Viscoelastic medium gas content prediction method, electronic equipment, storage medium and device

Abstract

The invention discloses a viscoelastic medium gas-bearing property prediction method, electronic equipment, a storage medium and a device. The method comprises the following steps: acquiring a plurality of pre-stack sub-angle superposition gathers based on a pre-stack full-angle gather; determining dominant frequency band data of each pre-stack sub-angle superposition gather based on typical well seismic channel time frequency analysis, and obtaining a plurality of pre-stack sub-angle frequency division superposition gathers; longitudinal wave impedance inversion is carried out based on each pre-stack sub-angle frequency division superposition gather, and longitudinal wave impedance corresponding to each pre-stack sub-angle frequency division superposition gather is obtained; calculating a frequency-dependent operator inversion coefficient under the viscoelastic medium based on the longitudinal wave impedance corresponding to each pre-stack angle-division frequency-division stack gather; calculating a frequency-dependent viscoelastic fluid factor based on the inversion coefficient of the frequency-dependent operator under the viscoelastic medium; and carrying out viscoelastic medium gas-bearing prediction based on the frequency change viscoelastic fluid factor. According to the method, the gas-bearing property prediction of the viscoelastic medium complex reservoir can be realized, and the inversion precision and the resolution are improved.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, and more specifically, relates to a method for predicting the gas content of viscoelastic media, an electronic device, a storage medium, and a device. Background Technology

[0002] In the prediction of gas content, many scholars have discovered a strong correlation between frequency-varying characteristics and the gas content of underground reservoirs. Therefore, various attribute and inversion methods utilizing frequency-varying characteristics have been developed. When seismic waves propagate in underground fluid-filled porous media, they are affected by the properties of the pore fluid (fluid type, saturation, etc.) and the characteristics of the rock skeleton (porosity, bedrock modulus, etc.), resulting in seismic wave attenuation and dispersion. Foreign scholars have conducted extensive research on the attenuation theory and dispersion laws of seismic waves propagating in porous media, as well as their practical applications. The attenuation and dispersion phenomena of seismic waves propagating in porous fluid-filled media are mainly caused by the equivalent flow of the pore fluid. Based on the different scales of fluid flow, it can be divided into: macroscopic-scale fluid flow, microscopic-scale fluid flow, and mesoscopic-scale fluid flow. In 1956, Biot et al. derived the mass conservation law, generalized Newton's theorem, and elastic constitutive relations for fluids in porous media, establishing the elastic wave theory of fluid-saturated porous media—Biot's theory. Biot theory under two-phase conditions and elastic wave theory under multiphase media (single-phase solid with unsaturated fluid, and two-phase solid media saturated with single-phase fluid) both rely on the attenuation mechanism of seismic waves propagating in fluid-containing media. This mechanism involves the equilibrium between wave crests and troughs, leading to relative motion and friction between fluids and solids, different fluids, and different solids, resulting in attenuation. This macroscopic fluid flow is analogous to the wavelength of the seismic wave. In 1993, Dvorkin and Nur, assuming the presence of a small amount of gas in the porous medium, first combined the macroscopic Biot elastic wave theory with the microscopic "jet flow" attenuation mechanism to establish the BISQ (Biot-Squirt) elastic wave theory. Their predicted relationship between permeability and seismic wave attenuation and dispersion velocity is consistent with experimental measurements. However, the attenuation and dispersion theories of seismic waves discussed above at both macroscopic and microscopic scales often predict values ​​in higher frequency bands and cannot explain the attenuation and dispersion of seismic waves in the seismic frequency band in detail. In 1975, White et al. first proposed the concept of mesoscale rock physics theory. Based on the Biot theoretical framework, they proposed two White models: one is a patchy saturation model of a horizontally layered medium containing two different fluids with periodically alternating patterns, and the other is a patchy saturation model of a saturated water-bearing medium containing periodically arranged spherical bubbles. These models successfully predicted the attenuation and dispersion of seismic frequencies as seismic waves propagate through porous media. Because the White model is mainly used to study the non-uniform wetting phenomenon caused by the infiltration of immiscible multiphase fluids into porous rocks, it is also called the "patchy saturation" model.

[0003] Subsequently, Dutta and Ode (1979) used a more rigorous theory of elasticity to solve for the seismic wave dispersion and attenuation in a saturated model of periodically arranged spherical patches, and analyzed the influence of parameters such as water saturation and spherical radius on seismic wave attenuation and dispersion. Dutta and Serif (1979) further improved the model, enhancing the agreement between the White model and Gassmann's theory at the zero-frequency limit. Gurevich and Lopatnikov (1995) used statistical smoothing theory to derive the plane wave modulus of the periodically layered features with randomly varying characteristic thicknesses in the White model. They found that the mesoscopic attenuation mechanism operates over a wider frequency band in random media because variations in the characteristic thickness cause changes in the characteristic frequency band of seismic wave attenuation. In 2002, Chapman et al. considered the influence of jet flow between adjacent pores and individual microscale fractures when seismic waves propagate in media containing micro-fractures and pores. They then considered the influence of large-scale directional (centimeter-meter scale) fractures, establishing an anisotropic fracture-pore microstructure elastic wave theory. This theory was subsequently used to analyze the effects of fluid type, saturation, porosity, fracture density, and fracture development scale on seismic wave attenuation and dispersion. In 2010, Gurevich et al. established a characteristic unit of a coin-shaped fracture connecting circular pores, deriving expressions for its frequency-varying skeletal bulk modulus and shear modulus. Their results showed low-frequency limits consistent with Gassmann's theory and high-frequency limits consistent with Mavko-Jizba's theory. In 2013, Rubino et al. used a simple numerical simulation process to connect the wave equation obtained by Gurevich et al. (2010) with the mesoscale attenuation mechanism proposed by White, and presented their attenuation and dispersion results.

[0004] Besides meso-scale heterogeneous rock physics models caused by the infiltration of multiphase fluids and non-uniform saturation, non-uniformity due to factors such as rock skeleton structure, pore shape and distribution can also lead to meso-scale heterogeneity, with the most representative example being the two-porosity medium model. Some scholars have studied the seismic response of two-porosity medium models. Berryman et al. first studied the definition and calculation methods of various parameters in the governing equations of two-porosity media, and in 1998 further investigated the elastic wave propagation and attenuation problem in two-porosity, two-infiltrated media. In 2004, Pride et al. conducted a detailed study on the attenuation and velocity dispersion of seismic waves based on three P-wave attenuation models simulated using a two-porosity medium model, indicating that both the meso-scale non-uniform porous solid skeleton model and the multiphase fluid non-uniform saturation model can produce P-wave dispersions consistent with actual observations within the seismic frequency band.

[0005] The construction of the aforementioned rock physics model attempts to clarify the influence of the presence of fluids in the reservoir on attenuation and dispersion phenomena from a mechanistic perspective, and at the same time lays a key theoretical foundation for the application of attenuation and frequency-varying properties in fluid identification (Wang et al., 2012). The method of using seismic wave attenuation and dispersion properties to describe reservoir characteristics can be traced back to 1979. Taner et al. (1979) discovered the phenomenon of frequency shifting towards lower frequencies below oil and gas reservoirs when extracting instantaneous frequency attributes during complex seismic trace analysis, which they called low-frequency shadowing. Castagana et al. (2003) used the matching pursuit instantaneous spectral decomposition technique to detect low-frequency shadowing features related to hydrocarbon content in reservoirs and demonstrated the good application prospects of low-frequency shadowing in seismic data analysis and interpretation, but neither of them clearly gave the formation mechanism of low-frequency shadowing. The study of the causes and numerical simulations of "low-frequency shadows" has been a hot topic of interest for geophysicists (Ebrom et al., 2004; Korneev et al., 2004; Goloshubin et al., 2012; He et al., 2008; Chen et al., 2009).

[0006] Some scholars have conducted extensive research on the quantitative characterization of velocity dispersion, attempting to use seismic data as the main source to achieve quantitative extraction of dispersion attributes in order to enrich the identification methods of reservoir fluid types. Among these, the most critical issue is the mathematical relationship between the frequency variation of medium parameters and seismic reflection characteristics. Some scholars have studied the reflection coefficient of viscoelastic dispersive medium interfaces. Cooper (1967) derived the reflection and transmission coefficients at the interface of linear viscoelastic media; Krebes (1984) and Nechtschein et al. (1997) derived the reflection and transmission coefficients of viscoelastic media; Ursin (2002) derived the reflection and transmission coefficients of viscoelastic isotropic thin-layer media; Sidler and Carcione (2007) analyzed the reflection and transmission characteristics in VTI viscoelastic media; and Ren (2009), combined with the mesoscopic White model, used numerical modeling to study the variation of normal incident amplitude with frequency at dispersive interfaces and classified it into three types. This provides theoretical guidance for the study of AVF (Amplitude Variation with Frequency). The above studies are mainly based on theoretical analysis based on various assumptions, and it is difficult to extract dispersive properties based on actual data. The key to quantitative extraction of dispersion attributes based on seismic reflection data lies in establishing the mathematical relationship between dispersion attributes and reflection coefficients, namely AVF (Cooper et al., 1967; Krebes et al., 1984; Nechtschein et al., 1987; Ren et al., 2009; Innanen et al., 2011). Due to the complexity of the AVF theoretical formula and the limitations of various assumptions, it is difficult to quantitatively extract dispersion attributes from actual seismic data, and it cannot be directly applied to seismic data processing and parameter inversion. Wilson et al. (2009) proposed a frequency-varying AVO inversion method based on the Chapman dispersion rock physics model, combined with spectral decomposition technology and the Smith-Gidlow approximation formula; Wu et al. (2010) implemented a frequency-varying AVO inversion method by combining the RSPWVD algorithm. The frequency-varying AVO inversion introduced above is actually based on the generalization of dispersion, and does not derive the pre-stack frequency-varying reflection characteristic equation containing the pre-stack frequency-varying fluid factor based on the characteristics of the attenuating medium. Furthermore, in the solution process, the conventional frequency-varying AVO inversion method adopts the elastic impedance inversion technique with smooth constraints, and the inversion accuracy and resolution of fluid factors still need to be improved.

[0007] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to propose a method, electronic device, storage medium and apparatus for predicting the gas content of viscoelastic media, thereby improving the accuracy and resolution of inversion.

[0009] To achieve the above objectives, the present invention proposes a method for predicting the gas content of viscoelastic media, an electronic device, a storage medium, and a device.

[0010] According to a first aspect of the present invention, a method for predicting the gas content of viscoelastic media is proposed, comprising:

[0011] Multiple pre-stack angle-segment stacked gathers were obtained based on the pre-stack full-angle gathers;

[0012] Based on time-frequency analysis of typical well seismic traces, the dominant frequency band data of each pre-stack angle-division stacked gather are determined, resulting in multiple pre-stack angle-division frequency-division stacked gathers.

[0013] Based on each of the pre-stack angle-division and frequency-division stacked gathers, longitudinal wave impedance inversion is performed to obtain the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division stacked gathers.

[0014] Calculate the frequency-varying operator inversion coefficients under viscoelastic medium based on the longitudinal wave impedance corresponding to each pre-stack angle- and frequency-divided stacked gather;

[0015] Calculate the frequency-varying viscoelastic fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium;

[0016] Predicting the gas content of viscoelastic media based on the frequency-varying viscoelastic fluid factor.

[0017] Optionally, obtaining multiple pre-stack angle-segment stacking gathers based on pre-stack full-angle gathers includes:

[0018] Based on the characteristics of pre-stack seismic data, an adaptive attenuation algorithm is used to determine the angle range of angle stacking. The FN angle stacking algorithm is then used to stack the pre-stack full-angle gathers angle by angle to obtain multiple pre-stack angle stacked gathers.

[0019] Optionally, the determination of the dominant frequency band data of each of the pre-stack angle-division stacking gathers based on time-frequency analysis of typical well seismic traces, resulting in multiple pre-stack angle-division frequency-division stacking gathers, includes:

[0020] The time-frequency characteristics of the typical well seismic traces were analyzed to determine the dominant frequency;

[0021] Based on the main frequency, the advantageous frequency band data of each of the pre-stack angle-division stacked gathers are obtained, resulting in multiple pre-stack angle-division frequency-division stacked gathers corresponding to each of the pre-stack angle-division stacked gathers.

[0022] Optionally, the calculation of the fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium includes:

[0023] Construct a set of inversion equations for frequency-varying viscoelastic fluid factors;

[0024] The frequency-varying viscoelastic fluid factor is calculated using the repeated weighted least squares method based on the inversion equation set of the frequency-varying viscoelastic fluid factor and the inversion coefficients of the frequency-varying operator under the viscoelastic medium.

[0025] Optionally, the prediction of gas content in viscoelastic media based on the frequency-varying viscoelastic fluid factor includes:

[0026] A frequency-varying AVO inversion objective functional is established based on Bayesian theory;

[0027] Gas content prediction is performed based on the frequency-varying AVO inversion objective functional and the frequency-varying viscoelastic fluid factor.

[0028] Optionally, the inversion equations for the frequency-varying viscoelastic fluid factor are:

[0029]

[0030] Where θ1, θ2, ... θ N This represents the stacking angle of N pre-stack gathers, where ω0 represents the dominant frequency, ω1, ω2, ... ω M This represents the M frequency divisions used in seismic data inversion, QEI(θ) n ,ω m ) represents the use of θ under sparse constraints n Angle superposition ω m The elastic impedance, i.e., the longitudinal wave impedance, is obtained by pre-stack angle-division frequency division superposition gather inversion after frequency division, where n = {1, 2, ... N}, m = {1, 2, ... M}, a(θ) n ,ω m )Δω m and b(θ) n ,ω m )Δω m Here are the frequency-varying viscoelastic parameter coefficients, Δω m =ω m -ω0, lnI ane Let lnI represent the frequency-varying viscoelastic fluid factor to be inverted in logarithmic form. μ Indicates the other parameters to be inverted in logarithmic form.

[0031] Optionally, the frequency-varying viscoelastic parameter coefficient a(θ) is calculated based on the inversion results of the frequency-varying elastic impedance of the well-side seismic trace and the well logging data. n ,ω m )Δω m and b(θ) n ,ω m )Δω mThe expression for calculating the frequency-varying viscoelastic parameter coefficient is as follows:

[0032]

[0033] Where t1, t2, ... t NN This represents the different times of N sampling points; This represents the logarithmic form of the frequency-varying viscoelastic fluid factor calculated using well logging data. The logarithmic form of other parameters, QEI(t1,θ), is calculated using well logging data. n ,ω m ) is the seismic trace near the well at time t1 using θ n Pre-stack gathers of angle superposition at ω m Frequency-varying elastic impedance obtained by inverting frequency-division angle-division frequency seismic data.

[0034] According to a second aspect of the present invention, a device for predicting the gas content of a viscoelastic medium is provided, comprising:

[0035] The acquisition module is used to acquire multiple pre-stack angle-segment stacked gathers based on the pre-stack full-angle gathers;

[0036] The determination module is used to determine the dominant frequency band data of each pre-stack angle-division stacked gather based on time-frequency analysis of typical well seismic traces, and to obtain multiple pre-stack angle-division frequency-division stacked gathers.

[0037] The longitudinal wave impedance inversion module is used to perform longitudinal wave impedance inversion based on each of the pre-stack angle-division and frequency-division stacked gathers, and obtain the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division stacked gathers.

[0038] The first calculation module is used to calculate the frequency-varying operator inversion coefficients under viscoelastic medium based on the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division superimposed gathers.

[0039] The second calculation module is used to calculate the frequency-varying viscoelastic fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium.

[0040] The prediction module is used to predict the gas content of viscoelastic media based on the frequency-varying viscoelastic fluid factor.

[0041] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0042] At least one processor; and,

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method for predicting the gas content of viscoelastic media according to any of the first aspects.

[0045] According to a fourth aspect of the invention, a non-transitory computer-readable storage medium is provided, which stores computer instructions for causing a computer to perform the method for predicting the gas content of a viscoelastic medium as described in any of the first aspects.

[0046] The beneficial effects of this invention are as follows: This invention obtains the superior pre-stack angle- and frequency-division superimposed gathers from the pre-stack full-angle gathers as input, obtains the corresponding longitudinal wave impedance results, and then calculates the frequency-varying operator inversion coefficients under viscoelastic media, thereby calculating the frequency-varying viscoelastic fluid factor. Based on the frequency-varying viscoelastic fluid factor, the gas content of viscoelastic media is predicted, realizing the prediction of gas content in complex viscoelastic reservoirs, improving the inversion accuracy and resolution, and to a certain extent making the anomalies more focused. The theory is rigorous and reliable, and the operation process is simple and practical.

[0047] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0048] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0049] Figure 1 A flowchart illustrating the steps of a method for predicting the gas content of a viscoelastic medium according to the present invention is shown.

[0050] Figure 2 A schematic diagram of a seismic data frequency division profile according to Embodiment 2 of the present invention is shown.

[0051] Figure 3 A schematic diagram of typical well time-frequency analysis results according to Embodiment 2 of the present invention is shown.

[0052] Figure 4 A schematic diagram of the gas-bearing prediction results of the well profile according to Embodiment 2 of the present invention is shown. Detailed Implementation

[0053] The invention will now be described in more detail with reference to the accompanying drawings. While preferred 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 limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0054] like Figure 1 As shown, a method for predicting the gas content of a viscoelastic medium according to the present invention includes:

[0055] Multiple pre-stack angle-segment stacked gathers were obtained based on the pre-stack full-angle gathers;

[0056] Based on time-frequency analysis of typical well seismic traces, the dominant frequency band data of each pre-stack angle-division stacked gathers were determined, resulting in multiple pre-stack angle-division frequency-division stacked gathers.

[0057] Based on each pre-stack angle- and frequency-divided stacked gather, longitudinal wave impedance inversion is performed to obtain the longitudinal wave impedance corresponding to each pre-stack angle- and frequency-divided stacked gather.

[0058] Calculate the frequency-varying operator inversion coefficients for viscoelastic media based on the longitudinal wave impedance corresponding to each pre-stack angle- and frequency-divided stacked gather;

[0059] Calculation of frequency-varying viscoelastic fluid factor based on inversion coefficients of frequency-varying operators in viscoelastic media;

[0060] Prediction of gas content in viscoelastic media based on frequency-varying viscoelastic fluid factors.

[0061] Specifically, this invention employs an adaptive attenuation algorithm to determine the angle range for angle-by-angle stacking, and uses the FN angle-by-angle stacking algorithm to perform angle-by-angle stacking on pre-stack full-angle gathers, resulting in multiple pre-stack angle-by-angle stacked gathers, typically three. It analyzes the time-frequency characteristics of typical well-side seismic traces, selecting the dominant frequency, low frequencies below the dominant frequency, and high frequencies above the dominant frequency as data frequency-division outputs. From each pre-stack angle-by-angle stacked gather, three pre-stack angle-by-angle frequency-division stacked gathers containing dominant data are extracted. Based on each pre-stack angle-by-angle frequency-division stacked gather, P-wave impedance inversion is performed to obtain the corresponding P-wave impedance. Based on the P-wave impedance corresponding to each pre-stack angle-by-angle frequency-division stacked gather, the frequency-varying operator inversion coefficients under viscoelastic media are calculated. Based on the frequency-varying operator inversion coefficients under viscoelastic media, the frequency-varying viscoelastic fluid factor is calculated. Based on the frequency-varying viscoelastic fluid factor, the gas content of the viscoelastic medium is predicted. This invention uses pre-stack angle- and frequency-division stacked gathers, which are advantageous obtained from pre-stack full-angle gathers, as input to obtain the corresponding P-wave impedance results. Then, it calculates the frequency-varying operator inversion coefficients under viscoelastic media, thereby calculating the frequency-varying viscoelastic fluid factor. Based on the frequency-varying viscoelastic fluid factor, it performs gas-bearing prediction of viscoelastic media, realizing the prediction of gas-bearing in complex viscoelastic reservoirs. This improves the inversion accuracy and resolution, and to a certain extent makes anomalies more focused. The theory is rigorous and reliable, and the operation process is simple and practical.

[0062] In one example, obtaining multiple pre-stack angle-segment stacking gathers based on pre-stack full-angle gathers includes:

[0063] Based on the characteristics of pre-stack seismic data, an adaptive attenuation algorithm is used to determine the angle range of angle stacking. The FN angle stacking algorithm is then used to stack the pre-stack full-angle gathers angle by angle to obtain multiple pre-stack angle stacked gathers.

[0064] In one example, based on time-frequency analysis of typical well seismic traces, the dominant frequency band data of each pre-stack angle-division stacking gather were determined, resulting in multiple pre-stack angle-division and frequency-division stacking gathers, including:

[0065] The time-frequency characteristics of typical well seismic traces are analyzed to determine the dominant frequency;

[0066] Based on the main frequency, the advantageous frequency band data of each pre-stack angle-division stacked gather is obtained, resulting in multiple pre-stack angle-division frequency-division stacked gathers corresponding to each pre-stack angle-division stacked gather.

[0067] In one example, the calculation of the fluid factor based on the inversion coefficients of the frequency-varying operator in a viscoelastic medium includes:

[0068] Construct a set of inversion equations for frequency-varying viscoelastic fluid factors;

[0069] The frequency-varying viscoelastic fluid factor is calculated using the iterative weighted least squares method based on the inversion equations of the frequency-varying viscoelastic fluid factor and the inversion coefficients of the frequency-varying operator under viscoelastic media.

[0070] In one example, predicting the gas content of a viscoelastic medium based on a frequency-varying viscoelastic fluid factor includes:

[0071] A frequency-varying AVO inversion objective functional is established based on Bayesian theory;

[0072] Gas content prediction is carried out based on the frequency-varying AVO inversion objective functional and the frequency-varying viscoelastic fluid factor.

[0073] In one example, the inversion equations for the frequency-varying viscoelastic fluid factor are as follows:

[0074]

[0075] Where θ1, θ2, ... θ N This represents the stacking angle of N pre-stack gathers, where ω0 represents the dominant frequency, ω1, ω2, ... ω M This represents the M frequency divisions used in seismic data inversion, QEI(θ) n ,ω m ) represents the use of θ under sparse constraints n Angle superposition ω m The elastic impedance, i.e., the longitudinal wave impedance, is obtained by pre-stack angle-division frequency division superposition gather inversion after frequency division, where n = {1, 2, ... N}, m = {1, 2, ... M}, a(θ) n ,ω m )Δω m and b(θ) n ,ω m )Δω m Here are the frequency-varying viscoelastic parameter coefficients, Δω m =ω m -ω0, lnI ane Let lnI represent the frequency-varying viscoelastic fluid factor to be inverted in logarithmic form. μ Indicates the other parameters to be inverted in logarithmic form.

[0076] In one example, the frequency-varying viscoelastic parameter coefficient a(θ) is calculated based on the inversion results of the frequency-varying elastic impedance of the well-side seismic trace and well logging data. n ,ω m )Δω m and b(θ) n ,ω m )Δω m The expression for calculating the frequency-varying viscoelastic parameter coefficients is:

[0077]

[0078] Where t1, t2, ... t MM This represents the different times of N sampling points; This represents the logarithmic form of the frequency-varying viscoelastic fluid factor calculated using well logging data. The logarithmic form of other parameters, QEI(t1,θ), is calculated using well logging data. n ,ω m ) is the seismic trace near the well at time t1 using θ n Pre-stack gathers of angle superposition at ω m Frequency-varying elastic impedance obtained by inverting frequency-division angle-division frequency seismic data.

[0079] Specifically, when the selected incident angle and frequency remain constant, the required elastic parameters... and lnI μ The coefficients remain unchanged at each sampling point. To quickly and reliably obtain the required elastic parameters, the frequency-varying viscoelastic parameter coefficients can be calculated first using the inversion results of the frequency-varying elastic impedance from the well-side seismic traces and well logging data. The following matrix can be constructed:

[0080]

[0081] Where t1, t2, ... t NN This represents the different times of N sampling points; This represents the logarithmic form of the frequency-varying viscoelastic fluid factor calculated using well logging data. The logarithmic form of other parameters, QEI(t1,θ), is calculated using well logging data. n ,ω m ) is the seismic trace near the well at time t1 using θ n Pre-stack gathers of angle superposition at ω m Frequency-varying elastic impedance obtained by inverting frequency-division angle-division frequency seismic data.

[0082] Solving the above matrix yields the frequency-varying viscoelastic parameter coefficients a(θ). n ,ω m )Δω m and b(θ) n ,ω m )Δω m It should be noted that, due to the previous assumption of N incident angles and M frequency information, the obtained variable viscoelastic parameter coefficient a(θ) n ,ω m )Δω m and b(θ) n ,ω m )Δω m Similarly, there are values ​​for N incident angles and M frequencies, so the coefficients can be written as an M*N matrix. By substituting the frequency-varying viscoelastic fluid factor inversion equations and combining them with elastic impedance data, the required frequency-varying viscoelastic fluid factor and frequency-varying viscoelastic shear modulus can be obtained.

[0083] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0084] Example 1

[0085] This embodiment provides a method for predicting the gas content of viscoelastic media, including:

[0086] Multiple pre-stack angle-segment stacked gathers were obtained based on the pre-stack full-angle gathers;

[0087] Based on time-frequency analysis of typical well seismic traces, the dominant frequency band data of each pre-stack angle-division stacked gathers were determined, resulting in multiple pre-stack angle-division frequency-division stacked gathers.

[0088] Based on each pre-stack angle- and frequency-divided stacked gather, longitudinal wave impedance inversion is performed to obtain the longitudinal wave impedance corresponding to each pre-stack angle- and frequency-divided stacked gather.

[0089] Calculate the frequency-varying operator inversion coefficients for viscoelastic media based on the longitudinal wave impedance corresponding to each pre-stack angle- and frequency-divided stacked gather;

[0090] Calculation of frequency-varying viscoelastic fluid factor based on inversion coefficients of frequency-varying operators in viscoelastic media;

[0091] Prediction of gas content in viscoelastic media based on frequency-varying viscoelastic fluid factors.

[0092] Multiple pre-stack angle-segment stacking gathers were obtained based on pre-stack full-angle gathers, including:

[0093] Based on the characteristics of pre-stack seismic data, an adaptive attenuation algorithm is used to determine the angle range of angle stacking. The FN angle stacking algorithm is then used to stack the pre-stack full-angle gathers angle by angle to obtain multiple pre-stack angle stacked gathers.

[0094] Based on time-frequency analysis of typical well seismic traces, the dominant frequency band data of each pre-stack angle-division stacking gather were determined, resulting in multiple pre-stack angle-division and frequency-division stacking gathers, including:

[0095] The time-frequency characteristics of typical well seismic traces are analyzed to determine the dominant frequency;

[0096] Based on the main frequency, the advantageous frequency band data of each pre-stack angle-division stacked gather is obtained, resulting in multiple pre-stack angle-division frequency-division stacked gathers corresponding to each pre-stack angle-division stacked gather.

[0097] The fluid factor is calculated based on the inversion coefficients of the frequency-varying operator in viscoelastic media, including:

[0098] Construct a set of inversion equations for frequency-varying viscoelastic fluid factors;

[0099] The frequency-varying viscoelastic fluid factor is calculated using the iterative weighted least squares method based on the inversion equations of the frequency-varying viscoelastic fluid factor and the inversion coefficients of the frequency-varying operator under viscoelastic media.

[0100] Prediction of gas-containing properties of viscoelastic media based on frequency-varying viscoelastic fluid factors includes:

[0101] A frequency-varying AVO inversion objective functional is established based on Bayesian theory;

[0102] Gas content prediction is carried out based on the frequency-varying AVO inversion objective functional and the frequency-varying viscoelastic fluid factor.

[0103] The inversion equations for the frequency-varying viscoelastic fluid factor are as follows:

[0104]

[0105] Where θ1, θ2, ... θ N This represents the stacking angle of N pre-stack gathers, where ω0 represents the dominant frequency, ω1, ω2, ... ω M This represents the M frequency divisions used in seismic data inversion, QEI(θ) m ,ω m ) represents the use of θ under sparse constraints n Angle superposition ω m The elastic impedance, i.e., the longitudinal wave impedance, is obtained by pre-stack angle-division frequency division superposition gather inversion after frequency division, where n = {1, 2, ... N}, m = {1, 2, ... M}, a(θ) n ,ω n )Δω m and b(θ) n ,ω m )Δω m Here are the frequency-varying viscoelastic parameter coefficients, Δω m =ω m -ω0, lnI ane Let lnI represent the frequency-varying viscoelastic fluid factor to be inverted in logarithmic form. μ This represents the rigidity parameter of the solid to be inverted in logarithmic form.

[0106] Based on the inversion results of frequency-variable elastic impedance from the well-side seismic trace and well logging data, the frequency-variable viscoelastic parameter coefficient a(θ) was calculated. n ,ω m )Δω m and b(θ) n ,ω m )Δω m The expression for calculating the frequency-varying viscoelastic parameter coefficients is:

[0107]

[0108] Where t1, t2, ... t NN This represents the different times of N sampling points; This represents the logarithmic form of the frequency-varying viscoelastic fluid factor calculated using well logging data. The logarithmic form of other parameters, QEI(t1,θ), is calculated using well logging data. n ,ω m ) is the seismic trace near the well at time t1 using θ n Pre-stack gathers of angle superposition at ω m Frequency-varying elastic impedance obtained by inverting frequency-division angle-division frequency seismic data.

[0109] Example 2

[0110] This embodiment provides a method for predicting the gas content of viscoelastic media, including:

[0111] 1. Obtaining multiple pre-stack angle-segment stacking gathers based on pre-stack full-angle gathers

[0112] An adaptive attenuation algorithm is used to determine the angle range of the angle stacking. The FN angle stacking algorithm is used to stack the pre-stack full angle gathers by angle, resulting in three pre-stack angle stacked gathers.

[0113] 2. Determining the dominant frequency band of matched tracking data based on time-frequency analysis of typical well seismic traces.

[0114] This embodiment analyzes the time-frequency characteristics of typical well-side seismic traces, selects the dominant frequency data, the frequency with obvious time-frequency characteristics in the low-frequency part below the dominant frequency, and the frequency with obvious time-frequency characteristics in the high-frequency part above the dominant frequency as the data frequency division output, and selects 3 pre-stack frequency division angle stacked traces from each pre-stack angle stacked trace, resulting in a total of 9 pre-stack frequency division angle stacked traces.

[0115] 3. Inverting longitudinal wave impedance based on superior data input

[0116] Seismic inversion requires well logging data modeling, well-seismic calibration wavelet extraction, and well logging constraints during the inversion process. Using pre-stack frequency- and angle-division stacked gathers as input, P-wave impedance results were obtained under nine dominant angle- and frequency-division data sets.

[0117] 4. Realizing the prediction and application of gas content in viscoelastic media

[0118] Using nine longitudinal wave impedances as inputs, the frequency-varying operator inversion coefficients under viscoelastic media are calculated.

[0119] Calculation of frequency-varying viscoelastic fluid factor based on inversion coefficients of frequency-varying operators in viscoelastic media;

[0120] Prediction of gas content in viscoelastic media based on frequency-varying viscoelastic fluid factors.

[0121] The inversion equations for the frequency-varying viscoelastic fluid factor are as follows:

[0122]

[0123] Where θ1, θ2, ... θ N This represents the stacking angle of N pre-stack gathers, where ω0 represents the dominant frequency, ω1, ω2, ... ω M This represents the M frequency divisions used in seismic data inversion, QEI(θ) m ,ω m ) represents the use of θ under sparse constraints n Angle superposition ω m The elastic impedance, i.e., the longitudinal wave impedance, is obtained by pre-stack angle-division frequency division superposition gather inversion after frequency division, where n = {1, 2, ... N}, m = {1, 2, ... M}, a(θ) n ,ω m )(Δω m ) and b(θ n ,ω m )(Δω m ) represents the inversion coefficients of the frequency-varying operator in viscoelastic media, Δω m =ω m -ω0, lnI ane Let lnI represent the frequency-varying viscoelastic fluid factor to be inverted in logarithmic form. μ This represents the rigidity parameter of the solid to be inverted in logarithmic form.

[0124] When the selected incident angle and frequency remain constant, the elastic parameters to be calculated are... and lnI μ The coefficients remain unchanged at each sampling point. To quickly and reliably obtain the required elastic parameters, the frequency-varying viscoelastic parameter coefficients can be calculated first using the inversion results of the frequency-varying elastic impedance from the well-side seismic traces and well logging data. The following matrix can be constructed:

[0125]

[0126] Where t1, t2, ... t NN This represents the different times of N sampling points; This represents the logarithmic form of the frequency-varying viscoelastic fluid factor calculated using well logging data. The logarithmic form of other parameters, QEI(t1,θ), is calculated using well logging data. n ,ω m ) is the seismic trace near the well at time t1 using θ n Pre-stack gathers of angle superposition at ω m Frequency-varying elastic impedance obtained by inverting frequency-division angle-division frequency seismic data.

[0127] Solving the above matrix yields the frequency-varying viscoelastic parameter coefficients a(θ). n ,ω m )Δω m and b(θ) n ,ω m )Δω m It should be noted that, due to the previous assumption of N incident angles and M frequency information, the obtained variable viscoelastic parameter coefficient a(θ) n ,ω m )Δω m and b(θ) n ,ω m )Δω m Similarly, there are values ​​for N incident angles and M frequencies, so the coefficients can be written as an M*N matrix. By substituting the frequency-varying viscoelastic fluid factor inversion equations and combining them with elastic impedance data, the required frequency-varying viscoelastic fluid factor and frequency-varying viscoelastic shear modulus can be obtained.

[0128] Regarding seismic constraints, a Bayesian inversion framework is adopted. Bayesian theory links seismic data with the sparsity constraint of prior distribution. Incorporating known information such as well logging and geological data as prior constraints into the objective function can mitigate the problem of multiple solutions in the inversion process, making the inversion results more realistic.

[0129] A frequency-varying AVO inversion objective functional is established based on Bayesian theory, and gas content prediction is carried out based on the frequency-varying AVO inversion objective functional and the frequency-varying viscoelastic fluid factor.

[0130] The gas content prediction method for viscoelastic media in this embodiment is used to predict the gas content of a certain work area. The frequency-division seismic profile is as follows. Figure 2 As shown, the time-frequency analysis position of a typical well Figure 3 The data frequency was determined to be around 30Hz, and the well-connection profile was as follows. Figure 4 As shown, the gas-bearing prediction results of typical wells are significantly higher than those of conventional pre-stack inversion, with a consistency rate of over 80%, which is consistent with the gas-bearing prediction results.

[0131] Example 3

[0132] This embodiment provides a device for predicting the gas content of viscoelastic media, including:

[0133] The acquisition module is used to acquire multiple pre-stack angle-segment stacked gathers based on the pre-stack full-angle gathers;

[0134] The determination module is used to determine the dominant frequency band data of each pre-stack angle-division stacked gather based on time-frequency analysis of typical well seismic traces, and to obtain multiple pre-stack angle-division frequency-division stacked gathers.

[0135] The longitudinal wave impedance inversion module is used to perform longitudinal wave impedance inversion based on each of the pre-stack angle-division and frequency-division stacked gathers, and obtain the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division stacked gathers.

[0136] The first calculation module is used to calculate the frequency-varying operator inversion coefficients under viscoelastic medium based on the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division superimposed gathers.

[0137] The second calculation module is used to calculate the frequency-varying viscoelastic fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium.

[0138] The prediction module is used to predict the gas content of viscoelastic media based on the frequency-varying viscoelastic fluid factor.

[0139] Example 4

[0140] This disclosure also provides an electronic device, which includes:

[0141] At least one processor; and,

[0142] A memory communicatively connected to the at least one processor; wherein,

[0143] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the viscoelastic medium gas content prediction method in Embodiment 1.

[0144] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0145] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0146] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0147] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0148] Example 5

[0149] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the viscoelastic medium gas content prediction method in Embodiment 1.

[0150] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0151] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0152] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for predicting the gas content of viscoelastic media, characterized in that, include: Multiple pre-stack angle-segment stacked gathers were obtained based on the pre-stack full-angle gathers; Based on time-frequency analysis of typical well seismic traces, the dominant frequency band data of each pre-stack angle-division stacked gather are determined, resulting in multiple pre-stack angle-division frequency-division stacked gathers. Based on each of the pre-stack angle-division and frequency-division stacked gathers, longitudinal wave impedance inversion is performed to obtain the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division stacked gathers. Calculate the frequency-varying operator inversion coefficients under viscoelastic medium based on the longitudinal wave impedance corresponding to each pre-stack angle- and frequency-divided stacked gather; Calculate the frequency-varying viscoelastic fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium; Predicting the gas content of viscoelastic media based on the frequency-varying viscoelastic fluid factor.

2. The method for predicting the gas content of viscoelastic media according to claim 1, characterized in that, The method of obtaining multiple pre-stack angle-segment stacking gathers based on pre-stack full-angle gathers includes: Based on the characteristics of pre-stack seismic data, an adaptive attenuation algorithm is used to determine the angle range of angle stacking. The FN angle stacking algorithm is then used to stack the pre-stack full-angle gathers angle by angle to obtain multiple pre-stack angle stacked gathers.

3. The method for predicting the gas content of viscoelastic media according to claim 1, characterized in that, The dominant frequency band data of each of the pre-stack angle-division stacked gathers are determined based on time-frequency analysis of typical well seismic traces, resulting in multiple pre-stack angle-division and frequency-division stacked gathers, including: The time-frequency characteristics of the typical well seismic traces were analyzed to determine the dominant frequency; Based on the main frequency, the advantageous frequency band data of each of the pre-stack angle-division stacked gathers are obtained, resulting in multiple pre-stack angle-division frequency-division stacked gathers corresponding to each of the pre-stack angle-division stacked gathers.

4. The method for predicting the gas content of viscoelastic media according to claim 1, characterized in that, The calculation of the fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium includes: Construct a set of inversion equations for frequency-varying viscoelastic fluid factors; The frequency-varying viscoelastic fluid factor is calculated using the repeated weighted least squares method based on the inversion equation set of the frequency-varying viscoelastic fluid factor and the inversion coefficients of the frequency-varying operator under the viscoelastic medium.

5. The method for predicting the gas content of viscoelastic media according to claim 1, characterized in that, The prediction of gas content in viscoelastic media based on the frequency-varying viscoelastic fluid factor includes: A frequency-varying AVO inversion objective functional is established based on Bayesian theory; Gas content prediction is performed based on the frequency-varying AVO inversion objective functional and the frequency-varying viscoelastic fluid factor.

6. The method for predicting the gas content of viscoelastic media according to claim 4, characterized in that, The inversion equations for the frequency-varying viscoelastic fluid factor are as follows: Where θ1, θ2, ... θ N This represents the stacking angle of N pre-stack gathers, where ω0 represents the dominant frequency, ω1, ω2, ... ω M This represents the M frequency divisions used in seismic data inversion, QEI(θ) n ,ω m ) represents the use of θ under sparse constraints n Angle superposition ω m The elastic impedance, i.e., the longitudinal wave impedance, is obtained by pre-stack angle-division frequency division superposition gather inversion after frequency division, where n = {1, 2, ... N}, m = {1, 2, ... M}, a(θ) n ,ω m )Δω m and b(θ) n ,ω m )Δω m Here are the frequency-varying viscoelastic parameter coefficients, Δω m =ω m -ω0, lnI ane Let lnI represent the frequency-varying viscoelastic fluid factor to be inverted in logarithmic form. μ This represents the other parameters to be inverted in logarithmic form.

7. The method for predicting the gas content of viscoelastic media according to claim 6, characterized in that, Based on the inversion results of frequency-varying elastic impedance from the well-side seismic traces and well logging data, the frequency-varying viscoelastic parameter coefficient a(θ) was calculated. n ,ω m )Δω m and b(θ) n ,ω m )Δω m The expression for calculating the frequency-varying viscoelastic parameter coefficients is as follows: Where t1, t2, ... t NN This represents the different times of N sampling points; This represents the logarithmic form of the frequency-varying viscoelastic fluid factor calculated using well logging data. The logarithmic form of other parameters, QEI(t1,θ), is calculated using well logging data. n ,ω m ) is the seismic trace near the well at time t1 using θ n Pre-stack gathers of angle superposition at ω m Frequency-varying elastic impedance obtained by inverting frequency-division angle-division seismic data.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method for predicting the gas content of viscoelastic media according to any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method for predicting the gas content of a viscoelastic medium as described in any one of claims 1-7.

10. A device for predicting the gas content of viscoelastic media, characterized in that, include: The acquisition module is used to acquire multiple pre-stack angle-segment stacked gathers based on the pre-stack full-angle gathers; The determination module is used to determine the dominant frequency band data of each pre-stack angle-division stacked gather based on time-frequency analysis of typical well seismic traces, and to obtain multiple pre-stack angle-division frequency-division stacked gathers. The longitudinal wave impedance inversion module is used to perform longitudinal wave impedance inversion based on each of the pre-stack angle-division and frequency-division stacked gathers, and obtain the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division stacked gathers. The first calculation module is used to calculate the frequency-varying operator inversion coefficients under viscoelastic medium based on the longitudinal wave impedance corresponding to each of the pre-stack angle-division and frequency-division superimposed gathers. The second calculation module is used to calculate the frequency-varying viscoelastic fluid factor based on the frequency-varying operator inversion coefficients under the viscoelastic medium. The prediction module is used to predict the gas content of viscoelastic media based on the frequency-varying viscoelastic fluid factor.

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