Nonlinear inversion method and system for shale content, porosity and fluid modulus

Abstract

The present invention relates to a nonlinear inversion method and system for mud content, porosity and fluid modulus, the method comprising the following steps: directly characterizing the reflection coefficient and elastic impedance expression with mud content, porosity and fluid modulus; predicting the elastic impedance data volume using prestack seismic data, and then performing nonlinear inversion to predict the mud content, porosity and fluid modulus according to the elastic impedance expression. Compared with the prior art, the present invention not only directly characterizes the influence of mud content, porosity and fluid modulus on seismic wave reflection characteristics, but also can estimate elastic impedance using seismic data, and then perform elastic impedance nonlinear inversion to directly estimate the mud content, porosity and fluid characteristics of the reservoir, providing method support for more reliable prediction of mud content, porosity and fluid type of oil and gas reservoirs.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir parameter inversion, and in particular to a nonlinear inversion method and system for mud content, porosity and fluid modulus. Background Art

[0002] Reliable prediction of reservoir parameters (shale content, porosity) and fluid types is crucial for the estimation of oil and gas reserves. Through geophysical inversion, elastic parameters for identifying oil and gas reservoir characteristics can be extracted from seismic amplitude data, and the extracted elastic parameters can be converted into reservoir parameters and fluid type indicator factors by combining rock physics theory and equivalent models. However, the research on direct inversion of reservoir parameters and fluid factors using seismic data is still immature.

[0003] The commonly used fluid factors are mainly based on the difference in bulk modulus of fluids (water: 2.865GPa; gas: 0.041GPa). The Gassmann fluid substitution model can be used to analyze the effects of different types of fluids and their contents on rock modulus (bulk modulus, shear modulus, etc.), elastic parameters (P-wave velocity, S-wave velocity, Lame parameter, Poisson's ratio, etc.) and density. Geophysicists have proposed a series of fluid indicator factors by combining various parameters related to fluid modulus. However, regardless of the fluid indicator factors formed by the combination of elastic parameters, they are all based on the difference in bulk modulus between gas and water, but are indeed affected by the combined influence of lithology, fluid and pores, which reduces the sensitivity to fluids to a certain extent.

[0004] Parameterization of seismic reflection coefficient is the basis of prestack seismic inversion of oil and gas reservoirs. The linearized reflection coefficient expression expressed by P-wave velocity and density reflectivity proposed by Aki and Richards (1980) has greatly promoted the development of prestack seismic amplitude inversion. However, the current research on the relationship between reservoir parameters (porosity and shale content) and reflection coefficient is mainly based on rock physics models, which indirectly simulates the influence of reservoir parameters on seismic reflection characteristics. Gui Jinyong et al. (2015) studied the relationship between rock elastic parameters and reservoir parameters (shale content, porosity and water saturation) by combining the mineral equivalent modulus calculation model, dry rock modulus calculation model and fluid substitution model, and analyzed the influence of reservoir parameters on seismic reflection characteristics based on the Zoeppritz equation. It can be seen that the current research on the characterization of reflection coefficient by reservoir parameters is mainly achieved by indirect means, and a seismic reflection coefficient expression directly characterized by reservoir parameters (shale content, porosity) and fluid factors has not yet been formed, which to a certain extent limits the development of predicting reservoir parameters using prestack seismic reflection amplitude.

[0005] Based on the theory and characteristic research of the change of reflection amplitude versus offset / angle (AVO / AVA), geophysicists have carried out a large number of inversion methods for predicting elastic parameters, physical parameters and fluid factors of oil and gas reservoirs using prestack seismic data. Analogous to acoustic impedance (AI), Connolly (1999) proposed the concept of elastic impedance (EI) based on the reflection coefficient approximation of Aki and Richards (1980). By rewriting the EI expression, geophysicists proposed a strategy of first predicting EI at different angles using partial angle stacking gathers, and then extracting the corresponding reservoir elastic parameters from the inverted EI data body. Among them, the process of extracting elastic parameters using the inverted EI data body requires calculating the logarithmic elastic impedance and establishing the linear relationship between the logarithmic elastic impedance and the logarithmic elastic parameters, so as to carry out the linear inversion of the elastic parameters. Whether it is the two-step inversion strategy based on elastic impedance or the AVO direct inversion strategy, the theoretical basis is the first-order linear approximation of the seismic wave reflection coefficient, and the accuracy of the unknown parameters depends largely on the contribution of different unknown parameters to the change of reflection coefficient with angle. At the same time, the inversion based on the first-order linear approximation of the seismic reflection coefficient depends on the first-order derivative of the reflection coefficient with respect to the unknown parameter variable (that is, the coefficient related to the angle before the reflectivity of each parameter). The correlation between the unknown parameters and the coupling of the contribution to the angle of the reflection coefficient can easily cause uncertainty in the inversion problem and reduce the accuracy of the inversion. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a nonlinear inversion method and system for mud content, porosity and fluid modulus.

[0007] The present invention will be based on rock physics theory and equivalent models. First, elastic impedance (EI) characterized by reservoir parameters (shale content, porosity) and fluid factors (fluid modulus) will be established; secondly, a method of predicting EI by using partial angle stacking gathers will be studied, and then EI nonlinear inversion will be carried out to predict unknown parameter variables (shale content, porosity, fluid factor, etc.).

[0008] This invention not only establishes the direct relationship between reservoir shale content, porosity and fluid modulus and seismic wave reflection coefficient, but also forms a method for linear inversion of EI using prestack seismic data and nonlinear inversion of shale content, porosity and fluid modulus using EI. The method has good noise resistance and can reliably predict the shale content, porosity and fluid modulus of the reservoir, which is of great significance for the fine characterization of oil and gas reservoirs.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A nonlinear inversion method for shale content, porosity and fluid modulus comprises the following steps:

[0011] The reflection coefficient and elastic impedance expression are directly characterized by mud content, porosity and fluid modulus;

[0012] Prestack seismic data are used to predict elastic impedance data, and then nonlinear inversion is performed based on the elastic impedance expression to predict shale content, porosity and fluid modulus.

[0013] Furthermore, the characterization process of the reflection coefficient and elastic impedance is specifically as follows:

[0014] Based on Gassmann fluid replacement formula, the bulk modulus and shear modulus of saturated rock are characterized by using the relationship between dry rock and porosity, rock mineral matrix and shale content;

[0015] Based on the bulk modulus and shear modulus of saturated rocks, the seismic wave reflection coefficient characterized by shale content, porosity, density and bulk modulus is established;

[0016] Based on the seismic wave reflection coefficient, an elastic impedance expression is established.

[0017] Furthermore, the expressions of reflection coefficient and elastic impedance are applicable to sandstone and mudstone reservoirs;

[0018] Bulk modulus K of saturated rock sat and shear modulus μ sat Characterized by:

[0019]

[0020]

[0021] Where K0 and μ0 are the equivalent volume and shear moduli of the mineral matrix that makes up the rock, respectively, and φ and K f are the porosity of the rock and the bulk modulus of the fluid filling the pores, respectively;

[0022] According to the rock mineral composition of the sandstone reservoir, the mineral matrix equivalent modulus K0 and μ0 are expressed as the mud content V c Function:

[0023]

[0024]

[0025] In the formula, K q and μ qis the bulk modulus and shear modulus of quartz, K c and μ c are the bulk modulus and shear modulus of muddy particles;

[0026] The expression of the seismic wave reflection coefficient is:

[0027]

[0028] Where θ is the incident angle, g dry and g sat are the ratios of transverse and longitudinal wave moduli of dry rock and saturated rock, respectively;

[0029] The elastic impedance expression is:

[0030] Where EI(θ) is the elastic impedance of the sandstone reservoir at the incident angle θ.

[0031] Furthermore, the elastic impedance data volume is predicted using pre-stack seismic data as follows:

[0032] Using pre-stack seismic data to obtain partial angle stacking data volumes, the least squares linear inversion based on model constraints was performed to obtain elastic impedance data volumes;

[0033] The solution equation of the elastic impedance data volume is:

[0034] x i+1 =x i +(A T A+εI) -1 A T (B-Ax i )

[0035] Where x is the elastic impedance matrix to be inverted, I is the unit diagonal matrix, ε is the damping factor related to the signal-to-noise ratio, B is the input partial angle stack data volume, A is the wavelet matrix constructed by the wavelet extracted from the partial angle stack data, i=0:N represents the number of iterations, and x0 is the initial elastic impedance model established using the logging results.

[0036] Furthermore, the inversion prediction process of the shale content, porosity and fluid modulus includes:

[0037] The obtained elastic impedance data body is used for nonlinear inversion to predict the variable m composed of unknown parameters. The solution process of the variable m composed of unknown parameters is as follows:

[0038] Set up the equation:

[0039] m mod=m0+Δm

[0040] In the formula, m0 is the initial estimated value of m, m mod is the model value of a given unknown parameter variable, Δm is the disturbance based on the initial value, and the calculation expression of Δm is:

[0041] Δm=-H -1 g

[0042] in,

[0043]

[0044] H=diag(g 2 )

[0045] In the formula, diag[] represents g 2 The elements are placed on the main diagonal, and Δd is the use of m mod The resulting elastic impedance results are

[0046]

[0047] in,

[0048]

[0049]

[0050]

[0051]

[0052] According to y1(V c )、y2(V c ), f1(φ), f2(φ), we can get and Numeric value;

[0053] Δm can be obtained by given initial estimate value m0, and after multiple iterations, the model value m of variable m composed of unknown parameters can be obtained. mod The inversion results.

[0054] The present invention also provides a nonlinear inversion system for shale content, porosity and fluid modulus, comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to perform the following steps:

[0055] The reflection coefficient and elastic impedance expression are directly characterized by mud content, porosity and fluid modulus;

[0056] Prestack seismic data are used to predict elastic impedance data, and then nonlinear inversion is performed based on the elastic impedance expression to predict shale content, porosity and fluid modulus.

[0057] Furthermore, the characterization process of the reflection coefficient and elastic impedance is specifically as follows:

[0058] Based on Gassmann fluid replacement formula, the bulk modulus and shear modulus of saturated rock are characterized by using the relationship between dry rock and porosity, rock mineral matrix and shale content;

[0059] Based on the bulk modulus and shear modulus of saturated rocks, the seismic wave reflection coefficient characterized by shale content, porosity, density and bulk modulus is established;

[0060] Based on the seismic wave reflection coefficient, an elastic impedance expression is established.

[0061] Furthermore, the expressions of reflection coefficient and elastic impedance are applicable to sandstone and mudstone reservoirs;

[0062] Bulk modulus K of saturated rock sat and shear modulus μ sat Characterized by:

[0063]

[0064]

[0065] Where K0 and μ0 are the equivalent volume and shear moduli of the mineral matrix that makes up the rock, respectively, and φ and K f are the porosity of the rock and the bulk modulus of the fluid filling the pores, respectively;

[0066] According to the rock mineral composition of the sandstone reservoir, the mineral matrix equivalent modulus K0 and μ0 are expressed as the mud content V c Function:

[0067]

[0068]

[0069] In the formula, K q and μ q is the bulk modulus and shear modulus of quartz, K c and μ c are the bulk modulus and shear modulus of muddy particles;

[0070] The expression of the seismic wave reflection coefficient is:

[0071]

[0072] Where θ is the incident angle, g dry and g sat are the ratios of transverse and longitudinal wave moduli of dry rock and saturated rock, respectively;

[0073] The elastic impedance expression is:

[0074]

[0075] Where EI(θ) is the elastic impedance of the sandstone reservoir at the incident angle θ.

[0076] Furthermore, the elastic impedance data volume is predicted using pre-stack seismic data as follows:

[0077] Using pre-stack seismic data to obtain partial angle stacking data volumes, the least squares linear inversion based on model constraints was performed to obtain elastic impedance data volumes;

[0078] The solution equation of the elastic impedance data volume is:

[0079] x i+1 =x i +(A T A+εI) -1 A T (B-Ax i )

[0080] Where x is the elastic impedance matrix to be inverted, I is the unit diagonal matrix, ε is the damping factor related to the signal-to-noise ratio, B is the input partial angle stack data volume, A is the wavelet matrix constructed by the wavelet extracted from the partial angle stack data, i=0:N represents the number of iterations, and x0 is the initial elastic impedance model established using the logging results.

[0081] Furthermore, the inversion prediction process of the shale content, porosity and fluid modulus includes:

[0082] The obtained elastic impedance data body is used for nonlinear inversion to predict the variable m composed of unknown parameters. The solution process of the variable m composed of unknown parameters is as follows:

[0083] Set up the equation:

[0084] m mod =m0+Δm

[0085] In the formula, m0 is the initial estimated value of m, m mod is the model value of a given unknown parameter variable, Δm is the disturbance based on the initial value, and the calculation expression of Δm is:

[0086] Δm=-H -1g

[0087] in,

[0088]

[0089] H=diag(g 2 )

[0090] In the formula, diag[] represents g 2 The elements are placed on the main diagonal, and Δd is the use of m mod The resulting elastic impedance results are

[0091]

[0092] in,

[0093]

[0094]

[0095]

[0096]

[0097] According to y1(V c )、y2(V c ), f1(φ), f2(φ), we can get and Numeric value;

[0098] Δm can be obtained by given initial estimate value m0, and after multiple iterations, the model value m of variable m composed of unknown parameters can be obtained. mod The inversion results.

[0099] Compared with the prior art, the present invention has the following advantages:

[0100] Firstly, the explicit mapping relationship between the shale content, porosity and fluid modulus and the seismic wave reflection coefficient and elastic impedance is directly established, which lays a theoretical foundation for directly simulating the influence of reservoir parameters on seismic wave reflection characteristics.

[0101] Secondly, a method for linear inversion of elastic impedance using prestack seismic data and nonlinear inversion of shale content, porosity and fluid modulus using elastic impedance was established. The second-order derivative of elastic impedance with respect to shale content, porosity and fluid modulus was used to improve the accuracy of parameter inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 A schematic flow chart of a nonlinear inversion method for shale content, porosity and fluid modulus provided in an embodiment of the present invention;

[0103] Figure 2 A comparison diagram of an inversion value obtained by applying the method of the present invention to noisy synthetic data and a true value of the model provided in an embodiment of the present invention;

[0104] Figure 3a A shale content inversion result diagram obtained by applying the method of the present invention to actual seismic data inversion provided in an embodiment of the present invention;

[0105] Figure 3b A porosity inversion result diagram obtained by applying the method of the present invention to actual seismic data inversion provided in an embodiment of the present invention;

[0106] Figure 3c A fluid modulus inversion result diagram obtained by applying the method of the present invention to actual seismic data inversion provided in an embodiment of the present invention;

[0107] Figure 3d The present invention provides a density inversion result diagram obtained by applying the method of the present invention to the inversion of actual seismic data. DETAILED DESCRIPTION

[0108] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0109] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0110] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0111] Example 1

[0112] This embodiment provides a nonlinear inversion method for shale content, porosity and fluid modulus, including the following steps:

[0113] S1: Directly characterize the reflection coefficient and elastic impedance expression by mud content, porosity and fluid modulus;

[0114] S2: Use pre-stack seismic data to predict elastic impedance data volumes, and then perform nonlinear inversion to predict shale content, porosity, and fluid modulus based on the elastic impedance expression.

[0115] The following is combined with Figure 1 The methods and basic principles involved in the specific implementation of the present invention are further explained.

[0116] S1: The reflection coefficient and elastic impedance expressions directly characterized by shale content, porosity and fluid modulus are derived. The specific calculation is:

[0117] Based on Gassmann fluid substitution formula, using the relationship between dry rock and porosity, the bulk modulus K of saturated rock can be sat and shear modulus μ sat Characterized by:

[0118]

[0119] where K0 and μ0 are the equivalent volume and shear moduli of the mineral matrix that makes up the rock, respectively, and φ and K f are the porosity of rock and the bulk modulus of the fluid filling the pores, respectively.

[0120] For sandstone reservoirs, the rock mineral components are mainly quartz and mud particles. The mineral matrix equivalent modulus K0 and μ0 can be expressed as the mud content V c Function:

[0121]

[0122] Among them, K q and μ q is the bulk modulus and shear modulus of quartz, K c and μ c are the bulk modulus and shear modulus of the argillaceous particles, and the bulk modulus of the mineral is assumed to be constant.

[0123] Based on the established saturated rock bulk modulus K sat and shear modulus μ sat The expression can be used to derive the seismic wave reflection coefficient characterized by shale content, porosity, density and bulk modulus:

[0124]

[0125] Where θ is the incident angle, g dry and g sat are the ratios of the transverse and longitudinal wave moduli of dry rock and saturated rock, respectively.

[0126] Based on the derived reflection coefficient characterized by shale content, porosity, fluid bulk modulus and density, the EI (elastic impedance) formula applicable to sandstone reservoirs is proposed:

[0127]

[0128] Using pre-stack seismic reflection amplitude, direct prediction of reservoir porosity, fluid modulus, density and shale content can be carried out based on the derived reflection coefficient and EI formula.

[0129] S2: The method of using elastic impedance for nonlinear inversion to predict shale content, porosity and fluid modulus is as follows:

[0130] Based on the above PP wave reflection coefficient and EI formula, a two-step inversion is carried out to predict the EI data volume through linear inversion of seismic gathers and the reservoir porosity, fluid modulus, density and shale content through nonlinear inversion of EI data volume. The EI data volume can be obtained by carrying out the least squares linear inversion based on model constraints using the partial angle stacking data volume. The solution equation is:

[0131] x i+1 =x i +(A T A+εI) -1 A T (B-Ax i ), (5)

[0132] Among them, x is the EI matrix to be inverted, I is the unit diagonal matrix, ε is the damping factor related to the signal-to-noise ratio, B is the input partial angle stacking data volume, A is the wavelet matrix constructed by the wavelet extracted from the partial angle stacking data, i=0:N represents the number of iterations, and x0 is the EI initial model established using the logging results. After a certain number of iterations, a better EI inversion result can be obtained.

[0133] Next, we will introduce how to use the EI inversion results to carry out nonlinear inversion of porosity, shale content, fluid modulus and density. From the derived EI formula, we can see that EI and shale content V c , porosity φ, fluid modulus K f There is a nonlinear relationship between the density ρ. By conducting research on nonlinear inversion methods, it is possible to use the EI data volume variable d to predict the unknown parameters (V c ,φ,K f and ρ). The solution of unknown parameter variables is:

[0134] m mod =m0+Δm, (6)

[0135] Where m0 is the initial estimated value of m, and Δm is the perturbation based on the initial value.

[0136] Δm=-H -1 g, (7)

[0137] in

[0138]

[0139] H=diag(g 2 ), (8)

[0140] In the formula, diag[] represents g 2 Elements are placed on the main diagonal, m mod is the model value of a given unknown parameter variable, Δd is the model value of the unknown parameter variable using m mod The EI results generated, and

[0141]

[0142] in

[0143]

[0144]

[0145]

[0146]

[0147] According to y1(V c )、y2(V c ), f1(φ), f2(φ), we can get and Numeric value.

[0148] In summary, Δm can be obtained by giving an initial estimated value m0, and the inversion result of the unknown parameter variable can be obtained after multiple iterations.

[0149] This embodiment also provides a nonlinear inversion system for mud content, porosity and fluid modulus, including a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the nonlinear inversion method for mud content, porosity and fluid modulus as described above.

[0150] Experimental Results

[0151] In order to verify the reliability of the present invention in predicting reservoir shale content, porosity and fluid modulus, the present invention is applied to synthetic seismic data and pre-stack seismic data for processing and analysis. Figure 2The comparison between the actual value of the model and the predicted value obtained by inversion when the present invention is applied to synthetic seismic data containing random noise (signal-to-noise ratio is 2). It can be seen that even when the signal-to-noise ratio is 2, the inversion results of porosity, shale content, and fluid modulus obtained by the present invention are in good agreement with the actual value of the model. Figure 3a-3d In order to apply the invention to the inversion results of shale content, porosity, fluid modulus and density obtained by prestack seismic data, the corresponding logging interpretation results are added to the figure. It can be seen that the inversion results of shale content, porosity, fluid modulus and density obtained by the invention match well with the logging interpretation results. Figure 2 , Figure 3a-3d The data processing effect verifies the feasibility and effectiveness of the inversion method in this patent to predict reservoir mud content, porosity, and fluid modulus, and has great application potential.

[0152] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A nonlinear inversion method for shale content, porosity and fluid modulus, characterized in that: The following steps are involved: The reflection coefficient and elastic impedance expression are directly characterized by mud content, porosity and fluid modulus; Pre-stack seismic data are used to predict elastic impedance data volumes, and then nonlinear inversion is performed based on the elastic impedance expression to predict shale content, porosity and fluid modulus. The characterization process of the reflection coefficient and elastic impedance is specifically as follows: Based on Gassmann fluid replacement formula, the bulk modulus and shear modulus of saturated rock are characterized by using the relationship between dry rock and porosity, rock mineral matrix and shale content; Based on the bulk modulus and shear modulus of saturated rocks, the seismic wave reflection coefficient characterized by shale content, porosity, density and bulk modulus is established; Based on the seismic wave reflection coefficient, an elastic impedance expression is established; The expressions of reflection coefficient and elastic impedance are applicable to sandstone and mudstone reservoirs; Bulk modulus K of saturated rock sat and shear modulus μ sat Characterized by: Where K0 and μ0 are the equivalent volume and shear moduli of the mineral matrix that makes up the rock, respectively, and φ and K f are the porosity of the rock and the bulk modulus of the fluid filling the pores, respectively; According to the rock mineral composition of the sandstone reservoir, the mineral matrix equivalent modulus K0 and μ0 are expressed as the mud content V c Function: In the formula, K q and μ q is the bulk modulus and shear modulus of quartz, K c and μ c are the bulk modulus and shear modulus of muddy particles; The expression of the seismic wave reflection coefficient is: Where θ is the incident angle, and g sat are the ratios of transverse and longitudinal wave moduli of dry rock and saturated rock, respectively; The elastic impedance expression is: Where, EI(θ) is the elastic impedance of the sandstone reservoir at the incident angle θ; The elastic impedance data volume predicted by prestack seismic data is as follows: Using pre-stack seismic data to obtain partial angle stacking data volumes, the least squares linear inversion based on model constraints was performed to obtain elastic impedance data volumes; The solution equation of the elastic impedance data volume is: x i+1 =x i +(A T A+εI) -1 A T (B-Ax i ) Where x is the elastic impedance matrix to be inverted, I is the unit diagonal matrix, ε is the damping factor related to the signal-to-noise ratio, B is the input partial angle stacking data volume, A is the wavelet matrix constructed by the wavelet extracted from the partial angle stacking data, i=0:N represents the number of iterations, and x0 is the initial elastic impedance model established using the logging results; The inversion prediction process of the shale content, porosity and fluid modulus includes: The obtained elastic impedance data body is used for nonlinear inversion to predict the variable m composed of unknown parameters. The solution process of the variable m composed of unknown parameters is as follows: Set up the equation: m mod =m0+Δm In the formula, m0 is the initial estimated value of m, m mod is the model value of a given unknown parameter variable, Δm is the disturbance based on the initial value, and the calculation expression of Δm is: Δm=-H -1 g in, H=diag(g 2 ) In the formula, diag[] represents g 2 The elements are placed on the main diagonal, and Δd is the use of m mod The resulting elastic impedance results are in, According to y1(V c )、y2(V c ), f1(φ), f2(φ), we can get and Numeric value; Δm can be obtained by given initial estimate m0, and after multiple iterations, the model value m of variable m composed of unknown parameters can be obtained. mod The inversion results.

2. A nonlinear inversion system for shale content, porosity and fluid modulus, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to perform the following steps: The reflection coefficient and elastic impedance expression are directly characterized by mud content, porosity and fluid modulus; Pre-stack seismic data are used to predict elastic impedance data volumes, and then nonlinear inversion is performed based on the elastic impedance expression to predict shale content, porosity and fluid modulus. The characterization process of the reflection coefficient and elastic impedance is specifically as follows: Based on Gassmann fluid replacement formula, the bulk modulus and shear modulus of saturated rock are characterized by using the relationship between dry rock and porosity, rock mineral matrix and shale content; Based on the bulk modulus and shear modulus of saturated rocks, the seismic wave reflection coefficient characterized by shale content, porosity, density and bulk modulus is established; Based on the seismic wave reflection coefficient, an elastic impedance expression is established; The expressions of reflection coefficient and elastic impedance are applicable to sandstone and mudstone reservoirs; Bulk modulus K of saturated rock sat and shear modulus μ sat Characterized by: Where K0 and μ0 are the equivalent volume and shear moduli of the mineral matrix that makes up the rock, respectively, and φ and K f are the porosity of the rock and the bulk modulus of the fluid filling the pores, respectively; According to the rock mineral composition of the sandstone reservoir, the mineral matrix equivalent modulus K0 and μ0 are expressed as the mud content V c Function: In the formula, K q and μ q is the bulk modulus and shear modulus of quartz, K c and μ c are the bulk modulus and shear modulus of muddy particles; The expression of the seismic wave reflection coefficient is: Where θ is the incident angle, and g sat are the ratios of transverse and longitudinal wave moduli of dry rock and saturated rock, respectively; The elastic impedance expression is: Where, EI(θ) is the elastic impedance of the sandstone reservoir at the incident angle θ; The elastic impedance data volume predicted by prestack seismic data is as follows: Using pre-stack seismic data to obtain partial angle stacking data volumes, the least squares linear inversion based on model constraints was performed to obtain elastic impedance data volumes; The solution equation of the elastic impedance data volume is: x i+1 =x i +(A T A+εI) -1 A T (B-Ax i ) Where x is the elastic impedance matrix to be inverted, I is the unit diagonal matrix, ε is the damping factor related to the signal-to-noise ratio, B is the input partial angle stacking data volume, A is the wavelet matrix constructed by the wavelet extracted from the partial angle stacking data, i=0:N represents the number of iterations, and x0 is the initial elastic impedance model established using the logging results; The inversion prediction process of the shale content, porosity and fluid modulus includes: The obtained elastic impedance data body is used for nonlinear inversion to predict the variable m composed of unknown parameters. The solution process of the variable m composed of unknown parameters is as follows: Set up the equation: m mod =m0+Δm In the formula, m0 is the initial estimated value of m, m mod is the model value of a given unknown parameter variable, Δm is the disturbance based on the initial value, and the calculation expression of Δm is: Δm=-H -1 g in, H=diag(g 2 ) In the formula, diag[] represents g 2 The elements are placed on the main diagonal, and Δd is the use of m mod The resulting elastic impedance results are in, According to y1(V c )、y2(V c ), f1(φ), f2(φ), we can get and Numeric value; Δm can be obtained by given initial estimate m0, and after multiple iterations, the model value m of variable m composed of unknown parameters can be obtained. mod The inversion results.

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