Generalized linear inversion method, electronic equipment, storage medium and device

Abstract

The invention discloses a generalized linear inversion method, electronic equipment, a storage medium and a device. The method comprises the following steps: constructing a fluid indication factor; constructing a generalized linear equation based on the fluid indication factor; and carrying out generalized linear inversion based on the generalized linear equation. According to the method, the generalized linear equation is constructed by constructing the new fluid indication factor, the generalized linear equation is used for carrying out generalized linear inversion to carry out fluid prediction, the newly constructed fluid indication factor has wider applicability, fluid prediction of a complex reservoir can be realized, and the fluid prediction precision and the fluid identification effect are improved.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, and more specifically, relates to a generalized linear inversion method, electronic equipment, storage medium and device. Background Technology

[0002] With increasing demand for oil and gas, the scope of exploration is gradually shifting from conventional oil and gas reservoirs to complex unconventional ones, thus increasing the difficulty of reservoir prediction and oil and gas identification. Compared to stacked profiles, pre-stack data has attracted much attention due to its rich gather information. AVO inversion technology, which describes the variation of amplitude with incident angle (offset), can obtain comprehensive angular gather information from seismic data, playing a crucial role in oil and gas exploration and reservoir characterization. Therefore, using AVO inversion technology to study elastic parameter inversion is an important research endeavor.

[0003] Due to factors such as exploration environment, observation errors, and exploration methods, inaccuracy, instability, and ill-posedness of inversion results are challenges in AVO inversion. Generalized linear inversion seismic exploration technology provides more comprehensive seismic data, including P-wave data containing fluid and rock skeleton information, and S-wave data containing rock skeleton information. It also obtains a more comprehensive elastic wave vector wavefield, and has received widespread attention in seismic exploration technology. The more accurate generalized linear inversion technique for AVO inversion can, to some extent, improve the instability problems existing in the inversion process, and provides a better direction for the description and evaluation of complex reservoir oil and gas reservoirs.

[0004] 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

[0005] The purpose of this invention is to propose a generalized linear inversion method, electronic device, storage medium, and apparatus to achieve fluid prediction in complex reservoirs and improve the accuracy of process prediction.

[0006] To achieve the above objectives, the present invention proposes a generalized linear inversion method, an electronic device, a storage medium, and an apparatus.

[0007] According to a first aspect of the present invention, a generalized linear inversion method is proposed, comprising:

[0008] Construct fluid indicator factors;

[0009] A generalized linear equation is constructed based on the fluid indicator factors;

[0010] Generalized linear inversion is performed based on the generalized linear equation.

[0011] Optionally, the generalized construction fluid indicator factor is:

[0012] Fρ=2σλρ;

[0013] Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

[0014] Optionally, the generalized linear equation is:

[0015] [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b;

[0016] Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

[0017] Optionally, the process of constructing the fluid indicator factor further includes:

[0018] The sensitivity of the fluid indicator factors to fluids was analyzed based on rock physics data.

[0019] According to a second aspect of the present invention, a generalized linear inversion apparatus is proposed, comprising:

[0020] The first building block is used to construct the fluid indicator factor;

[0021] The second construction module is used to construct a generalized linear equation based on the fluid indicator factor;

[0022] The prediction module is used to perform generalized linear inversion based on the generalized linear equation.

[0023] Optionally, the generalized construction fluid indicator factor is:

[0024] Fρ=2σλρ;

[0025] Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

[0026] Optionally, the generalized linear equation is:

[0027] [A1 A2 A3 A4][R PP R PS T PP TPS ] T =b;

[0028] Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

[0029] Optionally, the process of constructing the fluid indicator factor further includes:

[0030] The sensitivity of the fluid indicator factors to fluids was analyzed based on rock physics data.

[0031] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0032] At least one processor; and,

[0033] A memory communicatively connected to the at least one processor; wherein,

[0034] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the generalized linear inversion method described in any of the first aspects.

[0035] 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 generalized linear inversion method described in any of the first aspects.

[0036] The beneficial effects of this invention are as follows: This invention constructs a new fluid indicator factor and then constructs a generalized linear equation, which is then used to perform generalized linear inversion for fluid prediction. The newly constructed fluid indicator factor has a wider range of applicability, enabling fluid prediction of complex reservoirs and improving the accuracy of fluid prediction and the effect of fluid identification.

[0037] 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

[0038] 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.

[0039] Figure 1 A flowchart illustrating the steps of a generalized linear inversion method according to the present invention is shown.

[0040] Figure 2 a, Figure 2 b and Figure 2 c respectively shows the fluid indicator factor λρ according to the present invention. The curve of Fρ.

[0041] Figure 3 a and Figure 3 b shows the curves of the lithological indicator factors μρ and Eρ according to the present invention.

[0042] Figure 4 a, Figure 4 b、 Figure 4 c and Figure 4 Figure d shows schematic diagrams illustrating the intersection of properties of longitudinal and transverse wave velocities, λρ and μρ, ρf and μρ (c = 2.333), and Fρ and Eρ according to the present invention.

[0043] Figure 5 a, Figure 5 b and Figure 5 c shows a schematic diagram of a near-angle (1-15°) superimposed profile, a mid-angle (13-23°) superimposed profile, and a far-angle (21-37°) superimposed profile according to Embodiment 2 of the present invention.

[0044] Figure 6 A schematic diagram of a seismic wavelet according to Embodiment 2 of the present invention is shown.

[0045] Figure 7 A schematic diagram of the generalized linear inversion result according to Embodiment 2 of the present invention is shown. Detailed Implementation

[0046] 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.

[0047] like Figure 1 As shown, a generalized linear inversion method according to the present invention includes:

[0048] Construct fluid indicator factors;

[0049] Constructing generalized linear equations based on fluid indicator factors;

[0050] Generalized linear inversion is performed based on generalized linear equations.

[0051] Specifically, this invention constructs a new fluid indicator factor and then a generalized linear equation, which is then used to perform generalized linear inversion for fluid prediction. The newly constructed fluid indicator factor has a wider range of applicability, enabling fluid prediction of complex reservoirs and improving the accuracy of fluid prediction and the effectiveness of fluid identification.

[0052] In one example, the generalized construction fluid indicator is:

[0053] Fρ=2σλρ;

[0054] Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

[0055] Specifically, in isotropic media, the longitudinal wave velocity V P Shear wave velocity V S The relationship between the Lamé constants λ and μ is as follows:

[0056]

[0057] Using Z P =ρV P Z represents the longitudinal wave impedance. S =ρV S The transverse wave impedance, based on the relationship between P-wave and S-wave velocities, density, and rock elastic parameters, can be represented by a combination of P-wave and S-wave impedances. A representative example is the fluid indicator ρf proposed by Russell et al. (2003), which is expressed as:

[0058]

[0059] In the formula, the adjustment parameter c in ρf is the square of the ratio of P-wave to S-wave velocity in dry rock. When the adjustment parameter c equals 2, ρf is the fluid indicator factor λρ given by Goodway (2001):

[0060]

[0061] Analyzing equations (3) and (4) above, we can see that the fluid indicator factor can be expressed as the weighted difference between the squares of the longitudinal and transverse wave impedances. The transverse wave impedance mainly reflects the properties of the rock skeleton and is insensitive to fluids, while the longitudinal wave impedance is mainly determined by the rock skeleton and pore fluids. Therefore, changes in the lithology of the skeleton and the properties of the fluid will cause anomalies in the longitudinal wave impedance. The fluid indicator factor ρf can be understood as the difference between the longitudinal wave impedance Z... PThe contribution of the rock skeleton to the P-wave impedance is reduced, and the remaining contribution mainly comes from the influence of pore fluids. Therefore, the fluid factor ρf can be used to identify reservoir fluids.

[0062] However, in practical applications, the adjustment parameter of the fluid indicator factor ρf is difficult to determine. When a fixed adjustment parameter c is selected, for example, c = 2.333, the fluid indicator factor loses its advantage of fully describing fluid properties, causing the sensitivity of the fluid indicator factor to fluids to vary with different regional rocks. Therefore, it is particularly important to rationally select the adjustment parameter c. Equation (3) tells us that the fluid identification effect of ρf depends on The accuracy of characterizing the properties of the rock skeleton.

[0063] Sharma and Chopra (2012) proposed a lithological indicator factor Eρ, based on the relationship between Lamé constants λ and μ and Young's modulus E and Poisson's ratio σ:

[0064]

[0065] After transforming the above formula, multiply Young's modulus E by density ρ to calculate Eρ:

[0066]

[0067] Define a parameter a:

[0068]

[0069] The above formula can be further written as:

[0070] Eρ=aμρ (9)

[0071] Analyzing equations (1) and (2), the elastic parameter λ of rocks in nature is greater than zero, making the longitudinal wave velocity V... P It must be greater than the shear wave velocity V. S of The parameter a is greater than 2, therefore Eρ is more than 2. (i.e., μρ) enhances the lithological indication effect.

[0072] Considering that the accurate identification of fluids by the fluid factor proposed by Russell et al. (2003) depends on the selection of adjustment parameters, and that the P-wave velocity ratio of dry rocks is difficult to obtain accurately in practical work, and that the sensitivity of the fluid factor decreases when using the empirical value of 2.333, this paper draws on the idea of ​​constructing the fluid factor by Russell et al. (2003) and subtracts the lithological indicator factor Eρ given by Sharma et al. (2012), which characterizes the rock skeleton, from the P-wave impedance to obtain... Thus, a new fluid indicator factor Fρ was constructed:

[0073]

[0074] Where γ is the P-wave to S-wave velocity ratio. This fluid indicator factor is essentially still a weighted combination of the squares of the P-wave and S-wave impedances, and its adjustment parameter... As the P-wave and S-wave velocities of the formation change, the analytical fluid indicator factor Fρ can be further expressed as:

[0075] Fρ=2σλρ (11)

[0076] In the above formula, the fluid indicator factor Fρ is twice the product of Poisson's ratio σ and λρ. Since both σ and λρ are highly sensitive to fluids, theoretically Fρ is even more sensitive to fluids.

[0077] In one example, the generalized linear equation is:

[0078] [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b;

[0079] Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

[0080] Specifically, the exact Zoeppritz equation in its traditional form is shown below (Zoeppritz, 1919; Aki and Richards, 1980):

[0081]

[0082] In the above formula, V P1 V P2 V S1 V S2 ρ1 and ρ2 represent the longitudinal and transverse wave velocities and densities on both sides of the reflecting interface, respectively, and θ1 and θ2 represent the incident angle and transmission angle of the PP wave, respectively. and R represents the reflection angle and transmission angle of the PS converted wave, respectively. PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS These represent the transmission coefficients of the PP wave and the PS converted wave, respectively.

[0083] According to Snell's Law:

[0084]

[0085] Substituting formula (13) into formula (12) yields formula (14):

[0086]

[0087] Substituting equations (12) and (13) into equation (14), we obtain the following new form of the exact Zoeppritz equation, i.e., the generalized linear equation, which is expressed parametrically by bulk modulus Fρ, Eρ, and density ρ:

[0088] [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b.

[0089] In one example, the process of constructing the fluid indicator also includes:

[0090] Analysis of the sensitivity of fluid indicator factors to fluids based on rock physics data.

[0091] Specifically, fluid indicators can typically be used to predict hydrocarbon distribution. However, some fluid indicators are only applicable to specific reservoir types and not all reservoirs, so there is no universal fluid indicator. This section uses two rock assemblages to analyze the fluid identification performance of commonly used fluid indicators and newly constructed fluid indicators.

[0092] The velocity and density data of 25 groups of water-bearing sandstone, shale, and gas-bearing sandstone collected by Castagna and Smith (1994) represent a worldwide sample and include the three common types of gas-bearing sandstone anomalies. Sensitivity analysis of fluid and lithological indicators was performed using this data. Figure 2 a shows the results of the sensitivity analysis of the fluid indicator factor λρ. Figure 2 b shows the fluid indicator factor

[0093] Sensitivity analysis results when parameter c is set to a fixed value Figure 2 c shows the sensitivity analysis results of the fluid indicator factor Fρ of the present invention, and analyzes the fluid indicator factor of Russell (2003). It can be observed that the fluid indicator factor ρf is zero in dry rock conditions, while it is greater than zero in saturated rock conditions. However, in the fluid indicator factor curves shown in the figure above, when the adjustment parameter c is set to a fixed value, the ρf of gas-bearing sandstone exhibits negative values ​​and a relatively dispersed distribution on both sides of the zero value. Setting the adjustment parameter c to a fixed value reduces the applicability range of the fluid indicator factor. The newly constructed fluid indicator factor is more concentrated above zero, and the difference between gas-bearing sandstone and water-bearing sandstone and shale is also obvious. Therefore, the newly constructed fluid indicator factor has a wider range of applicability.

[0094] Figure 3 a shows the results of the sensitivity analysis of the lithological indicator factor μρ. Figure 3 b shows the sensitivity analysis results of the lithological indicator factor Eρ, from Figure 3 As can be seen, Eρ significantly improves the differentiation between gas-bearing shale and sandstone compared to μρ, providing a stronger indication of lithology. Using Castana and Smith (1994) petrophysical data, cross-plot analysis of fluid indicator factors shows that highly sensitive fluid indicator factors can clearly distinguish between gas and water. Cross-plot analysis was performed using fluid indicator factors and lithology indicator factors; the results are shown below. Figure 4 a- Figure 4 As shown in d, the intersection diagram of the longitudinal and transverse wave velocities can be seen from the figure. Figure 4 a) Intersection diagram of λρ and μρ ( Figure 4 b) Intersection of ρf and μρ (c = 2.333) Figure 4 c) and the intersection of Fρ and Eρ ( Figure 4 In d), gas-bearing sandstone can be well distinguished from water-bearing sandstone and shale. However, the cross plot of Fρ and Eρ shows that the distinction between gas-bearing sandstone and water-bearing sandstone and shale is even better, indicating that the fluid indicator factor Fρ is more effective in identifying fluids.

[0095] 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.

[0096] Example 1

[0097] This embodiment provides a generalized linear inversion method, including:

[0098] Construct fluid indicator factors;

[0099] Constructing generalized linear equations based on fluid indicator factors;

[0100] Generalized linear inversion is performed based on generalized linear equations.

[0101] The generalized construction fluid indicator factor is:

[0102] Fρ=2σλρ;

[0103] Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

[0104] The generalized linear equation is:

[0105] [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b;

[0106] Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

[0107] After constructing the fluid indicator factor, the following is also included:

[0108] Analysis of the sensitivity of fluid indicator factors to fluids based on rock physics data.

[0109] Example 2

[0110] This embodiment provides a generalized linear inversion method, including:

[0111] The first step is to construct fluid indicator factors;

[0112] In isotropic media, the longitudinal wave velocity V P Shear wave velocity V S The relationship between the Lamé constants λ and μ is as follows:

[0113]

[0114] Using Z P =ρV P Z represents the longitudinal wave impedance. S =ρV S The transverse wave impedance, based on the relationship between P-wave and S-wave velocities, density, and rock elastic parameters, can be represented by a combination of P-wave and S-wave impedances. A representative example is the fluid indicator ρf proposed by Russell et al. (2003), which is expressed as:

[0115]

[0116] In the formula, the adjustment parameter c in ρf is the square of the ratio of P-wave to S-wave velocity in dry rock. When the adjustment parameter c equals 2, ρf is the fluid indicator factor λρ given by Goodway (2001):

[0117]

[0118] Analyzing equations (3) and (4) above, we can see that the fluid indicator factor can be expressed as the weighted difference between the squares of the longitudinal and transverse wave impedances. The transverse wave impedance mainly reflects the properties of the rock skeleton and is not sensitive to fluids. The longitudinal wave impedance, however, is mainly determined by the rock skeleton and pore fluids; therefore, changes in the lithology of the skeleton and the properties of the fluids will cause anomalies in the longitudinal wave impedance. The fluid indicator factor ρf can be understood as the difference between the longitudinal wave impedance Z... P The contribution of the rock skeleton to the P-wave impedance is reduced, and the remaining contribution mainly comes from the influence of pore fluids. Therefore, the fluid factor ρf can be used to identify reservoir fluids.

[0119] However, in practical applications, the adjustment parameter of the fluid indicator factor ρf is difficult to determine. When a fixed adjustment parameter c is selected, for example, c = 2.333, the fluid indicator factor loses its advantage of fully describing fluid properties, causing the sensitivity of the fluid indicator factor to fluids to vary with different regional rocks. Therefore, it is particularly important to rationally select the adjustment parameter c. Equation (3) above tells us that the fluid identification effect of ρf depends on The accuracy of characterizing the properties of the rock skeleton.

[0120] Sharma and Chopra (2012) proposed a lithological indicator factor Eρ, based on the relationship between Lamé constants λ and μ and Young's modulus E and Poisson's ratio σ:

[0121]

[0122] After transforming equations (5) and (6) above, Eρ is calculated by multiplying Young's modulus E by density ρ.

[0123]

[0124] Define a parameter a:

[0125]

[0126] Equation (7) above can be further written as

[0127] Eρ=aμρ (9)

[0128] Analyzing equations (1) and (2) above, the elastic parameter λ of rocks in nature is greater than zero, making the longitudinal wave velocity V... P It must be greater than the shear wave velocity V. S of The parameter a is greater than 2, therefore Eρ is more than 2. (i.e., μρ) enhances the lithological indication effect.

[0129] Considering that the accurate identification of fluids by the fluid factor proposed by Russell et al. (2003) depends on the selection of adjustment parameters, and that the P-wave to S-wave velocity ratio of dry rocks is difficult to obtain accurately in practical work, and that the sensitivity of the fluid factor decreases when using the empirical value of 2.333, this invention draws on the idea of ​​constructing the fluid factor proposed by Russell et al. (2003), subtracting the lithological indicator factor Eρ given by Sharma et al. (2012) which characterizes the rock skeleton from the P-wave impedance, to obtain... Thus, a new fluid indicator factor Fρ was constructed:

[0130]

[0131] Where γ is the P-wave to S-wave velocity ratio. This fluid indicator factor is essentially still a weighted combination of the squares of the P-wave and S-wave impedances, and its adjustment parameter... As the P-wave and S-wave velocities of the formation change, the analytical fluid indicator factor Fρ can be further expressed as:

[0132] Fρ=2σλρ (11)

[0133] In equation (11) above, the fluid indicator factor Fρ is twice the product of Poisson's ratio σ and λρ. Since both σ and λρ are highly sensitive to fluids, theoretically Fρ is even more sensitive to fluids.

[0134] The second step is sensitivity analysis of fluid indicator factors;

[0135] Fluid indicators can typically be used to predict hydrocarbon distribution. However, some fluid indicators are only applicable to specific reservoir types and not all reservoirs, so there is no universal fluid indicator. This section uses two rock assemblages to analyze the fluid identification performance of commonly used fluid indicators and newly constructed fluid indicators.

[0136] The velocity and density data of 25 groups of water-bearing sandstone, shale, and gas-bearing sandstone collected by Castagna and Smith (1994) represent a worldwide sample and include the three common types of gas-bearing sandstone anomalies. Sensitivity analysis of fluid and lithological indicators was performed using this data.

[0137] Figure 2 a shows the results of the sensitivity analysis of the fluid indicator factor λρ. Figure 2 b shows the fluid indicator factor Sensitivity analysis results when parameter c is set to a fixed value Figure 2c shows the sensitivity analysis results of the fluid indicator factor Fρ of the present invention, and analyzes the fluid indicator factor of Russell (2003). It can be observed that the fluid indicator factor ρf is zero in dry rock conditions, while it is greater than zero in saturated rock conditions. However, in the fluid indicator factor curves shown in the figure above, when the adjustment parameter c is set to a fixed value, the ρf of gas-bearing sandstone exhibits negative values ​​and a relatively dispersed distribution on both sides of the zero value. Setting the adjustment parameter c to a fixed value reduces the applicability range of the fluid indicator factor. The newly constructed fluid indicator factor is more concentrated above zero, and the difference between gas-bearing sandstone and water-bearing sandstone and shale is also obvious. Therefore, the newly constructed fluid indicator factor has a wider range of applicability.

[0138] Figure 3 a shows the results of the sensitivity analysis of the lithological indicator factor μρ. Figure 3 b shows the sensitivity analysis results of the lithological indicator factor Eρ, from Figure 3 As can be seen, Eρ significantly improves the differentiation between gas-bearing shale and sandstone compared to μρ, providing a stronger indication of lithology. Using Castana and Smith (1994) petrophysical data, cross-plot analysis of fluid indicator factors shows that highly sensitive fluid indicator factors can clearly distinguish between gas and water. Cross-plot analysis was performed using fluid indicator factors and lithology indicator factors; the results are shown below. Figure 4 a- Figure 4 As shown in d, the intersection diagram of the longitudinal and transverse wave velocities can be seen from the figure. Figure 4 a) Intersection diagram of λρ and μρ ( Figure 4 b) Intersection of ρf and μρ (c = 2.333) Figure 4 c) and the intersection of Fρ and Eρ ( Figure 4 In d), gas-bearing sandstone can be well distinguished from water-bearing sandstone and shale. However, the cross plot of Fρ and Eρ shows that the distinction between gas-bearing sandstone and water-bearing sandstone and shale is even better, indicating that the fluid indicator factor Fρ is more effective in identifying fluids.

[0139] The third step is to derive the generalized linear equation based on the fluid indicator factor;

[0140] The exact Zoeppritz equation in its traditional form is shown below (Zoeppritz, 1919; Aki and Richards, 1980):

[0141]

[0142] In the above formula, V P1 V P2 V S1 V S2ρ1 and ρ2 represent the longitudinal and transverse wave velocities and densities on both sides of the reflecting interface, respectively, and θ1 and θ2 represent the incident angle and transmission angle of the PP wave, respectively. and R represents the reflection angle and transmission angle of the PS converted wave, respectively. PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS These represent the transmission coefficients of the PP wave and the PS converted wave, respectively.

[0143] According to Snell's Law:

[0144]

[0145] Substituting formula (13) into formula (12) yields formula (14):

[0146]

[0147] Substituting equations (12) and (13) into equation (14), we obtain the following new form of the exact Zoeppritz equation, i.e., the generalized linear equation, which is expressed parametrically by bulk modulus Fρ, Eρ, and density ρ:

[0148] [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b;

[0149] Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

[0150] The fourth step is to achieve fluid prediction based on generalized linear equations.

[0151] The actual test data in this embodiment comes from a shale exploration area in China. Figure 5 The test area consists of three superimposed profiles. Figure 5 a is a near-angle (1-15°) superimposed profile. Figure 5 b is a superimposed profile at a mid-angle (13-23°). Figure 5 c represents the superimposed profile at a far angle (21-37°). Figure 5The red line marks the location of the well. The corner gather data actually collected in this area has undergone a series of conventional processing steps to meet the requirements of pre-stack AVA inversion. Generalized linear inversion is performed using the generalized linear inversion method of this embodiment. Figure 6 It is a seismic wavelet extracted based on partially superimposed data. Figure 7 This is the actual inversion result of the parameters. (Through...) Figure 7 As can be seen, the parameter results predicted by the generalized linear inversion method in this embodiment can clearly characterize the target reservoir, fully verifying its feasibility and effectiveness.

[0152] Example 3

[0153] This embodiment provides a generalized linear inversion apparatus, including:

[0154] The first building block is used to construct the fluid indicator factor;

[0155] The second building module is used to construct generalized linear equations based on fluid indicator factors;

[0156] The prediction module is used to perform generalized linear inversion based on generalized linear equations.

[0157] The generalized construction fluid indicator factor is:

[0158] Fρ=2σλρ;

[0159] Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

[0160] The generalized linear equation is:

[0161] [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b;

[0162] Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

[0163] After constructing the fluid indicator factor, the following is also included:

[0164] Analysis of the sensitivity of fluid indicator factors to fluids based on rock physics data.

[0165] Example 4

[0166] This disclosure also provides an electronic device, which includes:

[0167] At least one processor; and,

[0168] A memory communicatively connected to the at least one processor; wherein,

[0169] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the generalized linear inversion method in Embodiment 1.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0174] Example 5

[0175] This disclosure provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the generalized linear inversion method in Embodiment 1.

[0176] 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.

[0177] 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).

[0178] 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 generalized linear inversion method, characterized in that, include: Construct fluid indicator factors; A generalized linear equation is constructed based on the fluid indicator factors; Generalized linear inversion is performed based on the generalized linear equation.

2. The generalized linear inversion method according to claim 1, characterized in that, The generalized construction fluid indicator factor is: Fρ=2σλρ; Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

3. The generalized linear inversion method according to claim 1, characterized in that, The generalized linear equation is: [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b; Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

4. The generalized linear inversion method according to claim 1, characterized in that, Following the construction of the fluid indicator factor, the following is also included: The sensitivity of the fluid indicator factors to fluids was analyzed based on rock physics data.

5. 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 generalized linear inversion method according to any one of claims 1-4.

6. 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 generalized linear inversion method as described in any one of claims 1-4.

7. A generalized linear inversion device, characterized in that, include: The first building block is used to construct the fluid indicator factor; The second construction module is used to construct a generalized linear equation based on the fluid indicator factor; The prediction module is used to perform generalized linear inversion based on the generalized linear equation.

8. The generalized linear inversion apparatus according to claim 1, characterized in that, The generalized construction fluid indicator factor is: Fρ=2σλρ; Where σ is Poisson's ratio, λ is Lamé's constant, and ρ is density.

9. The generalized linear inversion apparatus according to claim 1, characterized in that, The generalized linear equation is: [A1 A2 A3 A4][R PP R PS T PP T PS ] T =b; Among them, R PP and R PS T represents the reflection coefficients of the PP wave and the PS converted wave, respectively. PP and T PS A1, A2, A3, and A4 represent the transmission coefficients of the PP wave and the PS converted wave, respectively. A1, A2, A3, and A4 are the coefficient matrices of the fluid indicator factor, and B is the correlation coefficient matrix.

10. The generalized linear inversion apparatus according to claim 1, characterized in that, Following the construction of the fluid indicator factor, the following is also included: The sensitivity of the fluid indicator factors to fluids was analyzed based on rock physics data.

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