Reservoir prediction method and device based on five-dimensional seismic data and electronic equipment

Abstract

The application provides a reservoir prediction method and device based on five-dimensional seismic data and electronic equipment, and the method comprises the following steps: obtaining target five-dimensional seismic data, the target five-dimensional seismic data comprising a plurality of seismic trace data, one seismic trace data corresponding to one incident angle and one azimuth; inverting target azimuthal elastic impedance data from the target five-dimensional seismic data, the target azimuthal elastic impedance data comprising azimuthal elastic impedance data under any incident angle and any azimuth; constructing a target forward and inversion operator between the azimuthal elastic impedance and reservoir model parameters, and constructing an inversion target functional of the reservoir model parameters based on the target forward and inversion operator and the target azimuthal elastic impedance data; performing inversion solving on the inversion target functional to obtain a reservoir parameter prediction result, the reservoir parameter prediction result comprising a fracture density and a fluid factor. The embodiment of the application can improve the accuracy of reservoir prediction.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a reservoir prediction method, device and electronic equipment based on five-dimensional seismic data. Background Technology

[0002] Currently, fracture prediction and / or fluid identification involved in reservoir prediction play a crucial role in areas such as carbon dioxide storage, oil and gas reservoir management, geothermal energy development, and well location optimization. Fracture density and fluid factor are key parameters for directly measuring fracture development and conducting fluid identification. However, related technologies typically indirectly indicate the fracture development degree of HTI (Horizontal Transverse Isotropy) media by predicting anisotropy parameters or fracture weakness parameters. These parameters are affected by factors such as fracture density, fluid modulus, and background elastic properties, leading to significant uncertainty in the predicted fractures. Furthermore, these technologies often use the ratio of fracture weakness parameters as the fluid factor to indicate the fluid in the HTI medium. The prediction results, obtained through indirect calculation, are prone to introducing cumulative errors and neglect the influence of fluids in the background pores. This results in low accuracy in reservoir prediction, such as low accuracy in fracture prediction and / or fluid identification. Therefore, there is currently no satisfactory solution for improving the accuracy of reservoir prediction. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a reservoir prediction method, apparatus, and electronic device based on five-dimensional seismic data to solve the problem of low reservoir prediction accuracy caused by related technologies. That is, embodiments of the present invention can construct an inversion target functional of reservoir model parameters through target azimuth elastic impedance data to invert the reservoir parameter prediction results. In other words, embodiments of the present invention construct a direct prediction method for fracture density and fluid factor based on azimuth elastic impedance inversion, which can directly predict fluid factor and fracture density from target five-dimensional seismic data (also referred to as five-dimensional data), without the need to predict anisotropy parameters or fracture weakness parameters for reservoir prediction, thus reducing the ambiguity and uncertainty of fluid and fracture prediction, thereby effectively improving the accuracy of stratigraphic prediction.

[0004] According to one aspect of the present invention, a reservoir prediction method based on five-dimensional seismic data is provided, the method comprising:

[0005] Acquire target five-dimensional seismic data, which includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace is one of K incident angles, and the azimuth angle corresponding to one seismic trace is one of V azimuth angles. K and V are both integers greater than 1.

[0006] The target azimuth elastic impedance data is inverted from the target five-dimensional seismic data. The target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle.

[0007] Construct a target forward and inverse operation operator between azimuth elastic impedance and reservoir model parameters, and construct an inverse target functional of the reservoir model parameters based on the target forward and inverse operation operator and the target azimuth elastic impedance data;

[0008] The inversion objective functional is inverted and solved to obtain reservoir parameter prediction results, which include fracture density and fluid factor.

[0009] According to another aspect of the present invention, a reservoir prediction device based on five-dimensional seismic data is provided, the device comprising:

[0010] The acquisition unit is used to acquire target five-dimensional seismic data, which includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace is one of K incident angles, and the azimuth angle corresponding to one seismic trace is one of V azimuth angles. K and V are both integers greater than 1.

[0011] The processing unit is used to retrieve the target azimuth elastic impedance data from the target five-dimensional seismic data. The target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle.

[0012] The processing unit is also used to construct a target forward and inverse operation operator between azimuth elastic impedance and reservoir model parameters, and to construct an inverse target functional of the reservoir model parameters based on the target forward and inverse operation operator and the target azimuth elastic impedance data.

[0013] The processing unit is also used to perform inversion solution on the inversion objective functional to obtain reservoir parameter prediction results, which include fracture density and fluid factor.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device including a processor and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the methods mentioned above.

[0015] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods mentioned above is provided.

[0016] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, is used to cause the computer to perform the methods mentioned above.

[0017] This invention can acquire target five-dimensional seismic data, which includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle, with the incident angle corresponding to one of K incident angles and the azimuth angle corresponding to one of V azimuth angles. Target azimuth elastic impedance data is then inverted from the target five-dimensional seismic data. This target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle. Then, a target forward and inverse calculus operator can be constructed between the azimuth elastic impedance and reservoir model parameters. Based on the target forward and inverse calculus operator and the target azimuth elastic impedance data, an inversion target functional for the reservoir model parameters is constructed. Correspondingly, the inversion target functional can be solved to obtain reservoir parameter prediction results, including fracture density and fluid factor. As can be seen, the embodiments of the present invention can construct the inversion target functional of reservoir model parameters through the target azimuth elastic impedance data, so as to invert the reservoir parameter prediction results. That is, the embodiments of the present invention construct a direct prediction method of fracture density and fluid factor based on azimuth elastic impedance inversion, which can realize the direct prediction of fluid factor and fracture density from the target five-dimensional seismic data, without the need to predict anisotropy parameters or fracture weakness parameters for reservoir prediction, thus reducing the ambiguity and uncertainty of fluid and fracture prediction, thereby effectively improving the accuracy of stratigraphic prediction. Attached Figure Description

[0018] Further details, features, and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 A flowchart illustrating a reservoir prediction method based on five-dimensional seismic data according to an exemplary embodiment of the present invention is shown.

[0020] Figure 2 A flowchart illustrating another reservoir prediction method based on five-dimensional seismic data according to an exemplary embodiment of the present invention is shown.

[0021] Figure 3 A schematic diagram of an azimuth seismic gather according to an exemplary embodiment of the present invention is shown;

[0022] Figure 4 A schematic diagram of an orientation elastic impedance inversion result according to an exemplary embodiment of the present invention is shown;

[0023] Figure 5A schematic diagram of reservoir model parameter inversion results according to an exemplary embodiment of the present invention is shown;

[0024] Figure 6 A schematic diagram of a seismic profile according to an exemplary embodiment of the present invention is shown;

[0025] Figure 7 A schematic diagram of a profile of an azimuth elastic impedance inversion result according to an exemplary embodiment of the present invention is shown;

[0026] Figure 8 A schematic diagram of a reservoir model parameter inversion result profile according to an exemplary embodiment of the present invention is shown;

[0027] Figure 9 A schematic diagram of another reservoir model parameter inversion result according to an exemplary embodiment of the present invention is shown;

[0028] Figure 10 A schematic block diagram of a reservoir prediction device based on five-dimensional seismic data according to an exemplary embodiment of the present invention is shown.

[0029] Figure 11 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0030] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0031] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0032] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0033] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0034] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0035] It should be noted that the execution subject of the reservoir prediction method based on five-dimensional seismic data provided in this embodiment of the invention can be one or more electronic devices, and this invention does not limit this. The electronic device can be a terminal (i.e., a client) or a server. Therefore, when the execution subject includes multiple electronic devices, and these multiple electronic devices include at least one terminal and at least one server, the reservoir prediction method based on five-dimensional seismic data provided in this embodiment of the invention can be executed jointly by the terminal and the server. Accordingly, the terminal mentioned herein may include, but is not limited to, smartphones, laptops, desktop computers, intelligent voice interaction devices, etc. The server mentioned herein can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, etc.

[0036] Based on the above description, this invention proposes a reservoir prediction method based on five-dimensional seismic data (also referred to as a reservoir prediction method). This reservoir prediction method based on five-dimensional seismic data can be executed by the aforementioned electronic device (terminal or server); or, this reservoir prediction method based on five-dimensional seismic data can be executed jointly by a terminal and a server. For ease of explanation, the following description will use the execution of this reservoir prediction method based on five-dimensional seismic data by an electronic device as an example; such as Figure 1 As shown, the reservoir prediction method based on five-dimensional seismic data may include the following steps S101-S104:

[0037] S101, Obtain target five-dimensional seismic data. The target five-dimensional seismic data includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace is one of K incident angles, and the azimuth angle corresponding to one seismic trace is one of V azimuth angles. K and V are both integers greater than 1.

[0038] The five-dimensional seismic data may include three spatial dimensions, offset (i.e., incident angle, which can be simply referred to as incident angle), and azimuth (i.e., azimuth angle). Based on this, embodiments of the present invention propose reservoir prediction methods based on five-dimensional seismic data inversion, such as fracture density and fluid factor prediction methods based on five-dimensional seismic data inversion.

[0039] Optionally, the methods for acquiring the target five-dimensional seismic data may include, but are not limited to, the following:

[0040] The first acquisition method: The electronic device stores the target five-dimensional seismic data in its own storage space. In this case, the electronic device can acquire the target five-dimensional seismic data from its own storage space.

[0041] The second method of acquisition: electronic devices can obtain earthquake data download links and use these links to download the target five-dimensional earthquake data, etc.

[0042] S102, retrieve the target azimuth elastic impedance data from the target five-dimensional seismic data. The target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle.

[0043] Among them, any incident angle can be any of the K incident angles, and any azimuth angle can be any of the V azimuth angles; in other words, the target azimuth elastic impedance data includes the azimuth elastic impedance data of each of the K incident angles and each of the V azimuth angles.

[0044] Optionally, the electronic device can extract multiple azimuth wavelets from the target five-dimensional seismic data. The multiple azimuth wavelets include the azimuth wavelets of the k-th incident angle and the v-th azimuth angle, where k∈[1,K] and v∈[1,V]. In other words, azimuth wavelets of different incident angles (or simply angles) and different azimuth angles (or simply azimuths) can be extracted from the target five-dimensional seismic data to obtain the azimuth wavelets of each of the K incident angles and each of the V azimuth angles.

[0045] Then, the electronic device can construct the azimuth elastic impedance forward and inverse calculus operator corresponding to the target azimuth elastic impedance data based on multiple azimuth seismic wavelets (also referred to as seismic wavelets). Specifically, multiple azimuth seismic wavelets can be used to determine the target wavelet matrix, and the sub-target wavelet matrix and difference operator can be used to construct the azimuth elastic impedance forward and inverse calculus operator corresponding to the target azimuth elastic impedance data. Optionally, the target wavelet matrix may include the wavelet matrix of the k-th incident angle and the v-th azimuth angle (i.e., it may include the wavelet matrix of each incident angle in the K incident angles and each azimuth angle in the V azimuth angles). The wavelet matrix of an incident angle and an azimuth angle can be determined by the azimuth seismic wavelet of the corresponding incident angle and the corresponding azimuth angle. For example, a wavelet matrix can be a convolution matrix constructed by an azimuth seismic wavelet, or when an azimuth seismic wavelet is directly represented in matrix form, a wavelet matrix can be an azimuth seismic wavelet, etc. The embodiments of the present invention do not limit this. Optionally, the difference operator can be set according to experience or actual needs, and the embodiments of the present invention do not limit this; optionally, the difference operator can be used to calculate the difference between the parameters of the upper and lower medium models.

[0046] Furthermore, the electronic device can determine the initial azimuth elastic impedance model and, based on the initial azimuth elastic impedance model and the forward and inverse azimuth elastic impedance operators, inversely derive the target azimuth elastic impedance data from the target five-dimensional seismic data. Optionally, the initial azimuth elastic impedance model can be set according to experience or actual needs, or it can be determined based on well logging data, etc.; this embodiment of the invention does not limit this.

[0047] Optionally, when retrieving target azimuth elastic impedance data from target five-dimensional seismic data based on the initial azimuth elastic impedance model and the forward and inverse azimuth elastic impedance operators, the electronic device can construct an elastic seismic relationship function between the target five-dimensional seismic data and the target azimuth elastic impedance data based on the forward and inverse azimuth elastic impedance operators; and can solve the elastic seismic relationship function based on the model-constrained damped least squares inversion method and the initial azimuth elastic impedance model to obtain the target azimuth elastic impedance data, thereby realizing the retrieval of target azimuth elastic impedance data from target five-dimensional seismic data.

[0048] For example, the elastic seismic relationship function between the target five-dimensional seismic data and the target azimuth elastic impedance data can be shown in Equation 1.1:

[0049] Formula 1.1

[0050] Among them, S pp G1 can represent the target five-dimensional seismic data, X can represent the azimuth elastic impedance forward and inverse operators, and X can represent the target azimuth elastic impedance data. In this case, the target azimuth elastic impedance data can be calculated in one step using Formula 1.1, i.e., the azimuth elastic impedance data at each incident angle and each azimuth angle can be inverted in one step. Optionally, assuming a seismic trace includes j sampling points (j is a positive integer), taking the k-th incident angle and v-th azimuth angle as an example, the parameters in Formula 1.1 can be shown in Formula 1.2:

[0051] Equation 1.2

[0052] Wherein, S pp This can represent the seismic trace data for the k-th incident angle and the v-th azimuth angle, s j It can represent the k-th incident angle (i.e., θ) k and the vth azimuth angle (i.e., φ) v The seismic data of the j-th sampling point in the seismic trace data; G1 can be the sub-azimuth elastic impedance forward and inverse operators for the k-th incident angle and the v-th azimuth angle, W can be the wavelet matrix (which can be the wavelet matrix for the k-th incident angle and the v-th azimuth angle), D can be the difference operator (which can only include the difference operator corresponding to the seismic trace data for the k-th incident angle and the v-th azimuth angle); X can be the azimuth elastic impedance data at the k-th incident angle and the v-th azimuth angle, In can represent the logarithmic sign, AEI j This can represent the azimuth elastic impedance at the j-th sampling point. In this case, the azimuth elastic impedance data at each incident angle and each azimuth angle can be sequentially inverted using Equation 1.1, thereby inverting the target azimuth elastic impedance data. In this case, Equation 1.1 can represent the elastic seismic relationship function between the seismic trace data at the k-th incident angle and the v-th azimuth angle and the azimuth elastic impedance data at the k-th incident angle and the v-th azimuth angle in the target five-dimensional seismic data.

[0053] Furthermore, given an initial model X0 of the azimuth elastic impedance, the elastic seismic relationship function can be solved using the damped least squares inversion method based on model constraints, thereby obtaining the target azimuth elastic impedance data; for example, as shown in Equation 1.3, the target azimuth elastic impedance data can be solved as follows:

[0054] Equation 1.3

[0055] Where I can represent the identity matrix and σ can represent the damping coefficient; optionally, the damping coefficient can be set according to experience or actual needs, and the embodiments of the present invention do not limit this.

[0056] S103, construct the target forward and inverse operation operator between azimuth elastic impedance and reservoir model parameters, and construct the inverse target functional of reservoir model parameters based on the target forward and inverse operation operator and target azimuth elastic impedance data.

[0057] S104, the inversion objective functional is solved to obtain the reservoir parameter prediction results, including fracture density and fluid factor.

[0058] Optionally, the aforementioned inversion objective functional is also referred to as the objective functional. The reservoir parameter prediction results may include the prediction results of each reservoir parameter in the reservoir model parameters. The reservoir model parameters may include fracture density and fluid factor; that is, the aforementioned reservoir parameter prediction results including fracture density and fluid factor can mean that the reservoir parameter prediction results include the prediction results of fracture density and the prediction results of fluid factor, etc. Optionally, the reservoir model parameters may also include shear modulus and density, etc.; for example, the reservoir model parameters may include fracture density, fluid factor, shear modulus, and density.

[0059] In this embodiment of the invention, crack density and fluid factor can be predicted simultaneously in a fluid-saturated HTI medium, thereby allowing for the simultaneous determination of crack density and fluid factor.

[0060] This invention can acquire target five-dimensional seismic data, which includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle, with the incident angle corresponding to one of K incident angles and the azimuth angle corresponding to one of V azimuth angles. Target azimuth elastic impedance data is then inverted from the target five-dimensional seismic data. This target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle. Then, a target forward and inverse calculus operator can be constructed between the azimuth elastic impedance and reservoir model parameters. Based on the target forward and inverse calculus operator and the target azimuth elastic impedance data, an inversion target functional for the reservoir model parameters is constructed. Correspondingly, the inversion target functional can be solved to obtain reservoir parameter prediction results, including fracture density and fluid factor. As can be seen, the embodiments of the present invention can construct the inversion target functional of reservoir model parameters through the target azimuth elastic impedance data, so as to invert the reservoir parameter prediction results. That is, the embodiments of the present invention construct a direct prediction method of fracture density and fluid factor based on azimuth elastic impedance inversion, which can realize the direct prediction of fluid factor and fracture density from the target five-dimensional seismic data, without the need to predict anisotropy parameters or fracture weakness parameters for reservoir prediction, thus reducing the ambiguity and uncertainty of fluid and fracture prediction, thereby effectively improving the accuracy of stratigraphic prediction.

[0061] Based on the above description, this embodiment of the invention also proposes a more specific reservoir prediction method based on five-dimensional seismic data. Accordingly, this reservoir prediction method based on five-dimensional seismic data can be executed by the aforementioned electronic device (terminal or server); or, it can be executed jointly by a terminal and a server. For ease of explanation, the following description will use the execution of this reservoir prediction method based on five-dimensional seismic data by an electronic device as an example; please refer to [link to relevant documentation]. Figure 2 The reservoir prediction method based on five-dimensional seismic data may include the following steps S201-S206:

[0062] S201, acquire target five-dimensional seismic data. The target five-dimensional seismic data includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace is one of K incident angles, and the azimuth angle corresponding to one seismic trace is one of V azimuth angles.

[0063] S202, retrieves the target azimuth elastic impedance data from the target's five-dimensional seismic data. The target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle.

[0064] S203, Determine the target azimuth elastic impedance function. The target azimuth elastic impedance function is used to represent the linear relationship between each reservoir parameter and the azimuth elastic impedance in the reservoir model parameters.

[0065] Optionally, when determining the target azimuth elastic impedance function, the electronic device can construct an initial azimuth seismic reflection coefficient function, which includes the normal weakness parameter and tangential weakness parameter of the dry crack. Under the assumption of weak anisotropy, the expression functions of the normal weakness parameter and the tangential weakness parameter can be constructed using crack density, respectively. The expression functions of the normal weakness parameter and the tangential weakness parameter are then substituted into the initial azimuth seismic reflection coefficient function to obtain the target azimuth seismic reflection coefficient function, which includes crack density and fluid factor. Furthermore, based on the target azimuth seismic reflection coefficient function, an initial azimuth elastic impedance function can be constructed, and the initial azimuth elastic impedance function can be linearly expressed to obtain the target azimuth elastic impedance function.

[0066] In this embodiment of the invention, the derivation of the azimuth elastic impedance equation (i.e., function) directly characterized by fracture density and fluid factor is presented. Specifically, this embodiment of the invention can reconstruct the azimuth seismic reflection coefficient equation of PP waves in fluid-saturated HTI media based on the rock-physical relationship of fracture parameters and the weak anisotropy approximation, rewriting it as a form directly characterized by fluid factor, fracture density, shear modulus, and density parameter. Furthermore, the azimuth elastic impedance equation directly characterized by only the above four parameters is derived.

[0067] Specifically, for HTI media saturated with fluid, when the equivalent fluid bulk modulus in the pores and fractures is much smaller than the bulk modulus of the rock matrix, the initial azimuth seismic reflection coefficient function used for fracture detection and fluid identification can be expressed as shown in Equation 2.1:

[0068] Equation 2.1

[0069] Among them, R pp The azimuth reflection coefficient can be represented by θ, the incident angle by φ, and the azimuth angle by g. s =μ / M sat g d =μ / M dry μ can be the shear modulus of the rock, M sat and M dry The longitudinal wave modulus (g) of dry rock and fluid-saturated rock can be respectively defined as follows: d and g s It can be calculated from well logging data, or it can be set according to experience or actual needs; this embodiment of the invention does not limit this. It should be noted that it is only used here to indicate g. d and gs The definition of g is not calculated by the right side of the equation. d and g s For example, when calculating via the right side of the equation, u and M sat It can be calculated based on the P-wave and S-wave velocities in actual well logging data, M dry (This can be calculated using rock physics, etc.), where ρ can be the density of the rock, δ... N and δ T These can be the normal and tangential weakness parameters of the dry crack, respectively; f can be the fluid factor, parameter f / f, μ / μ and The denominator of ρ / ρ can represent the average value of the parameters of the upper and lower medium models, such as f representing the average value of the fluid factor of the upper and lower mediums, and so on; correspondingly, the symbols... This can represent the difference in model parameters between the upper and lower media layers; the aforementioned reservoir parameters can also be called model parameters. It can be seen that this reflection coefficient equation contains five unknown parameters (i.e., normal weakness parameter, tangential weakness parameter, shear modulus, density, and fluid factor), leading to significant uncertainties when using this equation for seismic inversion. Based on this, the electronic device uses Equation 2.1 to construct the initial azimuth seismic reflection coefficient function.

[0070] For example, the fluid factor can be expressed as porosity Φ (which can be determined through well logging data or based on experience, etc.) and equivalent fluid bulk modulus K. f (Can be calculated through well logging or set based on experience, etc.), critical porosity Φ0 (can be set according to experience or actual needs, etc.), normal weakness δ of dry fractures. N The function is shown in Formula 2.2:

[0071] Equation 2.2

[0072] It should be noted that Formula 2.2 only exemplifies the meaning (i.e., definition) of the fluid factor and does not limit the prediction process of the fluid factor.

[0073] Furthermore, under the assumption of weak anisotropy, the normal and tangential weakness parameters of dry cracks can be expressed as functions of crack density, as shown in Equation 2.3:

[0074] Equation 2.3

[0075] Where e can represent crack density, g b dry It can represent the ratio of shear modulus to compressive modulus of isotropic background matrix rocks; optionally, g b dryThe setting can be based on experience or actual needs, and this embodiment of the invention does not limit this. Optionally, the crack density in this embodiment of the invention can be defined as the volume density of the crack, i.e., e=ζr 3 ζ can represent the number of fractures per unit volume (e.g., determined by imaging logging), and r can represent the average radius of the fractures (e.g., obtained from core observation data); optionally, the volume density e of the fractures can also be related to the fracture porosity Φ. f (As can be obtained from well logging interpretation) Establish a mapping relationship, i.e., e = 3Φ f / (4πξ), where ξ can represent the crack aspect ratio (which can be set empirically, etc.); it should be understood that the expression for e here defines the crack density, but does not limit the crack density prediction process. Therefore, electronic devices can use formula 2.3 to construct the normal weakness parameter (δ). N The representation function of ) and the tangential weakness parameter (δ) T The representation function of ).

[0076] Based on this, the electronic device can substitute Equation 2.3 into Equation 2.1 to obtain the reflection coefficient equation of the fluid-saturated HTI medium directly characterized by crack density and fluid factor (i.e., the target azimuth seismic reflection coefficient function). This allows the substitution of the representation functions of the normal and tangential weakness parameters into the initial azimuth seismic reflection coefficient function to obtain the target azimuth seismic reflection coefficient function. Therefore, the corresponding target azimuth seismic reflection coefficient function can be expressed as shown in Equation 2.4:

[0077] Equation 2.4

[0078] Wherein, the fluid factor coefficient a f (θ), shear modulus a μ (θ), density coefficient a ρ (θ) and gap density coefficient a e (θ,φ) can be represented as shown in Equation 2.5:

[0079] Equation 2.5

[0080] Therefore, the fluid factor can be expressed as shown in Equation 2.6:

[0081] Equation 2.6

[0082] As can be seen, compared with the initial azimuth seismic reflection coefficient function, the target azimuth seismic reflection coefficient function is a function directly characterized by fluid factor, shear modulus, density, and fracture density, and can simultaneously achieve direct prediction of fluid factor and fracture density. Furthermore, the target azimuth seismic reflection coefficient function contains only four model parameters, greatly reducing the uncertainty of multi-parameter inversion in HTI media. It should be understood that the reflection coefficient reflects the interface information of the strata, while the elastic impedance reflects the layer properties, weakening the influence of wavelet and tuning effects. Compared with AVAZ inversion (Amplitude Variation with Angle and Azimuth), elastic impedance inversion has stronger noise resistance. The linear relationship between elastic impedance and reflection coefficient (i.e., azimuth seismic reflection coefficient) can be expressed as shown in Equation 2.7:

[0083] Equation 2.7

[0084] Where AEI can represent azimuth elastic impedance (or simply elastic impedance). It should be understood that when the elastic parameters on both sides of the reflecting interface are not significantly different, ( x) / x≈ In(x); then, combining Formula 2.4 (i.e., the target azimuth seismic reflection coefficient function) and Formula 2.7, after mathematical integration and simplification, the normalized azimuth elastic impedance equation (i.e., the initial equation elastic impedance function) directly characterized by the fluid factor and crack density can be derived. In other words, electronic equipment can use the target azimuth seismic reflection coefficient function and the linear relationship between elastic impedance and reflection coefficient to construct the initial equation elastic impedance function; based on this, the initial equation elastic impedance function can be shown in Formula 2.8:

[0085] Equation 2.8

[0086] In Formula 2.8, the subscript 0 can represent a reference value for the background medium model parameters, such as f0 representing a reference value for the fluid factor, etc. Optionally, the reference values ​​for each reservoir parameter can be obtained by averaging the logging curves of the target section (i.e., averaging the actual values ​​on the wellbore), or they can be set according to experience or actual needs. This embodiment of the invention does not limit this. EI0 can represent a constant normalized to the elastic impedance. Optionally, EI0 can be calculated by averaging the elastic impedance; or, EI0 can also be calculated based on the reference values ​​of the fluid factor, shear modulus, and density, as shown in Formula 2.9.

[0087] Equation 2.9

[0088] Furthermore, the electronic device can linearly represent the initial azimuth elastic impedance function to obtain the target azimuth elastic impedance function; specifically, the logarithm of both sides of the initial azimuth elastic impedance function can be taken simultaneously to achieve a linear representation, and the target azimuth elastic impedance function can then be represented as shown in Equation 2.10:

[0089] Equation 2.10

[0090] S204. Based on the target azimuth elastic impedance data, the target azimuth elastic impedance function is converted into a matrix form to obtain the matrix form azimuth elastic impedance function. The matrix form azimuth elastic impedance function includes the target forward and inverse operators between the azimuth elastic impedance and the reservoir model parameters, so as to realize the construction of the target forward and inverse operators between the azimuth elastic impedance and the reservoir model parameters.

[0091] The aforementioned target forward and inverse operators can also be referred to as forward and inverse operators of reservoir model parameters.

[0092] In this embodiment of the invention, the electronic device can represent Formula 2.10 in matrix form and substitute the target azimuth elastic impedance data into the matrix form to obtain the matrix form azimuth elastic impedance function; for example, the matrix form azimuth elastic impedance function can be as shown in Formula 2.11:

[0093] Equation 2.11

[0094] Where d represents the normalized logarithmic domain azimuth elastic impedance data (i.e., logarithmic domain azimuth elastic impedance data), m represents the reservoir model parameters, and G represents the target forward and inverse modeling operators. Furthermore, the parameters in Equation 2.11 can be represented as shown in Equation 2.12:

[0095] Equation 2.12

[0096] Where the superscript T denotes the transpose of the matrix, diag denotes the logarithmic matrix, and t j This can represent the j-th sampling time (i.e., the time of the j-th sampling point). Based on this, the target forward and inverse operators can be determined from the matrix form of the azimuth elastic impedance function, thereby realizing the construction of the target forward and inverse operators.

[0097] S205, based on the target forward and inverse inversion operators and the target azimuth elastic impedance data, constructs the inversion target functional of the reservoir model parameters.

[0098] Optionally, embodiments of the present invention may use Bayesian theory to estimate the probability distribution of reservoir model parameters. In Bayesian inference, the posterior probability distribution function of the model parameters is proportional to the prior probability distribution function and the likelihood function. Based on this, assuming that the prior probability of the reservoir model parameters follows a Cauchy distribution and the noise record follows a Gaussian distribution, the Gaussian distribution can be used as the likelihood function.

[0099] Accordingly, the electronic equipment can determine the posterior probability distribution function of the reservoir model parameters based on the target forward and inverse operation operators and the target azimuth elastic impedance data; for example, this posterior probability distribution function can be shown in Equation 2.13:

[0100] Equation 2.13

[0101] Where, σ m 2 and σ n 2 The variances of noise and reservoir model parameters can be represented separately (including the variances of each reservoir parameter, represented in vector form); optionally, the noise variance and the reservoir model parameter variance can be set according to experience or actual needs; or, they can be determined based on seismic data or well logging data; or, the noise variance can be determined based on d-Gm, that is, the noise variance can be used to indicate the difference between d and Gm, etc.; the embodiments of the present invention do not limit this.

[0102] Furthermore, the electronic device can solve for the maximum a posteriori probability distribution of the reservoir model parameters through the posterior probability distribution function to determine the undetermined inversion objective functional of the reservoir model parameters; that is, by solving for the maximum a posteriori probability distribution of the reservoir model parameters, the expression of the undetermined inversion objective functional can be obtained as shown in Equation 2.14:

[0103] Equation 2.14

[0104] Based on this, electronic devices can construct the inversion objective functional of reservoir model parameters based on the undetermined inversion objective functional.

[0105] In one implementation, the electronic device can use the undetermined inversion objective functional as the inversion objective functional of the reservoir model parameters.

[0106] In another implementation, to improve the stability and lateral continuity of the inversion results, the electronic device can also introduce low-frequency constraint terms of reservoir model parameters into the target functional to be inverted, thereby obtaining the target functional. In this case, the target functional also includes low-frequency constraint terms of reservoir model parameters, which can be constructed using the reservoir model parameters and the corresponding initial reservoir model parameters. For example, the low-frequency constraint terms can be constructed using the reservoir model parameters (i.e., the initial reservoir model parameters) and the seismic interpretation horizons (i.e., the reservoir model parameters) in the well logging data. For example, the target functional in this case can be as shown in Equation 2.15:

[0107] Equation 2.15

[0108] Where, x i and x i ’ The i-th element can represent the reservoir model parameters and the reservoir initial model parameters (also called the reservoir initial model, which may include the initial model of each reservoir parameter), respectively. In other words, the i-th element can represent the inversion column vector (i.e., the inversion result, such as including the parameter values ​​at each sampling time) of the i-th reservoir parameter and the initial model of the i-th reservoir parameter (such as including the initial model values ​​at each sampling time). This can represent the weighting coefficients of the reservoir model parameters (i.e., the weighting coefficient of the i-th element). Optionally, the initial reservoir model parameters can be set according to experience or actual needs, or determined through well logging data, or obtained through actual values ​​on the well and seismic horizon constraints, etc.; this embodiment of the invention does not limit this. Optionally, the weighting coefficients of the reservoir model parameters can be set according to experience or actual needs, or determined through multiple experiments, etc.; this embodiment of the invention does not limit this.

[0109] S206, the inversion objective functional is inverted and solved to obtain the reservoir parameter prediction results, which include fracture density and fluid factor.

[0110] Optionally, the electronic device can solve the aforementioned inversion objective functional using an iterative reweighted least squares inversion algorithm to obtain the reservoir parameter prediction results. Based on this, embodiments of the present invention can use a Bayesian inference-based azimuthal elastic inversion method to predict fracture density and fluid factor; that is, embodiments of the present invention can construct an inversion objective functional of reservoir model parameters under Bayesian inference, and solve this inversion objective functional using an iterative reweighted least squares inversion algorithm, thereby achieving simultaneous prediction of fracture density and fluid factor.

[0111] Therefore, when it is necessary to predict fracture density and fluid factor, the predicted results of fracture density (i.e., inversion results) and fluid factor can be determined from the reservoir parameter prediction results, thus obtaining the inversion results of fracture density and fluid factor respectively. Optionally, reservoir model parameters may also include shear modulus, density, etc.

[0112] In this embodiment of the invention, in order to further verify the feasibility and effectiveness of the reservoir prediction method based on five-dimensional seismic data mentioned in this embodiment of the invention, the proposed reservoir prediction method based on five-dimensional seismic data was tested by synthetic testing and actual data of a fractured oil and gas reservoir in eastern my country.

[0113] On one hand, this embodiment of the invention selects time-domain logging data of a fractured reservoir in HTI medium for synthetic testing. First, it uses Formula 2.4 and a Ricker wavelet with a dominant frequency of 30 Hz to synthesize azimuth seismic gathers (i.e., the azimuth seismic reflection coefficient is calculated from the logging data, and the azimuth seismic reflection coefficient and Ricker wavelet are used to synthesize the seismic gathers). The incident angles of the seismic gathers are 10°, 20°, and 30°, and the azimuth angles are 0° and 90°. Then, random Gaussian noise with a signal-to-noise ratio of 10:1 is added to the seismic gathers to obtain the target five-dimensional seismic data (which may include the final synthesized azimuth seismic gathers, where one seismic trace is one seismic trace data). Figure 3 As shown; then, the target azimuth elastic impedance data are inverted using Formula 1.3. Taking an azimuth angle of 0° as an example, the inverted azimuth elastic impedance data at each incident angle when the azimuth angle is 0° (i.e., the azimuth elastic impedance inversion results) can be illustrated as follows. Figure 4 As shown, Figure 4 The horizontal axis in the equation represents azimuth elastic impedance, with units of kilograms per square meter per second. Finally, the target forward and inverse operators for reservoir model parameters can be constructed using Equation 2.11, and the fluid factor, shear modulus, density, and fracture density can be simultaneously inverted using a Bayesian inference-based azimuth elastic impedance inversion method. The reservoir model parameter inversion results (i.e., reservoir parameter prediction results) can be obtained as follows: Figure 5 As shown, Figure 5 The solid lines, dashed lines, and dotted lines in the diagram represent the true value, the initial model, and the inversion result, respectively. Figure 5 The subplot representing the fluid factor has its x-axis set to the fluid factor (in gigapascals); the subplot representing the shear modulus has its x-axis set to the shear modulus (in gigapascals); the subplot representing the density has its x-axis set to the density (in kilograms per cubic meter); and the subplot representing the crack density has its x-axis set to the crack density (unitless). Based on this, from... Figure 5 As can be seen, the inversion results are in good agreement with the actual values, verifying the feasibility and effectiveness of the reservoir prediction method based on five-dimensional seismic data proposed in this embodiment of the invention.

[0114] On the other hand, geological and well logging data indicate that the target layer in a work area is a lacustrine turbidite fan deposit, with lithology mainly consisting of sandstone, mudstone, and shale. The target layer exhibits both porosity and near-vertical fractures, with fractures sparsely distributed in the sandstone and mudstone but widely developed in the shale, which can be equivalent to a fluid-saturated HTI medium. For example, Figure 6 This section displays seismic profiles (also known as azimuth seismic data profiles) corresponding to different incident angles (11°, 22°, and 33°) at azimuth angles of 0° and 90°. Figure 6 The black dashed lines (i.e., dashed lines intersecting the ground) represent well trajectories, and the colored bars (i.e., color scales) represent seismic amplitudes (unitless, i.e., dimensionless). Therefore, reservoirs with a time window below 3000 milliseconds are sandstone / mudstone reservoirs, exhibiting weak amplitude responses; reservoirs with a time window above 3000 milliseconds are shale reservoirs, exhibiting strong amplitude responses. Furthermore... Figure 7 The diagram shows the azimuth elastic impedance inversion results (i.e., target azimuth elastic impedance data) profile. It reveals differences in azimuth elastic impedance data at different incident angles and azimuths, providing a good data foundation for fracture prediction and fluid identification (i.e., reservoir prediction). Figure 7 The colored bars represent directional elastic impedance, measured in kilograms per square meter per second. It should be noted that... Figure 6 and Figure 7 The horizontal and vertical coordinates of all sub-graphs are the same, so some identical coordinate labels have been omitted for adaptability. The trace number can also be called the line trace number, which is the number of the seismic trace.

[0115] Therefore, accordingly Figure 8 This section describes the inversion results of fluid factors, shear modulus, density, and fracture density using the reservoir prediction method based on five-dimensional seismic data proposed in this invention, i.e., the reservoir model parameter inversion profile. Well logging interpreters, considering porosity, fractures, and fluid interpretation, classify the target area's oil and gas reservoirs into Class I reservoirs (effective porosity higher than 7%). Figure 8 In (a) (i.e., the inversion result of the fluid factor; the color bars represent the fluid factor, in gigapascals), the rectangular areas on the well trajectory (i.e., the horizontal black areas on the well trajectory), Class II reservoirs (i.e., the horizontal light black areas on the well trajectory), and non-reservoir (i.e., reservoirs without rectangular areas) are shown. Figure 8 As can be seen, the fluid factor values ​​are very low in both Class I and Class II reservoirs, which is consistent with well logging interpretation of oil and gas reservoirs. Furthermore, along the time axis, the shear modulus (i.e., Figure 8 As shown in (b) of the figure, the colored bars in this subgraph represent the shear modulus (in gigapascals) and density (i.e., Figure 8As shown in (c) of the figure, the colored bars in this subfigure represent density (unit: kg per cubic meter). The inversion results show that the sandstone and mudstone sections exhibit greater variation, while the shale sections show less variation (geologically consistent). This is because the elastic parameter variations are more pronounced in sandstone and mudstone reservoirs, while the differences in elastic parameters are smaller in shale reservoirs. Laterally, both the shear modulus and density inversion results show a gradual change along the layers, consistent with the lacustrine turbidite fan depositional environment of the reservoir. Furthermore, as... Figure 8 As shown in section (d) (i.e., the inversion result of fracture density; the color bars in this subplot represent fracture density, which is dimensionless), the estimated fracture density values ​​in the sandstone and mudstone sections are lower, indicating poorer lateral continuity, while the fracture density values ​​in the shale sections are higher, indicating relatively better lateral continuity. This is consistent with prior geological and imaging logging information for the working area, confirming the reasonableness of the inversion results. It should be noted that... Figure 8 Since all subplots have the same horizontal and vertical coordinates, some identical coordinate labels have been omitted for adaptability.

[0116] To further verify the inversion results, the predicted reservoir parameters (i.e., the inversion results of each reservoir parameter) were compared with the high-shear filtered logging data at the well location. Figure 9 As shown, Figure 9 The x-coordinates of each subgraph are respectively with Figure 5 The parameters and units indicated by the horizontal coordinates of the corresponding subgraphs are the same, and will not be repeated here in the embodiments of the present invention. Figure 9 The solid lines and dotted lines in the diagram represent the true values ​​and the inversion results, respectively. It can be observed that the two match well, which confirms the feasibility and effectiveness of the proposed reservoir prediction method based on five-dimensional seismic data.

[0117] This invention, after acquiring target 5D seismic data, can inversely derive target azimuth elastic impedance data from the target 5D seismic data. The target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle. Furthermore, it can determine the target azimuth elastic impedance function, which represents the linear relationship between each reservoir parameter and the azimuth elastic impedance in the reservoir model parameters. Based on this, the target azimuth elastic impedance function can be converted into a matrix form based on the target azimuth elastic impedance data, resulting in a matrix-form azimuth elastic impedance function. This matrix-form azimuth elastic impedance function includes target forward and inverse operators between the azimuth elastic impedance and reservoir model parameters, thus realizing the construction of target forward and inverse operators between the azimuth elastic impedance and reservoir model parameters. Further, based on the target forward and inverse operators and the target azimuth elastic impedance data, an inversion target functional for the reservoir model parameters can be constructed; and the inversion target functional can be solved to obtain reservoir parameter prediction results, including fracture density and fluid factor. As can be seen, the forward modeling equations (i.e., azimuth seismic reflection coefficient equations and azimuth elastic impedance equations) derived in this embodiment of the invention, which directly characterize fluid factor and fracture density parameters, separate the influence of fluid and fracture. In other words, this embodiment proposes a calculation method for forward modeling equations of fluid-saturated HTI media directly characterized by fluid factor and fracture density parameters. This method is applicable to HTI media with both porosity and fractures. Furthermore, the equations derived in this embodiment can contain only four model parameters, with the fluid factor and fracture density indicating the fluid and fractures in the reservoir, respectively. Based on this, this embodiment can invert fracture density and fluid factor through azimuth elastic impedance, without being affected by background fluid in the pores, effectively improving the accuracy of reservoir prediction and obtaining highly accurate reservoir parameter prediction results. This effectively improves the accuracy of fracture prediction and fluid identification. Moreover, this embodiment can simultaneously invert fracture density and fluid factor, effectively simplifying the calculation.

[0118] Based on the description of the relevant embodiments of the reservoir prediction method based on five-dimensional seismic data above, this invention also proposes a reservoir prediction device based on five-dimensional seismic data. This device can be a computer program (including program code) running on an electronic device; such as... Figure 10 As shown, the reservoir prediction device based on five-dimensional seismic data may include an acquisition unit 1001 and a processing unit 1002. This reservoir prediction device based on five-dimensional seismic data can perform... Figure 1 or Figure 2 The reservoir prediction method based on five-dimensional seismic data shown, i.e., the reservoir prediction device based on five-dimensional seismic data, can operate the above-mentioned unit:

[0119] The acquisition unit 1001 is used to acquire target five-dimensional seismic data, which includes multiple seismic trace data. Each seismic trace data corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace data is one of K incident angles, and the azimuth angle corresponding to one seismic trace data is one of V azimuth angles. K and V are both integers greater than 1.

[0120] Processing unit 1002 is used to retrieve target azimuth elastic impedance data from the target five-dimensional seismic data, wherein the target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle.

[0121] The processing unit 1002 is also used to construct a target forward and inverse operation operator between azimuth elastic impedance and reservoir model parameters, and to construct an inverse target functional of the reservoir model parameters based on the target forward and inverse operation operator and the target azimuth elastic impedance data.

[0122] The processing unit 1002 is also used to perform inversion solution on the inversion objective functional to obtain reservoir parameter prediction results, which include fracture density and fluid factor.

[0123] In one embodiment, when processing unit 1002 retrieves target azimuth elastic impedance data from the target five-dimensional seismic data, it may specifically be used to:

[0124] Multiple azimuth seismic wavelets are extracted from the target five-dimensional seismic data. The multiple azimuth seismic wavelets include the azimuth seismic wavelets of the k-th incident angle and the v-th azimuth angle, where k∈[1,K] and v∈[1,V].

[0125] Based on the multiple azimuth seismic wavelets, construct the azimuth elastic impedance forward and inverse operators corresponding to the target azimuth elastic impedance data;

[0126] An initial model of azimuth elastic impedance is determined, and based on the initial model and the forward and inverse azimuth elastic impedance operators, the target azimuth elastic impedance data is inverted from the target five-dimensional seismic data.

[0127] In another embodiment, when processing unit 1002 inverts the target azimuth elastic impedance data from the target five-dimensional seismic data based on the initial azimuth elastic impedance model and the forward and inverse azimuth elastic impedance operators, it can specifically be used for:

[0128] Based on the azimuth elastic impedance forward and inverse operation, an elastic seismic relationship function between the target five-dimensional seismic data and the target azimuth elastic impedance data is constructed.

[0129] Based on the model-constrained damped least squares inversion method and the initial model of the azimuth elastic impedance, the elastic seismic relationship function is solved to obtain the target azimuth elastic impedance data, so as to realize the inversion of the target azimuth elastic impedance data from the target five-dimensional seismic data.

[0130] In another embodiment, when constructing the target forward and inverse transformation operator between azimuth elastic impedance and reservoir model parameters, the processing unit 1002 can specifically be used for:

[0131] Determine the target azimuth elastic impedance function, which is used to represent the linear relationship between each reservoir parameter in the reservoir model parameters and the azimuth elastic impedance;

[0132] Based on the target azimuth elastic impedance data, the target azimuth elastic impedance function is converted into a matrix form to obtain a matrix form azimuth elastic impedance function. The matrix form azimuth elastic impedance function includes a target forward and inverse operation operator between the azimuth elastic impedance and the reservoir model parameters, so as to realize the construction of the target forward and inverse operation operator between the azimuth elastic impedance and the reservoir model parameters.

[0133] In another embodiment, when determining the elastic impedance function of the target orientation, the processing unit 1002 may specifically be used for:

[0134] An initial azimuth seismic reflection coefficient function is constructed, which includes the normal weakness parameter and the tangential weakness parameter of the dry crack;

[0135] Under the assumption of weak anisotropy, the expression functions of the normal weakness parameter and the tangential weakness parameter are constructed using the crack density, respectively.

[0136] Substituting the representation functions of the normal weakness parameter and the tangential weakness parameter into the initial azimuth seismic reflection coefficient function, the target azimuth seismic reflection coefficient function is obtained, wherein the target azimuth seismic reflection coefficient function includes the fracture density and the fluid factor.

[0137] Based on the target azimuth seismic reflection coefficient function, an initial azimuth elastic impedance function is constructed; and the target azimuth elastic impedance function is obtained by linearly representing the initial azimuth elastic impedance function.

[0138] In another embodiment, when the processing unit 1002 constructs the inversion target functional of the reservoir model parameters based on the target forward and inverse inversion operators and the target azimuth elastic impedance data, it can specifically be used for:

[0139] Based on the target forward and inverse operation operators and the target azimuth elastic impedance data, the posterior probability distribution function of the reservoir model parameters is determined;

[0140] The maximum a posteriori probability distribution of the reservoir model parameters is solved by using the posterior probability distribution function to determine the undetermined inversion objective functional of the reservoir model parameters.

[0141] Based on the undetermined inversion objective functional, the inversion objective functional of the reservoir model parameters is constructed.

[0142] In another embodiment, the inverted objective functional further includes a low-frequency constraint term for the reservoir model parameters, which is constructed using the reservoir model parameters and the corresponding initial reservoir model parameters.

[0143] According to one embodiment of the present invention, Figure 10 Each unit in the reservoir prediction device based on five-dimensional seismic data shown can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effect of the embodiments of the present invention. The above units are based on logical function division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, any reservoir prediction device based on five-dimensional seismic data may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0144] According to another embodiment of the present invention, it is possible to perform operations such as those described above by running on a general-purpose electronic device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). Figure 1 or Figure 2 The computer program (including program code) involved in each step of the corresponding method shown, to construct such... Figure 10 The diagram illustrates a reservoir prediction device based on five-dimensional seismic data, and a reservoir prediction method based on five-dimensional seismic data for implementing embodiments of the present invention. The computer program can be stored on, for example, a computer storage medium, loaded onto the aforementioned electronic device via the computer storage medium, and run therein.

[0145] Based on the description of the method and apparatus embodiments above, an exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method according to an embodiment of the present invention.

[0146] An exemplary embodiment of the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0147] An exemplary embodiment of the present invention also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of the present invention.

[0148] refer to Figure 11 The present invention will now be described in the form of a structural block diagram of an electronic device 1100 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0149] like Figure 11 As shown, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded into a random access memory (RAM) 1103 from a storage unit 1108. The RAM 1103 may also store various programs and data required for the operation of the electronic device 1100. The computing unit 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0150] Multiple components in electronic device 1100 are connected to I / O interface 1105, including: input unit 1106, output unit 1107, storage unit 1108, and communication unit 1109. Input unit 1106 can be any type of device capable of inputting information to electronic device 1100. Input unit 1106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 1107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1108 may include, but is not limited to, disk and optical disk. Communication unit 1109 allows electronic device 1100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0151] The computing unit 1101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1101 performs the various methods and processes described above. For example, in some embodiments, the reservoir prediction method based on five-dimensional seismic data can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1100 via ROM 1102 and / or communication unit 1109. In some embodiments, the computing unit 1101 can be configured by any other suitable means (e.g., by means of firmware) to perform the reservoir prediction method based on five-dimensional seismic data.

[0152] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0153] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0154] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0156] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0157] Furthermore, it should be understood that the above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method of reservoir prediction based on five-dimensional seismic data, characterized in that, include: Acquire target five-dimensional seismic data, which includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace is one of K incident angles, and the azimuth angle corresponding to one seismic trace is one of V azimuth angles. K and V are both integers greater than 1. The process of retrieving target azimuth elastic impedance data from the target five-dimensional seismic data includes: extracting multiple azimuth seismic wavelets from the target five-dimensional seismic data, wherein the multiple azimuth seismic wavelets include azimuth seismic wavelets at the k-th incident angle and the v-th azimuth angle, k∈[1,K], v∈[1,V]; constructing azimuth elastic impedance forward and inverse calculus operators corresponding to the target azimuth elastic impedance data based on the multiple azimuth seismic wavelets; determining an initial azimuth elastic impedance model, and retrieving the target azimuth elastic impedance data from the target five-dimensional seismic data based on the initial azimuth elastic impedance model and the azimuth elastic impedance forward and inverse calculus operators, wherein the target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle; Constructing a target forward and inverse calculus operator between azimuth elastic impedance and reservoir model parameters includes: determining a target azimuth elastic impedance function, wherein the target azimuth elastic impedance function represents the linear relationship between each reservoir parameter and the azimuth elastic impedance in the reservoir model parameters; based on the target azimuth elastic impedance data, converting the target azimuth elastic impedance function into a matrix form to obtain a matrix form azimuth elastic impedance function, wherein the matrix form azimuth elastic impedance function includes the target forward and inverse calculus operator between the azimuth elastic impedance and the reservoir model parameters, thereby realizing the construction of the target forward and inverse calculus operator between the azimuth elastic impedance and the reservoir model parameters; Based on the target forward and inverse inversion operators and the target azimuth elastic impedance data, the inversion target functional of the reservoir model parameters is constructed. The inversion objective functional is inverted and solved to obtain reservoir parameter prediction results, which include fracture density and fluid factor. The determination of the target orientation elastic impedance function includes: An initial azimuth seismic reflection coefficient function is constructed, which includes the normal weakness parameter and the tangential weakness parameter of the dry crack; Under the assumption of weak anisotropy, the expression functions of the normal weakness parameter and the tangential weakness parameter are constructed using the crack density, respectively. Substituting the representation functions of the normal weakness parameter and the tangential weakness parameter into the initial azimuth seismic reflection coefficient function, the target azimuth seismic reflection coefficient function is obtained, wherein the target azimuth seismic reflection coefficient function includes the fracture density and the fluid factor. Based on the target azimuth seismic reflection coefficient function, an initial azimuth elastic impedance function is constructed; and the target azimuth elastic impedance function is obtained by linearly representing the initial azimuth elastic impedance function.

2. The method of claim 1, wherein, The process of retrieving the target azimuth elastic impedance data from the target five-dimensional seismic data based on the initial azimuth elastic impedance model and the forward and inverse azimuth elastic impedance operators includes: Based on the azimuth elastic impedance forward and inverse operation, an elastic seismic relationship function between the target five-dimensional seismic data and the target azimuth elastic impedance data is constructed. Based on the model-constrained damped least squares inversion method and the initial model of the azimuth elastic impedance, the elastic seismic relationship function is solved to obtain the target azimuth elastic impedance data, so as to realize the inversion of the target azimuth elastic impedance data from the target five-dimensional seismic data.

3. The method according to claim 1 or 2, characterized in that, The step of constructing the inversion target functional of the reservoir model parameters based on the target forward and inverse operators and the target azimuth elastic impedance data includes: Based on the target forward and inverse operation operators and the target azimuth elastic impedance data, the posterior probability distribution function of the reservoir model parameters is determined; The maximum a posteriori probability distribution of the reservoir model parameters is solved by using the posterior probability distribution function to determine the undetermined inversion objective functional of the reservoir model parameters. Based on the undetermined inversion objective functional, the inversion objective functional of the reservoir model parameters is constructed.

4. The method according to claim 1 or 2, characterized in that, The inversion objective functional also includes a low-frequency constraint term for the reservoir model parameters, which is constructed using the reservoir model parameters and the corresponding initial reservoir model parameters.

5. A reservoir prediction device based on five-dimensional seismic data, used to implement the method of claim 1, characterized in that, The device includes: The acquisition unit is used to acquire target five-dimensional seismic data, which includes multiple seismic traces. Each seismic trace corresponds to an incident angle and an azimuth angle. The incident angle corresponding to one seismic trace is one of K incident angles, and the azimuth angle corresponding to one seismic trace is one of V azimuth angles. K and V are both integers greater than 1. The processing unit is used to retrieve the target azimuth elastic impedance data from the target five-dimensional seismic data. The target azimuth elastic impedance data includes azimuth elastic impedance data at any incident angle and any azimuth angle. The processing unit is also used to construct a target forward and inverse operation operator between azimuth elastic impedance and reservoir model parameters, and to construct an inverse target functional of the reservoir model parameters based on the target forward and inverse operation operator and the target azimuth elastic impedance data. The processing unit is also used to perform inversion solution on the inversion objective functional to obtain reservoir parameter prediction results, which include fracture density and fluid factor.

6. An electronic device, characterized in that, include: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-4.

7. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

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