagation under multiphase flow conditions. Background Technology

[0002] As conventional oil and gas exploration and development gradually enters a bottleneck period, the development and utilization of unconventional oil and gas reservoirs are of great significance. Studying the propagation law of seismic waves in complex porous media is an important foundation for achieving precise exploration of unconventional oil and gas reservoirs. Unconventional oil and gas reservoirs are generally characterized by complex pore structures and strong heterogeneity of the medium. When seismic waves propagate within these reservoirs, they are affected by multiple factors coupled together, such as the framework structure and the differences in the physical properties of the pore-filling medium.

[0003] Due to the complex pore fluids within unconventional oil and gas reservoirs, differences in medium properties affect wave propagation, resulting in complex characteristics in the actual wave propagation response. Traditional wave propagation analysis and prediction methods mostly rely on idealized medium assumptions for simplification, failing to reflect the actual impact of fluid differences within the reservoir and deviating from the actual wave propagation characteristics under real reservoir geological conditions. Therefore, how to predict wave propagation in unconventional oil and gas reservoirs has become an urgent problem to be solved. Summary of the Invention

[0004] In view of this, this application provides a wave propagation prediction method, apparatus, equipment and medium under multiphase flow conditions, which equates the fluid parameters of multiphase fluid to the pore fluid physical property parameters under multiphase flow conditions, thereby constructing a unified wave propagation model of unconventional oil and gas reservoirs with multiple physical mechanisms. This allows the wave dispersion attenuation characteristics of unconventional oil and gas reservoirs under multiphase flow conditions to be obtained, effectively realizing wave propagation prediction for unconventional oil and gas reservoirs.

[0005] Specifically, this application is implemented through the following technical solution: According to a first aspect of this application, a wave propagation prediction method under multiphase flow conditions is provided, the method comprising: For each phase of fluid in an unconventional oil and gas reservoir, fluid parameters for each phase of fluid are obtained; the unconventional oil and gas reservoir includes multiphase fluids. Based on the fluid parameters of the multiphase fluid, the pore fluid properties under multiphase flow conditions are determined; the pore fluid properties are used to characterize the fluid behavior in the porous medium. Based on the pore fluid properties and solid skeleton properties, a unified unconventional oil and gas reservoir wave propagation model with multiple physical mechanisms is constructed. The solid skeleton properties are used to characterize the solid skeleton in the porous medium. The multiple physical mechanisms include non-Darcy flow mechanism, fractional-order viscoelastic mechanism, and multiphase flow coexistence mechanism. Plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0006] In one optional embodiment, the multiphase fluid includes a gas phase fluid and a liquid phase fluid, the fluid parameters include saturation, density, viscosity and bulk modulus, and the pore fluid physical properties include pore fluid density, fluid viscosity and Biot fluid storage coefficient. The determination of equivalent fluid parameters under multiphase flow conditions based on the fluid parameters of the multiphase fluid includes: The pore fluid density is determined based on the saturation and density of the multiphase fluid; The viscosity of the fluid is determined based on the saturation and viscosity of the multiphase fluid; The Biot fluid storage coefficient is determined based on the bulk modulus of the multiphase fluid and the saturation of the liquid phase fluid.

[0007] In one optional implementation, the step of performing plane wave analysis on the unconventional oil and gas reservoir wave propagation model to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Determine the fluid viscoelastic parameters of the unconventional oil and gas reservoir wave propagation model; the fluid viscoelastic parameters are used to characterize the viscoelastic memory effect of pore fluids; Based on the fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0008] In one optional embodiment, the fluid viscoelastic parameters include the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, a first characteristic angular frequency, a second characteristic angular frequency, a first reference quality factor, and a second reference quality factor. The first characteristic angular frequency and the second characteristic angular frequency are determined based on the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, and the relaxation time parameter of the viscoelastic mechanism. The first reference quality factor and the second reference quality factor are determined based on the relaxation time parameter of the viscoelastic mechanism. The method further includes: Based on the wave propagation property data, at least one parameter included in the fluid viscoelastic parameters is updated to obtain the updated fluid viscoelastic parameters. The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Based on the updated fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0009] In one optional implementation, the attribute data includes the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, and the longitudinal wave low-frequency limiting velocity. The first reference quality factor and the second reference quality factor are updated using the following steps: Based on the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, and the longitudinal wave low-frequency limiting velocity, the first reference quality factor is updated to obtain the updated first reference quality factor. Based on the shear wave high-frequency limiting velocity and the shear wave low-frequency limiting velocity, the second reference quality factor is updated to obtain the updated second reference quality factor.

[0010] In one optional implementation, the attribute data includes shear wave dispersion data; The fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton are updated by the following steps: Based on the initial values ​​of the fractional order of the shear modulus of the solid skeleton and the initial value of the second characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted shear wave velocity. Using the predicted shear wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the shear wave dispersion data to obtain at least one of the updated fractional order of the shear modulus of the solid skeleton and the updated second characteristic angular frequency.

[0011] In one optional implementation, the attribute data includes longitudinal wave dispersion data; The fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton are updated by the following steps: Based on the initial values ​​of the fractional order of the bulk modulus of the solid skeleton and the initial value of the first characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted longitudinal wave velocity. Using the predicted longitudinal wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the longitudinal wave dispersion data to obtain at least one of the updated bulk modulus fractional order and the updated first characteristic angular frequency of the solid skeleton.

[0012] In one optional implementation, the wave dispersion attenuation characteristics include shear wave velocity, shear wave inverse quality factor, longitudinal wave velocity, and longitudinal wave inverse quality factor. The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Based on the fluid viscoelastic parameters, the displacement field of the wave is decomposed to obtain the transverse wave field and the longitudinal wave field, and the transverse wave field and the longitudinal wave field are subjected to Fourier transform to obtain the Fourier transformed transverse wave field and the Fourier transformed longitudinal wave field. The transverse wave field and the longitudinal wave field after Fourier transform are respectively subjected to formal transformation to obtain the formally transformed transverse wave field and the formally transformed longitudinal wave field; the formally transformed transverse wave field and the formally transformed longitudinal wave field are matrix multiplication forms of constant variables multiplied by coefficient matrices. When the constant variable is non-zero, the first coefficient matrix is ​​extracted from the transformed transverse wave field, and the second coefficient matrix is ​​extracted from the transformed longitudinal wave field. The transverse wave complex number is obtained when the determinant of the first coefficient matrix is ​​zero, and the longitudinal wave complex number is obtained when the determinant of the second coefficient matrix is ​​zero. Based on the shear wave complex number, the shear wave velocity and the shear wave inverse quality factor are determined; based on the longitudinal wave complex number, the longitudinal wave velocity and the longitudinal wave inverse quality factor are determined.

[0013] According to a second aspect of this application, a wave propagation prediction device under multiphase flow conditions is provided, the device comprising: The parameter acquisition module is used to acquire the fluid parameters of each phase fluid in an unconventional oil and gas reservoir; the unconventional oil and gas reservoir includes multiphase fluids. The parameter construction module is used to determine the pore fluid physical property parameters under multiphase flow conditions based on the fluid parameters of the multiphase fluid; the pore fluid physical property parameters are used to characterize the fluid behavior in the porous medium. The model building module is used to construct a unified unconventional oil and gas reservoir wave propagation model based on the pore fluid properties and solid skeleton properties. The solid skeleton properties are used to characterize the solid skeleton in the porous medium. The multi-physics mechanisms include non-Darcy flow mechanism, fractional-order viscoelastic mechanism, and multiphase flow coexistence mechanism. The feature analysis module is used to perform plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0014] According to a third aspect of this application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the wave propagation prediction method under multiphase flow conditions described in the first aspect above.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the wave propagation prediction method under multiphase flow conditions described in the first aspect above.

[0016] The wave propagation prediction method, apparatus, equipment, and medium provided in this application's embodiments under multiphase flow conditions equate the fluid parameters of each phase fluid in unconventional oil and gas reservoirs to the pore fluid properties under multiphase flow conditions. This constructs a unified wave propagation model for unconventional oil and gas reservoirs that integrates non-Darcy flow mechanisms, fractional-order viscoelastic mechanisms, and multiphase flow coexistence mechanisms. The constructed model can more reasonably characterize the variations in fluid compressibility, flow resistance, and inertial characteristics under multiphase flow conditions, improving the model's physical consistency. Plane wave analysis of this model yields the wave dispersion attenuation characteristics of unconventional oil and gas reservoirs under multiphase flow conditions, effectively enabling wave propagation prediction for unconventional oil and gas reservoirs. This more accurately characterizes the influence of multiphase fluids on wave propagation characteristics in actual unconventional oil and gas reservoirs, improving the accuracy of wave propagation prediction for unconventional oil and gas reservoirs.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this disclosure.

[0018] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a wave propagation prediction method under multiphase flow conditions, as shown in an exemplary embodiment of this application; Figure 2 This is a schematic diagram illustrating a wave propagation prediction process under multiphase flow conditions, as shown in an exemplary embodiment of this application. Figure 3 This is a schematic diagram illustrating a longitudinal wave dispersion characteristic according to an exemplary embodiment of this application; Figure 4 This is a schematic diagram illustrating a transverse wave dispersion characteristic according to an exemplary embodiment of this application; Figure 5 This is a schematic diagram illustrating another longitudinal wave dispersion characteristic according to an exemplary embodiment of this application; Figure 6This is a schematic diagram illustrating another transverse wave dispersion characteristic according to an exemplary embodiment of this application; Figure 7 This is a schematic diagram of a wave propagation prediction device under multiphase flow conditions, as illustrated in an exemplary embodiment of this application; Figure 8 This is a schematic diagram of the structure of a computer device shown in an exemplary embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0023] Research has shown that since the Biot model proposed the theory of wave propagation in fluid-porous media, related models have continued to develop, but traditional wave propagation models are no longer able to meet the actual needs of exploration in complex and unconventional oil and gas reservoirs.

[0024] Pore ​​fluids in unconventional oil and gas reservoirs typically exhibit a multiphase coexistence state. Variations in the saturation of multiphase fluids affect fluid parameters, which in turn influence wave propagation processes. However, most existing models simplify pore fluids to single-phase fluids, making it difficult to realistically reflect wave propagation patterns under multiphase flow conditions. Therefore, predicting wave propagation in unconventional oil and gas reservoirs has become an urgent problem to be solved.

[0025] Based on the above research, this application provides a wave propagation prediction method under multiphase flow conditions. By equating the fluid parameters of each phase fluid in unconventional oil and gas reservoirs to the pore fluid properties under multiphase flow conditions, a unified unconventional oil and gas reservoir wave propagation model integrating non-Darcy flow mechanism, fractional-order viscoelastic mechanism, and multiphase flow coexistence mechanism is constructed. Plane wave analysis of this model can more accurately characterize the influence of multiphase fluids on wave propagation characteristics in actual unconventional oil and gas reservoirs, and effectively realize wave propagation prediction for unconventional oil and gas reservoirs.

[0026] To facilitate understanding of this embodiment, a detailed description of the wave propagation prediction method under multiphase flow conditions disclosed in this application embodiment is provided first. The execution entity of the wave propagation prediction method under multiphase flow conditions provided in this application embodiment is generally a computer device with certain computing capabilities. This computer device can be a server, which can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. In some possible implementations, the computer device can also be a terminal device, which can be a mobile device, terminal, handheld device, computing device, vehicle-mounted device, etc. In other implementations, the wave propagation prediction method under multiphase flow conditions can be applied to an implementation environment composed of a terminal device and a server. Furthermore, the wave propagation prediction method under multiphase flow conditions can also be implemented by a processor calling computer-readable instructions stored in memory.

[0027] The wave propagation prediction method under multiphase flow conditions provided by the embodiments of this application will be described below with reference to the accompanying drawings.

[0028] See Figure 1 The diagram shown is a flowchart illustrating a wave propagation prediction method under multiphase flow conditions, as presented in an exemplary embodiment of this application. Figure 1 As shown in the embodiments of this disclosure, the wave propagation prediction method under multiphase flow conditions includes steps S101 to S104, wherein: S101: For each phase fluid in an unconventional oil and gas reservoir, obtain the fluid parameters of each phase fluid; the unconventional oil and gas reservoir includes multiphase fluids.

[0029] Here, the unconventional oil and gas reservoir includes multiphase fluids, which include gaseous fluids and liquid fluids. In practical applications, the gaseous fluid is, for example, air or natural gas, and the liquid fluid is, for example, water or petroleum. For each phase fluid in the unconventional oil and gas reservoir, the fluid parameters for each phase fluid are obtained.

[0030] S102: Based on the fluid parameters of the multiphase fluid, determine the pore fluid properties under multiphase flow conditions; the pore fluid properties are used to characterize the fluid behavior in the porous medium.

[0031] Here, this embodiment of the disclosure takes into account that if single-phase fluid parameters are directly used under multiphase flow conditions, or only some fluid parameters are replaced, it is difficult to accurately reflect the overall change law of model parameters under multiphase flow conditions. Therefore, the fluid parameters of each phase fluid in unconventional oil and gas reservoirs are equivalent to the pore fluid physical property parameters under multiphase flow conditions, so as to construct a wave propagation model under multiphase flow conditions suitable for prediction in actual work areas.

[0032] In some possible implementations, the multiphase fluid includes a gas phase fluid and a liquid phase fluid, the fluid parameters include saturation, density, viscosity and bulk modulus, and the pore fluid properties include pore fluid density, fluid viscosity and Biot fluid storage coefficient. The determination of pore fluid properties under multiphase flow conditions based on the fluid parameters of the multiphase fluid includes: The pore fluid density is determined based on the saturation and density of the multiphase fluid; The viscosity of the fluid is determined based on the saturation and viscosity of the multiphase fluid; The Biot fluid storage coefficient is determined based on the bulk modulus of the multiphase fluid and the saturation of the liquid phase fluid.

[0033] In the above steps, the density of each phase fluid can be obtained by weighted summation based on the saturation of each phase fluid.

[0034] Here, in order to ensure the rationality of determining the pore fluid density and fluid viscosity, the saturation of each phase fluid can be normalized so that the sum of the saturation of each phase fluid is 1.

[0035] Specifically, the relationship between the saturation of each phase fluid can be expressed by the following formula (1): (1) in, Indicates the first The saturation of a liquid phase fluid. This indicates the total number of liquid phase fluid types included in unconventional oil and gas reservoirs. Indicates the first The saturation of a gaseous fluid. This indicates the total number of gaseous fluid types included in unconventional oil and gas reservoirs.

[0036] Specifically, the pore fluid density can be determined by the following formula (2): (2) in, Indicates pore fluid density. Indicates the first The saturation of a liquid phase fluid. Indicates the first The density of a liquid phase fluid, This indicates the total number of liquid phase fluid types included in unconventional oil and gas reservoirs. Indicates the first The saturation of a gaseous fluid. Indicates the first The density of a gaseous fluid, This indicates the total number of gaseous fluid types included in unconventional oil and gas reservoirs.

[0037] The viscosity of the fluid can be obtained by weighted summation of the logarithms of the viscosity of each phase fluid based on the saturation of each phase fluid.

[0038] Specifically, the viscosity of the fluid can be determined by the following formula (3): (3) in, Indicates fluid viscosity. Indicates the first The saturation of a liquid phase fluid. Indicates the first The viscosity of a liquid fluid. This indicates the total number of liquid phase fluid types included in unconventional oil and gas reservoirs. Indicates the first The saturation of a gaseous fluid. Indicates the first The viscosity of a gaseous fluid. This indicates the total number of gaseous fluid types included in unconventional oil and gas reservoirs. It represents a logarithm with base 10.

[0039] Specifically, when determining the Biot fluid storage coefficient, the equivalent liquid phase fluid bulk modulus can be determined based on the bulk modulus and saturation of each liquid phase fluid, and the equivalent gas phase fluid bulk modulus can be determined based on the bulk modulus and saturation of each gas phase fluid; the equivalent fluid bulk modulus can be determined based on the equivalent liquid phase fluid bulk modulus, the equivalent gas phase fluid bulk modulus, and the saturation of each liquid phase fluid; and the Biot fluid storage coefficient can be determined based on the equivalent fluid bulk modulus, the Biot-Williams coefficient, porosity, and the bulk modulus of the solid skeleton.

[0040] Specifically, the equivalent liquid phase fluid bulk modulus and the equivalent gas phase fluid bulk modulus can be determined by the following formula (4): (4) in, Indicates the bulk modulus of the equivalent liquid phase fluid. Indicates the first The saturation of a liquid phase fluid. This indicates the total number of liquid phase fluid types included in unconventional oil and gas reservoirs. Indicates the first The bulk modulus of a liquid fluid Indicates the equivalent gaseous fluid bulk modulus. Indicates the first The saturation of a gaseous fluid. This indicates the total number of gaseous fluid types included in unconventional oil and gas reservoirs. Indicates the first The bulk modulus of a gaseous fluid.

[0041] The equivalent fluid bulk modulus can be determined by the following formula (5): (5) in, Represents the equivalent fluid bulk modulus. Indicates the bulk modulus of the equivalent liquid phase fluid. Indicates the equivalent gaseous fluid bulk modulus. Indicates the first The saturation of a liquid phase fluid. This indicates the total number of liquid phase fluid types included in unconventional oil and gas reservoirs. An empirical index representing the spatial distribution of fluid.

[0042] Specifically, the Bio fluid storage coefficient can be determined by the following formula (6): (6) in, Indicates the Biot fluid storage coefficient. Represents the equivalent fluid bulk modulus. This represents the Biot-Williams coefficient. Indicates porosity. This represents the bulk modulus of a solid skeleton.

[0043] In this way, the fluid parameters of multiphase flow can be equivalent to pore fluid density, fluid viscosity, and Biot fluid storage coefficient, effectively quantifying the contribution of each phase flow to pore fluid properties. Compared with the simplified treatment of fluid properties in traditional methods, this approach can more precisely characterize the pore fluid properties under multiphase flow coexistence conditions, providing an accurate, reliable, and realistic parameter basis for the construction of subsequent wave propagation models, and ensuring the authenticity and accuracy of wave propagation prediction in unconventional oil and gas reservoirs under multiphase flow conditions.

[0044] S103: Based on the pore fluid properties and solid skeleton properties, a unified unconventional oil and gas reservoir wave propagation model with multiple physical mechanisms is constructed; the solid skeleton properties are used to characterize the solid skeleton in the porous medium; the multiple physical mechanisms include non-Darcy flow mechanism, fractional-order viscoelastic mechanism and multiphase flow coexistence mechanism.

[0045] In this step, solid skeleton physical properties can be collected. Based on the pore fluid physical properties and solid skeleton physical properties, a unified unconventional oil and gas reservoir wave propagation model with multiple physical mechanisms can be constructed. The solid skeleton physical properties include the bulk modulus of the solid skeleton, the shear modulus of the solid skeleton, the relaxation time of the viscoelastic mechanism, the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, the Biot-Williams coefficient, porosity, permeability, power-law exponent, solid phase displacement, fluid phase displacement, solid matrix density, and solid-fluid coupling density.

[0046] Specifically, the multi-physics mechanism unified unconventional oil and gas reservoir wave propagation model can be expressed by the following formula (7): (7) in, The bulk modulus of a solid skeleton; Represents the shear modulus of a solid skeleton; This represents the first relaxation time of the viscoelastic mechanism. This represents the second relaxation time of the viscoelastic mechanism. This represents the third relaxation time of the viscoelastic mechanism. This represents the fourth relaxation time of the viscoelastic mechanism; Indicates time; The order of the bulk modulus of a solid framework is indicated by its fractional order. It is the first order of the fractional derivative. Used to characterize the memory effect in the volumetric deformation response of a medium; The fractional order of the shear modulus of a solid skeleton is represented by the following: It is the second order of the fractional derivative. Used to characterize the memory effect in the shear deformation response of a medium; Represents the horizontal direction in three-dimensional space; Indicates the biofluid storage coefficient; This represents the Biot-Williams coefficient; Indicates fluid viscosity; Indicates porosity; Indicates penetration rate; Indicates the power-law exponent; Indicates solid-phase displacement; Indicates the flow phase displacement; Represents the strain tensor of a solid. Represents the solid-state displacement tensor; Represents the fluid strain tensor. Represents the flow phase displacement tensor; , , ,in Indicates the density of the solid matrix. Represents the solid-fluid coupling density. This indicates the density of the pore fluid.

[0047] It can be expressed by the following formula (8): (8) in, Represents the gamma function. Indicates time, The order of the bulk modulus of a solid framework is indicated by its fractional order. The fractional order of the shear modulus of a solid skeleton is represented by the following: This represents the first relaxation time of the viscoelastic mechanism. This represents the second relaxation time of the viscoelastic mechanism.

[0048] In unconventional oil and gas reservoirs, pore fluid seepage often does not obey Darcy's law, exhibiting significant non-Darcy flow characteristics. This renders conventional models based on Darcy's law unsuitable for unconventional oil and gas reservoirs. Simultaneously, some fluid remains trapped in poorly connected pore structures, exhibiting viscoelastic dissipation effects together with the solid framework. Fractional-order viscoelastic mechanisms can effectively characterize this energy dissipation behavior caused by microscopic fluid trapping. Traditional wave propagation models struggle to simultaneously account for non-Darcy flow, fractional-order viscoelastic mechanisms, and the coexistence of multiphase fluids. However, the unconventional oil and gas reservoir wave propagation model constructed in this embodiment can simultaneously account for non-Darcy flow, fractional-order viscoelastic mechanisms, and the coexistence of multiphase fluids, thus more accurately characterizing the propagation characteristics of seismic waves in unconventional oil and gas reservoir media.

[0049] S104: Perform plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0050] In this step, the unconventional oil and gas reservoir wave propagation model can be solved using plane wave analysis to obtain the wave dispersion and attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions. This enables effective prediction of wave propagation characteristics under multiphase flow conditions, thus effectively solving the problem that traditional single-phase flow models cannot effectively characterize wave propagation laws under mixed underground fluid conditions. It can clearly depict the wave dispersion and attenuation behavior under multiphase flow conditions and is suitable for practical applications such as seismic wave propagation simulation and reservoir property parameter inversion in unconventional oil and gas reservoirs.

[0051] In some possible implementations, performing plane wave analysis on the unconventional oil and gas reservoir wave propagation model to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Determine the fluid viscoelastic parameters of the unconventional oil and gas reservoir wave propagation model; the fluid viscoelastic parameters are used to characterize the viscoelastic memory effect of pore fluids; Based on the fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0052] Here, since the wave propagation model that simultaneously considers multiphase flow, non-Darcy flow, and fractional-order viscoelastic mechanism is quite complex and difficult to solve directly, fluid viscoelastic parameters can be extracted from the unconventional oil and gas reservoir wave propagation model. Based on these fluid viscoelastic parameters, plane wave analysis can be performed on the unconventional oil and gas reservoir wave propagation model, which can more directly determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0053] In this way, by using fluid viscoelastic parameters to perform plane wave analysis on the unconventional oil and gas reservoir wave propagation model, the intrinsic influence of the viscoelastic memory characteristics of the pore fluid itself on the wave propagation process can be fully considered. Compared with the traditional analysis method that ignores the fluid viscoelastic effect and only performs simplification, it can accurately obtain the dispersion variation law and energy attenuation characteristics of seismic waves propagating in the reservoir pore medium under multiphase flow conditions, so that the obtained wave dispersion attenuation characteristics are more in line with the propagation law under the real geological conditions of unconventional oil and gas reservoirs.

[0054] In some possible implementations, the fluid viscoelastic parameters include the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, a first characteristic angular frequency, a second characteristic angular frequency, a first reference quality factor, and a second reference quality factor. The first and second characteristic angular frequencies are determined based on the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, and the relaxation time parameter of the viscoelastic mechanism. The first and second reference quality factors are determined based on the relaxation time parameter of the viscoelastic mechanism.

[0055] Refer to formula (7) to obtain the fractional order of the bulk modulus of the solid skeleton. Fractional order of shear modulus of solid skeleton The first relaxation time of the viscoelastic mechanism The second relaxation time of the viscoelastic mechanism The third relaxation time of the viscoelastic mechanism and the fourth relaxation time of the viscoelastic mechanism First relaxation time based on viscoelastic mechanism The second relaxation time of the viscoelastic mechanism The third relaxation time of the viscoelastic mechanism and the fourth relaxation time of the viscoelastic mechanism Fractional order of the bulk modulus of a solid skeleton and the fractional order of the shear modulus of the solid skeleton Determine the first characteristic angular frequency and the second characteristic angular frequency; the first relaxation time based on the viscoelastic mechanism. The second relaxation time of the viscoelastic mechanism The third relaxation time of the viscoelastic mechanism and the fourth relaxation time of the viscoelastic mechanism The first reference quality factor and the second reference quality factor are determined.

[0056] Specifically, it can be represented by the following formula (9): (9) in, Indicates the first characteristic angular frequency. Indicates the second characteristic angular frequency. Indicates the first reference quality factor. Indicates the second reference quality factor. This represents the first relaxation time of the viscoelastic mechanism. This represents the second relaxation time of the viscoelastic mechanism. This represents the third relaxation time of the viscoelastic mechanism. This represents the fourth relaxation time of the viscoelastic mechanism. The order of the bulk modulus of a solid framework is indicated by its fractional order. This indicates the fractional order of the shear modulus of a solid skeleton.

[0057] In some possible implementations, the wave dispersion attenuation characteristics include shear wave velocity, inverse shear wave quality factor, longitudinal wave velocity, and inverse longitudinal wave quality factor. The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: The displacement field of the wave is decomposed to obtain the transverse wave field and the longitudinal wave field; Based on the fluid viscoelastic parameters, the transverse wave field and the longitudinal wave field are subjected to Fourier transform processing to obtain the Fourier transformed transverse wave field and the Fourier transformed longitudinal wave field. The transverse wave field and the longitudinal wave field after Fourier transform are respectively subjected to formal transformation to obtain the formally transformed transverse wave field and the formally transformed longitudinal wave field; the formally transformed transverse wave field and the formally transformed longitudinal wave field are matrix multiplication forms of constant variables multiplied by coefficient matrices. When the constant variable is non-zero, the first coefficient matrix is ​​extracted from the transformed transverse wave field, and the second coefficient matrix is ​​extracted from the transformed longitudinal wave field. The transverse wave complex number is obtained when the determinant of the first coefficient matrix is ​​zero, and the longitudinal wave complex number is obtained when the determinant of the second coefficient matrix is ​​zero. Based on the shear wave complex number, the shear wave velocity and the shear wave inverse quality factor are determined; based on the longitudinal wave complex number, the longitudinal wave velocity and the longitudinal wave inverse quality factor are determined.

[0058] In the above steps, the displacement field of the seismic wave can be decomposed into a rotational, source-free shear wave field and a source-irrotational P-wave field according to the Helmholtz decomposition theorem, thereby achieving decoupling between the shear wave field and the P-wave field.

[0059] Specifically, according to the Helmholtz decomposition theorem, the solid-phase displacement tensor and the flow-phase displacement tensor can be decomposed into the sum of the gradient of the scalar potential and the curl of the vector potential. The solid-phase displacement tensor and the flow-phase displacement tensor can be expressed by the following formula (10): (10) in, Represents the solid-state displacement tensor. Represents the solid-state displacement tensor scalar potential function Represents the solid-state displacement tensor The vector potential function, Represents the flow phase displacement tensor. Represents the flow phase displacement tensor scalar potential function Represents the flow phase displacement tensor The vector potential function.

[0060] Based on the decomposed solid phase displacement tensor, the decomposed flow phase displacement tensor, and the unconventional oil and gas reservoir wave propagation model, the transverse wave field and the longitudinal wave field can be obtained. Specifically, by substituting the decomposed solid phase displacement tensor and the decomposed flow phase displacement tensor (i.e., formula (10)) into the unconventional oil and gas reservoir wave propagation model (i.e., formula (7)), the wave field can be decomposed into a transverse wave field and a longitudinal wave field.

[0061] Here, the transverse wave field and the longitudinal wave field are in the time-space domain. Based on the fluid viscoelastic parameters, the transverse wave field and the longitudinal wave field are subjected to Fourier transform processing, transforming the transverse wave field and the longitudinal wave field from the time-space domain to the frequency-wavenumber domain, to obtain the Fourier transformed transverse wave field and the Fourier transformed longitudinal wave field.

[0062] Specifically, the transverse wave field after Fourier transform can be represented by the following formula (11): (11) in, Represents the shear modulus of a solid skeleton; Indicates the second characteristic angular frequency; The fractional order of the shear modulus of a solid skeleton; Indicates the second reference quality factor; Represents the imaginary unit; Indicates angular frequency; , Indicates wave number; , , ,in Indicates the density of the solid matrix. Represents the solid-fluid coupling density. Indicates pore fluid density; Denotes the first constant vector. Fourier transform ; Represents the second constant vector. Fourier transform ; Indicates fluid viscosity; Indicates porosity; Indicates the power-law exponent; Indicates penetration rate; Indicates the initial displacement of the fluid; This represents the initial displacement of the solid.

[0063] Specifically, the longitudinal wave field after Fourier transform can be represented by the following formula (12): (12) in, , Indicates wave number; Indicates angular frequency; Indicates the biofluid storage coefficient; This represents the Biot-Williams coefficient; Indicates porosity; , , ,in Indicates the density of the solid matrix. Represents the solid-fluid coupling density. Indicates pore fluid density; Denotes the first constant scalar. Fourier transform ; Denotes the second constant scalar. Fourier transform ; Represents the imaginary unit; Indicates fluid viscosity; Indicates the power-law exponent; Indicates penetration rate; Indicates the initial displacement of the fluid; This represents the initial displacement of the solid.

[0064] It can be expressed by the following formula (13): (13) in, The bulk modulus of a solid skeleton is represented by its mass. Represents the shear modulus of a solid skeleton. Indicates the first characteristic angular frequency. Indicates the second characteristic angular frequency. Indicates the first reference quality factor. Indicates the second reference quality factor. The order of the bulk modulus of a solid framework is indicated by its fractional order. The fractional order of the shear modulus of a solid skeleton is represented by the following: Represents the imaginary unit. It represents angular frequency.

[0065] The transverse wave field and the longitudinal wave field after Fourier transform are respectively subjected to formal transformation to obtain formally transformed transverse wave field and formally transformed longitudinal wave field; the formally transformed transverse wave field and the formally transformed longitudinal wave field are in matrix multiplication form, which is the product of constant variables and coefficient matrices. Specifically, for the formally transformed transverse wave field, the constant variables include a first constant vector and a second constant vector; for the formally transformed longitudinal wave field, the constant variables include a first constant scalar and a second constant scalar.

[0066] When the transverse wave field after Fourier transform has a non-zero solution, both the first and second constant vectors are non-zero. Given that both the first and second constant vectors are non-zero, a first coefficient matrix is ​​extracted from the transformed transverse wave field. When the longitudinal wave field after Fourier transform has a non-zero solution, both the first and second constant scalars are non-zero. Given that both the first and second constant scalars are non-zero, a second coefficient matrix is ​​extracted from the transformed longitudinal wave field.

[0067] When the determinant of the first coefficient matrix is ​​zero, the first nonlinear model can be obtained. The first nonlinear model can be expressed by the following formula (14): (14) in, , Represents angular frequency. , , , , , Represents the imaginary unit. Represents the power-law exponent. Represents the shear modulus of a solid skeleton. Indicates the second characteristic angular frequency. The fractional order of the shear modulus of a solid skeleton is represented by the following: Indicates the second reference quality factor. , , , Indicates the density of the solid matrix. Represents the solid-fluid coupling density. Indicates pore fluid density. Indicates porosity. Indicates fluid viscosity. Indicates the initial displacement of the solid. This indicates the penetration rate.

[0068] In formula (14), The unknown quantity is denoted as , while the other parameters are known quantities. The complex wave number of the shear wave can be obtained by solving the first nonlinear model. .

[0069] When the determinant of the second coefficient matrix is ​​zero, the second nonlinear model can be obtained. The second nonlinear model can be expressed by the following formula (15): (15) in, , , , Represents angular frequency. Represents the imaginary unit. , Represents the power-law exponent. , , , , , Indicates the Biot fluid storage coefficient. Indicates porosity. , , , Indicates the density of the solid matrix. Represents the solid-fluid coupling density. Indicates pore fluid density. This represents the Biot-Williams coefficient. Indicates fluid viscosity. Indicates the initial displacement of the solid. Indicates penetration rate. The bulk modulus of a solid skeleton is represented by its mass. Represents the shear modulus of a solid skeleton. Indicates the first characteristic angular frequency. Indicates the second characteristic angular frequency. Indicates the first reference quality factor. Indicates the second reference quality factor. The order of the bulk modulus of a solid framework is indicated by its fractional order. This indicates the fractional order of the shear modulus of a solid skeleton.

[0070] In formula (15), The unknown quantity is denoted as , while the other parameters are known quantities. The P-wave complex number can be obtained by solving the second nonlinear model. .

[0071] Based on the shear wave complex number, the shear wave velocity and the shear wave inverse quality factor are determined. Specifically, the shear wave velocity and the shear wave inverse quality factor can be determined by the following formula (16): (16) in, Indicates the transverse wave velocity. Indicates the inverse quality factor of transverse waves. This indicates the complex wave number of the transverse wave.

[0072] Based on the P-wave complex wave number, the P-wave velocity and the P-wave inverse quality factor are determined. Specifically, the P-wave velocity and the P-wave inverse quality factor can be determined by the following formula (17): (17) in, Indicates the longitudinal wave velocity. Indicates the longitudinal wave inverse quality factor. This represents the longitudinal wave number.

[0073] In this way, by decomposing the wave displacement field into transverse and longitudinal wave fields, and combining the fluid viscoelastic parameters with Fourier transform to perform frequency domain transformation of the wave field, the wave field is reconstructed into the form of constant variables multiplied by the coefficient matrix. When the constant variables are non-zero, the coefficient matrix is ​​extracted. With the constraint that the determinant of the coefficient matrix is ​​zero, the complex wave number of the transverse and longitudinal waves is solved. The wave velocity and inverse quality factor are further obtained from the complex wave number. The dispersion law and energy attenuation degree of transverse and longitudinal waves under multiphase flow and fluid viscoelastic effects can be accurately obtained, and the wave propagation characteristics in unconventional oil and gas reservoirs can be truly characterized.

[0074] In some possible implementations, the method further includes: Based on the wave propagation property data, at least one parameter included in the fluid viscoelastic parameters is updated to obtain the updated fluid viscoelastic parameters. The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Based on the updated fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0075] Here, the various parameters included in the fluid viscoelastic parameters are directly related to the fluid parameters. At least one of the fluid viscoelastic parameters is dynamically updated based on the wave propagation property data. Plane wave analysis is performed using the updated fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics. Compared with traditional models that use fixed parameters and cannot adapt to different reservoir geological conditions, this method achieves adaptive optimization of the fluid viscoelastic parameters, making the model parameters more consistent with the heterogeneous and viscoelastic variation characteristics of the actual reservoir, and effectively improving the accuracy of determining the wave dispersion attenuation characteristics.

[0076] In some possible implementations, the attribute data includes the high-frequency limiting velocity of the shear wave, the low-frequency limiting velocity of the shear wave, the high-frequency limiting velocity of the longitudinal wave, and the low-frequency limiting velocity of the longitudinal wave. The first reference quality factor and the second reference quality factor are updated using the following steps: Based on the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, and the longitudinal wave low-frequency limiting velocity, the first reference quality factor is updated to obtain the updated first reference quality factor. Based on the shear wave high-frequency limiting velocity and the shear wave low-frequency limiting velocity, the second reference quality factor is updated to obtain the updated second reference quality factor.

[0077] Here, the bulk modulus, shear modulus, Biot-Williams coefficient, Biot fluid storage coefficient, and porosity of the solid skeleton of the unconventional oil and gas reservoir wave propagation model can be determined. Based on the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, the longitudinal wave low-frequency limiting velocity, the bulk modulus, the shear modulus, the Biot-Williams coefficient, the Biot fluid storage coefficient, and the porosity, the first reference quality factor is updated to obtain the updated first reference quality factor.

[0078] Specifically, the updated first reference quality factor and the updated second reference quality factor can be determined by the following formula (18): (18) in, This represents the updated first reference quality factor. This represents the updated second reference quality factor. , , , The bulk modulus of a solid skeleton is represented by its mass. Represents the shear modulus of a solid skeleton. This represents the Biot-Williams coefficient. Indicates the Biot fluid storage coefficient. Indicates porosity. This indicates the high-frequency limiting wave velocity of transverse waves. This indicates the low-frequency limiting wave velocity of transverse waves. This indicates the high-frequency limiting velocity of longitudinal waves. It represents the low-frequency limiting wave velocity of longitudinal waves.

[0079] In this way, by utilizing the inherent propagation characteristics of the frequency-limiting wave velocities of the P-wave and S-wave respectively, the first and second reference quality factors are updated respectively. This makes the updated first and second reference quality factors more consistent with the physical characteristics of wave propagation dispersion, and can effectively adapt to the heterogeneous and viscoelastic differences of unconventional oil and gas reservoir media. Compared with the method of assigning fixed values ​​to the reference quality factors, this method is more consistent with the wave energy attenuation characteristics of the actual formation, thereby improving the accuracy of subsequent wave dispersion attenuation characteristics determination and enhancing the adaptability of the overall wave propagation prediction scheme to unconventional oil and gas reservoirs.

[0080] In some possible implementations, the attribute data includes shear wave dispersion data; The fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton are updated by the following steps: Based on the initial values ​​of the fractional order of the shear modulus of the solid skeleton and the initial value of the second characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted shear wave velocity. Using the predicted shear wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the shear wave dispersion data to obtain at least one of the updated fractional order of the shear modulus of the solid skeleton and the updated second characteristic angular frequency.

[0081] In the above steps, initial values ​​for the fractional order of the shear modulus of the solid skeleton and the second characteristic angular frequency can be set; the displacement field of the wave is decomposed to obtain the transverse wave field; based on the initial values ​​for the fractional order of the shear modulus of the solid skeleton and the second characteristic angular frequency, the transverse wave field is subjected to Fourier transform to obtain the Fourier-transformed transverse wave field; the Fourier-transformed transverse wave field is formally transformed to obtain the formally transformed transverse wave field; when the first constant vector and the second constant vector are non-zero, the first coefficient matrix is ​​extracted from the formally transformed transverse wave field; when the determinant of the first coefficient matrix is ​​zero, the transverse wave complex number is obtained, and the predicted transverse wave velocity is determined based on the transverse wave complex number; the predicted transverse wave velocity is used as the initial point for least squares fitting, and least squares fitting is performed based on the transverse wave dispersion data to obtain at least one of the updated fractional order of the shear modulus of the solid skeleton and the updated second characteristic angular frequency.

[0082] Specifically, the least squares fitting process based on the shear wave dispersion data can be represented by the following formula (19): (19) in, This represents the least squares fit. Indicates the second characteristic angular frequency. The fractional order of the shear modulus of a solid skeleton is represented by the following: Indicates the weighting coefficient. This indicates the predicted shear wave velocity. This represents the shear wave dispersion data. This indicates the amount of shear wave dispersion data.

[0083] In this way, the predicted shear wave velocity, determined based on the initial values ​​of the fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton, is used as the initial point. Then, the parameters are optimized by constraining the inversion using actual shear wave dispersion data. At least one of the parameters, the fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton, is updated. This makes the updated fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton more consistent with the actual formation shear wave dispersion evolution characteristics, thereby improving the accuracy of subsequent wave dispersion attenuation characteristics determination and enhancing the adaptability of the overall wave propagation prediction scheme to unconventional oil and gas reservoirs.

[0084] In some possible implementations, the attribute data includes longitudinal wave dispersion data; The fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton are updated by the following steps: Based on the initial values ​​of the fractional order of the bulk modulus of the solid skeleton and the initial value of the first characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted longitudinal wave velocity. Using the predicted longitudinal wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the longitudinal wave dispersion data to obtain at least one of the updated bulk modulus fractional order and the updated first characteristic angular frequency of the solid skeleton.

[0085] In the above steps, initial values ​​for the fractional order of the bulk modulus of the solid skeleton and the first characteristic angular frequency can be set. The displacement field of the wave is decomposed to obtain the longitudinal wave field. Based on the initial values ​​for the fractional order of the bulk modulus of the solid skeleton and the first characteristic angular frequency, the longitudinal wave field is subjected to Fourier transform to obtain the Fourier-transformed longitudinal wave field.

[0086] Specifically, based on the initial value of the fractional order of the bulk modulus of the solid skeleton, the initial value of the first characteristic angular frequency, the fractional order of the shear modulus of the solid skeleton, and the second characteristic angular frequency, the longitudinal wave field is subjected to Fourier transform processing to obtain the Fourier transformed longitudinal wave field.

[0087] Here, if the updated fractional order of the bulk modulus and the updated second characteristic angular frequency of the solid skeleton are obtained before updating the fractional order of the bulk modulus and the first characteristic angular frequency, the Fourier-transformed longitudinal wave field can be obtained using the updated fractional order of the bulk modulus and the updated second characteristic angular frequency. If the updated fractional order of the bulk modulus and the updated second characteristic angular frequency of the solid skeleton are not obtained before updating the fractional order of the bulk modulus and the first characteristic angular frequency, initial values ​​for the fractional order of the bulk modulus and the second characteristic angular frequency of the solid skeleton can be set. Using these initial values, the longitudinal wave field can be subjected to a Fourier transform to obtain the Fourier-transformed longitudinal wave field.

[0088] The P-wave field after Fourier transform is formally transformed to obtain the formally transformed P-wave field. With both the first and second constant scalars non-zero, a second coefficient matrix is ​​extracted from the formally transformed P-wave field. When the determinant of the second coefficient matrix is ​​zero, the P-wave complex wave number is obtained, and the predicted P-wave velocity is determined based on the P-wave complex wave number. Using the predicted P-wave velocity as the initial point for least-squares fitting, least-squares fitting is performed based on the P-wave dispersion data to obtain at least one of the updated solid skeleton bulk modulus fractional order and the updated first characteristic angular frequency.

[0089] Specifically, the least squares fitting process based on the longitudinal wave dispersion data can be expressed by the following formula (20): (20) in, This represents the least squares fit. Indicates the first characteristic angular frequency. The order of the bulk modulus of a solid framework is indicated by its fractional order. Indicates the weighting coefficient. Indicates the predicted P-wave velocity. This represents the longitudinal wave dispersion data. This indicates the number of P-wave dispersion data points. In practical applications, the number of S-wave dispersion data points is generally the same as the number of P-wave dispersion data points.

[0090] In this way, taking the predicted P-wave velocity determined based on the initial values ​​of the fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton as the initial point, and then using the actual P-wave dispersion data to constrain the inversion optimization parameters, at least one of the parameters of the fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton is updated. This makes the updated fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton more consistent with the actual formation P-wave dispersion evolution characteristics, thereby improving the accuracy of subsequent wave dispersion attenuation characteristics determination and improving the adaptability of the overall wave propagation prediction scheme to unconventional oil and gas reservoirs.

[0091] To more clearly illustrate the wave propagation prediction process under multiphase flow conditions, please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram illustrating a wave propagation prediction process under multiphase flow conditions, as shown in an exemplary embodiment of this application. Figure 2 As shown, the fluid parameters of the multiphase fluid in unconventional oil and gas reservoirs are equivalent to the pore fluid properties under multiphase flow conditions. A model is constructed based on the pore fluid properties and the solid skeleton properties to obtain a unified multi-physics mechanism wave propagation model for unconventional oil and gas reservoirs. The viscoelastic parameters of the unconventional oil and gas reservoir wave propagation model are determined, and updated to obtain updated viscoelastic parameters. Based on the updated viscoelastic parameters, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the wave dispersion attenuation characteristics of unconventional oil and gas reservoirs under multiphase flow conditions. For specific details, please refer to the aforementioned embodiments; they will not be repeated here.

[0092] The wave propagation prediction using the unified multi-physics mechanism unconventional oil and gas reservoir wave propagation model constructed through the embodiments of this disclosure can accurately characterize the wave propagation law under multiphase coexistence conditions in unconventional oil and gas reservoirs compared to other traditional wave propagation models. This enables a fine characterization of seismic wave propagation characteristics under multiphase flow conditions, providing theoretical support for practical applications such as seismic wave propagation simulation, dispersion and attenuation analysis, and reservoir parameter prediction. For example, see [link to relevant documentation]. Figures 3-6 , Figure 3 This is a schematic diagram illustrating a longitudinal wave dispersion characteristic as shown in an exemplary embodiment of this application. Figure 4 This is a schematic diagram illustrating a transverse wave dispersion characteristic as shown in an exemplary embodiment of this application. Figure 5 This is a schematic diagram illustrating another longitudinal wave dispersion characteristic as shown in an exemplary embodiment of this application. Figure 6 This is a schematic diagram illustrating another transverse wave dispersion characteristic as an exemplary embodiment of this application. Figure 3 and Figure 4 The dispersion characteristics are shown when the water saturation is 0.7. Figure 5 and Figure 6 The dispersion characteristics are shown when the water saturation is 0.5. Figures 3-6The horizontal axis represents frequency, which is the angular frequency. Divide by 2π, Figure 3 and Figure 5 The ordinate represents the longitudinal wave velocity. Figure 4 and Figure 6 The vertical axis represents the transverse wave velocity. The solid line represents the wave dispersion characteristics obtained using the unified multi-physics mechanism unconventional oil and gas reservoir wave propagation model constructed according to the embodiments of this application, the dashed line represents the wave dispersion characteristics obtained using the Biot model, and the star-shaped points represent the actual collected data. It can be seen that, compared to the Biot model, the unified multi-physics mechanism unconventional oil and gas reservoir wave propagation model constructed according to the embodiments of this application can better predict the wave dispersion characteristics of unconventional oil and gas reservoirs under different liquid saturation conditions. The obtained longitudinal wave dispersion characteristics and transverse wave dispersion characteristics are more consistent with the real data, and the wave propagation prediction accuracy is higher, demonstrating superior prediction accuracy and engineering applicability compared to traditional models. The unified multi-physics mechanism unconventional oil and gas reservoir wave propagation model of this disclosure can accurately characterize the influence of multiphase fluids on wave propagation characteristics in unconventional oil and gas reservoirs, possessing good physical significance and engineering application value.

[0093] The wave propagation prediction method under multiphase flow conditions provided in this application constructs a unified wave propagation model for unconventional oil and gas reservoirs by equating the fluid parameters of each phase fluid in unconventional oil and gas reservoirs with the pore fluid properties under multiphase flow conditions. This model unifies the non-Darcy flow mechanism, fractional-order viscoelastic mechanism, and multiphase flow coexistence mechanism. The constructed model can more reasonably characterize the changes in fluid compressibility, flow resistance, and inertial characteristics under multiphase flow conditions, improving the physical consistency of the model. Plane wave analysis of this model can obtain the wave dispersion attenuation characteristics of unconventional oil and gas reservoirs under multiphase flow conditions, effectively realizing wave propagation prediction for unconventional oil and gas reservoirs. This method can more accurately characterize the influence of multiphase fluids on wave propagation characteristics in actual unconventional oil and gas reservoirs, improving the accuracy of wave propagation prediction for unconventional oil and gas reservoirs.

[0094] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0095] Corresponding to the aforementioned embodiments of the wave propagation prediction method under multiphase flow conditions, this application also provides embodiments of the wave propagation prediction device under multiphase flow conditions.

[0096] Please see Figure 7 This is a schematic diagram illustrating a wave propagation prediction device under multiphase flow conditions, as shown in an exemplary embodiment of this application. Figure 7As shown in the embodiment of this application, the wave propagation prediction device 700 under multiphase flow conditions includes: The parameter acquisition module 701 is used to acquire the fluid parameters of each phase fluid in an unconventional oil and gas reservoir; the unconventional oil and gas reservoir includes multiphase fluids. The parameter construction module 702 is used to determine the pore fluid physical property parameters under multiphase flow conditions based on the fluid parameters of the multiphase fluid; the pore fluid physical property parameters are used to characterize the fluid behavior in the porous medium. The model building module 703 is used to construct a unified unconventional oil and gas reservoir wave propagation model based on the pore fluid physical properties and solid skeleton physical properties; the solid skeleton physical properties are used to characterize the solid skeleton in the porous medium; the multi-physics mechanisms include non-Darcy flow mechanism, fractional-order viscoelastic mechanism and multiphase flow coexistence mechanism. The feature analysis module 704 is used to perform plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0097] In one optional embodiment, the multiphase fluid includes a gas phase fluid and a liquid phase fluid, the fluid parameters include saturation, density, viscosity and bulk modulus, and the pore fluid physical properties include pore fluid density, fluid viscosity and Biot fluid storage coefficient. The parameter construction module 702 is specifically used for: The pore fluid density is determined based on the saturation and density of the multiphase fluid; The viscosity of the fluid is determined based on the saturation and viscosity of the multiphase fluid; The Biot fluid storage coefficient is determined based on the bulk modulus of the multiphase fluid and the saturation of the liquid phase fluid.

[0098] In one optional implementation, the feature analysis module 704 is specifically used for: Determine the fluid viscoelastic parameters of the unconventional oil and gas reservoir wave propagation model; the fluid viscoelastic parameters are used to characterize the viscoelastic memory effect of pore fluids; Based on the fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0099] In one optional embodiment, the fluid viscoelastic parameters include the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, a first characteristic angular frequency, a second characteristic angular frequency, a first reference quality factor, and a second reference quality factor. The first characteristic angular frequency and the second characteristic angular frequency are determined based on the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, and the relaxation time parameter of the viscoelastic mechanism. The first reference quality factor and the second reference quality factor are determined based on the relaxation time parameter of the viscoelastic mechanism. The feature analysis module 704 is also used for: Based on the wave propagation property data, at least one parameter included in the fluid viscoelastic parameters is updated to obtain the updated fluid viscoelastic parameters. The feature analysis module 704, when used to perform plane wave analysis on the unconventional oil and gas reservoir wave propagation model based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions, is specifically used for: Based on the updated fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

[0100] In one optional implementation, the attribute data includes the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, and the longitudinal wave low-frequency limiting velocity. The feature analysis module 704 is used to update the first reference quality factor and the second reference quality factor through the following steps: Based on the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, and the longitudinal wave low-frequency limiting velocity, the first reference quality factor is updated to obtain the updated first reference quality factor. Based on the shear wave high-frequency limiting velocity and the shear wave low-frequency limiting velocity, the second reference quality factor is updated to obtain the updated second reference quality factor.

[0101] In one optional implementation, the attribute data includes shear wave dispersion data; The feature analysis module 704 is used to update the fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton through the following steps: Based on the initial values ​​of the fractional order of the shear modulus of the solid skeleton and the initial value of the second characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted shear wave velocity. Using the predicted shear wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the shear wave dispersion data to obtain at least one of the updated fractional order of the shear modulus of the solid skeleton and the updated second characteristic angular frequency.

[0102] In one optional implementation, the attribute data includes longitudinal wave dispersion data; The feature analysis module 704 is used to update the fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton through the following steps: Based on the initial values ​​of the fractional order of the bulk modulus of the solid skeleton and the initial value of the first characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted longitudinal wave velocity. Using the predicted longitudinal wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the longitudinal wave dispersion data to obtain at least one of the updated bulk modulus fractional order and the updated first characteristic angular frequency of the solid skeleton.

[0103] In one optional implementation, the wave dispersion attenuation characteristics include shear wave velocity, shear wave inverse quality factor, longitudinal wave velocity, and longitudinal wave inverse quality factor. The feature analysis module 704, when used to perform plane wave analysis on the unconventional oil and gas reservoir wave propagation model based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions, is specifically used for: The displacement field of the wave is decomposed to obtain the transverse wave field and the longitudinal wave field; Based on the fluid viscoelastic parameters, the transverse wave field and the longitudinal wave field are subjected to Fourier transform processing to obtain the Fourier transformed transverse wave field and the Fourier transformed longitudinal wave field. The transverse wave field and the longitudinal wave field after Fourier transform are respectively subjected to formal transformation to obtain the formally transformed transverse wave field and the formally transformed longitudinal wave field; the formally transformed transverse wave field and the formally transformed longitudinal wave field are matrix multiplication forms of constant variables multiplied by coefficient matrices. When the constant variable is non-zero, the first coefficient matrix is ​​extracted from the transformed transverse wave field, and the second coefficient matrix is ​​extracted from the transformed longitudinal wave field. The transverse wave complex number is obtained when the determinant of the first coefficient matrix is ​​zero, and the longitudinal wave complex number is obtained when the determinant of the second coefficient matrix is ​​zero. Based on the shear wave complex number, the shear wave velocity and the shear wave inverse quality factor are determined; based on the longitudinal wave complex number, the longitudinal wave velocity and the longitudinal wave inverse quality factor are determined.

[0104] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0105] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0106] Based on the same technical concept, this application also provides a computer device 800, referring to... Figure 8 The diagram shown is a schematic representation of the structure of a computer device according to an exemplary embodiment of this application, comprising: The processor 810, memory 820, and bus 830 are included. The memory 820 is used to store execution instructions and includes main memory 821 and external memory 822. The main memory 821, also known as internal memory, is used to temporarily store the operation data in the processor 810 and the data exchanged with external memory 822 such as hard disk. The processor 810 exchanges data with external memory 822 through main memory 821.

[0107] In this embodiment, the memory 820 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 810. That is, when the computer device 800 is running, the processor 810 communicates with the memory 820 through the bus 830, or the processor 810 communicates with the memory 820 through other means, so that the processor 810 executes the application code stored in the memory 820, and then executes the steps of the wave propagation prediction method under multiphase flow conditions described in any of the foregoing embodiments.

[0108] The memory 820 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0109] Processor 810 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0110] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 800. In other embodiments of this application, the computer device 800 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0111] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the wave propagation prediction method under multiphase flow conditions described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0112] This disclosure also provides a computer program product, which stores a computer program. When the computer program is run by a processor, it executes the steps of the wave propagation prediction method under multiphase flow conditions provided in any of the above embodiments of this disclosure. For details, please refer to the above method embodiments, which will not be repeated here.

[0113] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium, which can be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0114] Furthermore, embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0115] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by dedicated logic circuitry—such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as dedicated logic circuitry.

[0116] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0117] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0118] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily intended to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0119] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0120] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0121] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A wave propagation prediction method under multiphase flow conditions, characterized in that, The method includes: For each phase of fluid in an unconventional oil and gas reservoir, fluid parameters for each phase of fluid are obtained; the unconventional oil and gas reservoir includes multiphase fluids. Based on the fluid parameters of the multiphase fluid, the pore fluid properties under multiphase flow conditions are determined; the pore fluid properties are used to characterize the fluid behavior in the porous medium. Based on the pore fluid properties and solid skeleton properties, a unified unconventional oil and gas reservoir wave propagation model with multiple physical mechanisms is constructed. The solid skeleton properties are used to characterize the solid skeleton in the porous medium. The multiple physical mechanisms include non-Darcy flow mechanism, fractional-order viscoelastic mechanism, and multiphase flow coexistence mechanism. Plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

2. The method according to claim 1, characterized in that, The multiphase fluid includes gaseous fluid and liquid fluid, the fluid parameters include saturation, density, viscosity and bulk modulus, and the pore fluid properties include pore fluid density, fluid viscosity and Biot fluid storage coefficient. The determination of pore fluid properties under multiphase flow conditions based on the fluid parameters of the multiphase fluid includes: The pore fluid density is determined based on the saturation and density of the multiphase fluid; The viscosity of the fluid is determined based on the saturation and viscosity of the multiphase fluid; The Biot fluid storage coefficient is determined based on the bulk modulus of the multiphase fluid and the saturation of the liquid phase fluid.

3. The method according to claim 1, characterized in that, The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Determine the fluid viscoelastic parameters of the unconventional oil and gas reservoir wave propagation model; the fluid viscoelastic parameters are used to characterize the viscoelastic memory effect of pore fluids; Based on the fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

4. The method according to claim 3, characterized in that, The fluid viscoelastic parameters include the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, a first characteristic angular frequency, a second characteristic angular frequency, a first reference quality factor, and a second reference quality factor. The first characteristic angular frequency and the second characteristic angular frequency are determined based on the fractional order of the bulk modulus of the solid skeleton, the fractional order of the shear modulus of the solid skeleton, and the relaxation time parameter of the viscoelastic mechanism. The first reference quality factor and the second reference quality factor are determined based on the relaxation time parameter of the viscoelastic mechanism. The method further includes: Based on the wave propagation property data, at least one parameter included in the fluid viscoelastic parameters is updated to obtain the updated fluid viscoelastic parameters. The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: Based on the updated fluid viscoelastic parameters, plane wave analysis was performed on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

5. The method according to claim 4, characterized in that, The attribute data includes the high-frequency limiting velocity of shear waves, the low-frequency limiting velocity of shear waves, the high-frequency limiting velocity of longitudinal waves, and the low-frequency limiting velocity of longitudinal waves. The first reference quality factor and the second reference quality factor are updated using the following steps: Based on the shear wave high-frequency limiting velocity, the shear wave low-frequency limiting velocity, the longitudinal wave high-frequency limiting velocity, and the longitudinal wave low-frequency limiting velocity, the first reference quality factor is updated to obtain the updated first reference quality factor. Based on the shear wave high-frequency limiting velocity and the shear wave low-frequency limiting velocity, the second reference quality factor is updated to obtain the updated second reference quality factor.

6. The method according to claim 4, characterized in that, The attribute data includes shear wave dispersion data; The fractional order of the shear modulus and the second characteristic angular frequency of the solid skeleton are updated by the following steps: Based on the initial values ​​of the fractional order of the shear modulus of the solid skeleton and the initial value of the second characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted shear wave velocity. Using the predicted shear wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the shear wave dispersion data to obtain at least one of the updated fractional order of the shear modulus of the solid skeleton and the updated second characteristic angular frequency.

7. The method according to claim 4, characterized in that, The attribute data includes longitudinal wave dispersion data; The fractional order of the bulk modulus and the first characteristic angular frequency of the solid skeleton are updated by the following steps: Based on the initial values ​​of the fractional order of the bulk modulus of the solid skeleton and the initial value of the first characteristic angular frequency, plane wave analysis is performed on the unconventional oil and gas reservoir wave propagation model to determine the predicted longitudinal wave velocity. Using the predicted longitudinal wave velocity as the initial point for least squares fitting, least squares fitting is performed based on the longitudinal wave dispersion data to obtain at least one of the updated bulk modulus fractional order and the updated first characteristic angular frequency of the solid skeleton.

8. The method according to claim 3, characterized in that, The wave dispersion attenuation characteristics include shear wave velocity, shear wave inverse quality factor, longitudinal wave velocity, and longitudinal wave inverse quality factor. The step of performing plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir based on the fluid viscoelastic parameters to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions includes: The displacement field of the wave is decomposed to obtain the transverse wave field and the longitudinal wave field; Based on the fluid viscoelastic parameters, the transverse wave field and the longitudinal wave field are subjected to Fourier transform processing to obtain the Fourier transformed transverse wave field and the Fourier transformed longitudinal wave field. The transverse wave field and the longitudinal wave field after Fourier transform are respectively subjected to formal transformation to obtain the formally transformed transverse wave field and the formally transformed longitudinal wave field; the formally transformed transverse wave field and the formally transformed longitudinal wave field are matrix multiplication forms of constant variables multiplied by coefficient matrices. When the constant variable is non-zero, the first coefficient matrix is ​​extracted from the transformed transverse wave field, and the second coefficient matrix is ​​extracted from the transformed longitudinal wave field. The transverse wave complex number is obtained when the determinant of the first coefficient matrix is ​​zero, and the longitudinal wave complex number is obtained when the determinant of the second coefficient matrix is ​​zero. Based on the shear wave complex number, the shear wave velocity and the shear wave inverse quality factor are determined; based on the longitudinal wave complex number, the longitudinal wave velocity and the longitudinal wave inverse quality factor are determined.

9. A wave propagation prediction device under multiphase flow conditions, characterized in that, The device includes: The parameter acquisition module is used to acquire the fluid parameters of each phase fluid in an unconventional oil and gas reservoir; the unconventional oil and gas reservoir includes multiphase fluids. The parameter construction module is used to determine the pore fluid physical property parameters under multiphase flow conditions based on the fluid parameters of the multiphase fluid; the pore fluid physical property parameters are used to characterize the fluid behavior in the porous medium. The model building module is used to construct a unified unconventional oil and gas reservoir wave propagation model based on the pore fluid properties and solid skeleton properties. The solid skeleton properties are used to characterize the solid skeleton in the porous medium. The multi-physics mechanisms include non-Darcy flow mechanism, fractional-order viscoelastic mechanism, and multiphase flow coexistence mechanism. The feature analysis module is used to perform plane wave analysis on the wave propagation model of the unconventional oil and gas reservoir to determine the wave dispersion attenuation characteristics of the unconventional oil and gas reservoir under multiphase flow conditions.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the wave propagation prediction method under multiphase flow conditions as described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the wave propagation prediction method under multiphase flow conditions as described in any one of claims 1 to 8.

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