A reservoir inversion method, device, storage medium and electronic device

Abstract

The present application relates to the technical field of geophysics, in particular to a reservoir inversion method, device, storage medium and electronic equipment, wherein the method comprises: constructing a two-term AVO reflection characteristic model, the two-term AVO reflection characteristic model comprising a fluid bulk modulus and a solid rigidity parameter; calculating the fluid bulk modulus and the solid rigidity parameter based on parameters of a target area; performing elastic impedance inversion on two partial angle stacked seismic data and well logging curves to obtain elastic impedance inversion result data bodies of two angles; constructing a two-term elastic impedance model based on the two-term AVO reflection characteristic model, and combining the elastic impedance inversion result data bodies of the two angles to perform direct prestack seismic inversion on a deep reservoir sensitive fluid factor. The sensitive fluid factor obtained by the present application is only related to the elastic effect of pore fluid, and the accuracy of reservoir fluid identification can be significantly improved.

Description

Technical Field

[0001] This invention relates to the field of geophysical technology, and in particular to a method, apparatus, storage medium, and electronic device for direct inversion of sensitive fluid factors based on deep reservoirs. Background Technology

[0002] Deep reservoirs are the main area for increasing reserves and production in oilfield exploration and development. However, most exploration areas face technical problems such as deep reservoir burial, weak signal, low resolution and narrow bandwidth of seismic data, difficulty in reservoir description and inaccurate fluid prediction. Summary of the Invention

[0003] This invention provides a reservoir inversion method, apparatus, storage medium, and electronic device, which solves the technical problems of insufficient fluid identification accuracy caused by low illumination, low signal-to-noise ratio, and lack of large-angle incident information in current deep seismic data.

[0004] In a first aspect, the present invention provides a reservoir inversion method, comprising:

[0005] A bipartite AVO reflection characteristic model is constructed, which includes fluid bulk modulus and solid rigidity parameters;

[0006] Calculate fluid bulk modulus and solid rigidity parameters based on parameters of the target region;

[0007] Elastic impedance inversion was performed on the seismic data and well logging curves superimposed from two partial angles to obtain the elastic impedance inversion result data volume from the two angles.

[0008] A two-dimensional elastic impedance model is constructed based on the two-dimensional AVO reflection characteristic model. The elastic impedance inversion results from two angles are combined to perform pre-stack seismic direct inversion of sensitive fluid factors in deep reservoirs.

[0009] In some embodiments, the two-term AVO reflectance feature model includes:

[0010]

[0011] Among them, R pp Where K is the reflection coefficient, θ is the incident angle, and K is the reflection coefficient. f f is the bulk modulus of the fluid. m For solid rigidity parameters, A and B are preset coefficients.

[0012] In some embodiments, in the bispecific AVO reflectance feature model, preset coefficients A and B are included, including:

[0013]

[0014]

[0015] Where, γ sat 2 γ is the square of the ratio of P-wave to S-wave velocity in saturated rock. dry 2 R is the square of the ratio of P-wave velocity to S-wave velocity in dry rock, θ is the incident angle, r1 is the fitting coefficient between the P-wave velocity reflection coefficient and the density reflection coefficient in the target area, and r2 is the fitting coefficient between the S-wave velocity reflection coefficient and the density reflection coefficient.

[0016] In some embodiments, the parameters of the target region include: transverse wave velocity, density, and porosity;

[0017] The steps for calculating the fluid bulk modulus and solid rigidity parameters based on the parameters of the target region include:

[0018] f m =φμ=φ·ρv s 2

[0019] Among them, f m Here are the parameters for a solid's rigidity, where φ is porosity, ρ is density, and v is... s The velocity is the transverse wave velocity.

[0020] In some embodiments, the parameters of the target region include: longitudinal wave velocity, transverse wave velocity, density, and porosity;

[0021] The steps for calculating the fluid bulk modulus and solid rigidity parameters based on the parameters of the target region include:

[0022]

[0023] Among them, K f v is the bulk modulus of the fluid. p Let φ be the longitudinal wave velocity. c For critical porosity, γ dry 2 It is the square of the ratio of longitudinal to transverse wave velocities in dry rock.

[0024] In some embodiments, the two-term elastic impedance model includes:

[0025] EI(θ)=K f a(θ) f m b(θ)

[0026] Among them, K f f is the bulk modulus of the fluid. m For solid rigidity parameters, α(θ) and b(θ) are preset coefficients.

[0027] In some embodiments, in the biphasic elastic impedance model, the expressions for the exponential terms a(θ) and b(θ) are:

[0028]

[0029]

[0030]

[0031] Where, γ sat 2 γ is the square of the ratio of P-wave to S-wave velocity in saturated rock. dry 2 θ is the square of the ratio of P-wave velocity to S-wave velocity in dry rock, θ is the incident angle, r1 is the fitting coefficient between the P-wave velocity reflection coefficient and the density reflection coefficient in the target area, r2 is the fitting coefficient between the S-wave velocity reflection coefficient and the density reflection coefficient, and r is a parameter obtained by fitting based on the relationship between the solid rigidity parameter and porosity.

[0032] In a second aspect, the present invention provides a reservoir inversion apparatus, comprising:

[0033] The building module is used to construct a bipartite AVO reflection characteristic model, which includes fluid bulk modulus and solid rigidity parameters;

[0034] The parameter acquisition module is used to calculate the fluid bulk modulus and solid rigidity parameters based on the parameters of the target area.

[0035] The elastic impedance inversion module is used to perform elastic impedance inversion on seismic data and well logging curves superimposed from two partial angles to obtain elastic impedance inversion result data volumes from the two angles.

[0036] The direct inversion module is used to construct a two-dimensional elastic impedance model based on the two-dimensional AVO reflection characteristic model, and combine the elastic impedance inversion results data volumes from two angles to perform pre-stack seismic direct inversion of sensitive fluid factors in deep reservoirs.

[0037] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of the first aspect.

[0038] Fourthly, the present invention provides an electronic device including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the method of the first aspect.

[0039] This invention provides a reservoir inversion method, apparatus, storage medium, and electronic device. It constructs a bipartite AVO reflection characteristic model, which includes fluid bulk modulus and solid rigidity parameters; calculates the fluid bulk modulus and solid rigidity parameters based on parameters of the target area; performs elastic impedance inversion on seismic data and logging curves superimposed at two partial angles to obtain elastic impedance inversion result data volumes at two angles; constructs a bipartite elastic impedance model based on the bipartite AVO reflection characteristic model, and combines the elastic impedance inversion result data volumes at two angles to perform pre-stack seismic direct inversion of sensitive fluid factors in deep reservoirs.

[0040] This invention discloses a direct inversion method for sensitive fluid factors in deep reservoirs. The method comprises four parts: derivation of a two-dimensional AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoirs; calculation and parameter fitting of well logging curves; elastic impedance inversion; and construction and inversion of a two-dimensional elastic impedance equation based on the sensitive fluid factor of deep reservoirs. First, this invention derives a two-dimensional AVO approximation equation, including the fluid bulk modulus, applicable to narrow angles in deep reservoirs, by combining rock physics theoretical models and empirical expressions. Based on this, sensitive fluid factor curves are obtained using well logging curves and parameter fitting, and elastic impedance inversion is performed. Finally, by deriving the established two-dimensional elastic impedance equation and utilizing the quantitative characterization relationship between angle-dependent elastic impedance data and the sensitive fluid factor, pre-stack seismic direct inversion prediction of the sensitive fluid factor in deep reservoirs is achieved. The sensitive fluid factor obtained by this invention is only related to the elastic effect of pore fluids, which can significantly improve the accuracy of reservoir fluid identification. Simultaneously, the derived two-dimensional AVO approximation equation avoids the disadvantage of a narrow incident angle range in deep reservoirs, improving stability and making the inversion method more suitable for deep reservoirs. Attached Figure Description

[0041] The invention will now be described in more detail with reference to embodiments and the accompanying drawings:

[0042] Figure 1 A schematic diagram of a direct inversion method for sensitive fluid factors based on deep reservoirs provided in an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram comparing the degree of influence of porosity on the sensitive fluid factor and the conventional fluid factor obtained in the embodiments of the present invention;

[0044] Figure 3 A schematic diagram of the single-well sensitive fluid factor results obtained using the present invention is provided in an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of an elastic impedance inversion result obtained using the present invention is provided in an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of the sensitive fluid factor inversion result obtained by utilizing the present invention is provided in an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of a reservoir inversion device provided in an embodiment of the present invention.

[0048] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present invention and to fully understand and implement the process of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The embodiments of the present invention and the various features therein can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0050] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0052] Deep reservoirs are the main area for increasing reserves and production in oilfield exploration and development. However, most exploration areas face technical problems such as deep reservoir burial, weak signal, low resolution and narrow bandwidth of seismic data, difficulty in reservoir description and inaccurate fluid prediction.

[0053] The amplitude versus offset (AVO) inversion method based on the Zoeppritz approximation equation has low noise resistance and poor performance in fluid identification applications in complex areas.

[0054] In some technical solutions, an elastic impedance equation is first established, and then angular stacking inversion is performed within the incident angle range of pre-stack seismic data. Compared with AVO inversion, elastic impedance inversion can avoid attribute "leakage" caused by wavelets with different offsets or incident angles. To this end, some scholars have proposed different forms of two-term elastic impedance equations based on deep reservoirs. However, the sensitive factors involved in these equations are not applicable to areas with insufficient consolidation or drastic porosity changes, resulting in lower accuracy of the inversion results. Moreover, they are easily affected by factors such as porosity, leading to artifacts in fluid identification.

[0055] To address this, the technical solution of this invention, combining rock physics theoretical models and empirical formulas, derives a two-term AVO approximation equation incorporating fluid bulk modulus applicable to narrow-angle intrusions in deep reservoirs, along with its corresponding elastic impedance equation. Fluid bulk modulus directly reflects the characteristics of pore fluids and is only related to the elastic effect of pore fluids, independent of factors such as the rock skeleton. Therefore, it can separate the solid skeleton from the fluid elastic effect, achieving decoupling of the solid and liquid phases, significantly improving the accuracy of reservoir fluid identification. Considering the narrow incident angle range of deep reservoirs, this equation can meet the accuracy and stability requirements of inversion under narrow-angle incidence, laying the foundation for more stable extraction of fluid factors. The technical solution and its effects will be described below with reference to embodiments.

[0056] Example 1

[0057] Figure 1 This is a schematic diagram of a direct inversion method for sensitive fluid factors based on deep reservoirs, provided as an embodiment of the present invention. Figure 1 As shown, this embodiment provides a reservoir inversion method, including:

[0058] A bipartite AVO reflection characteristic model is constructed, which includes fluid bulk modulus and solid rigidity parameters;

[0059] Calculate fluid bulk modulus and solid rigidity parameters based on parameters of the target region;

[0060] Elastic impedance inversion was performed on the seismic data and well logging curves superimposed from two partial angles to obtain the elastic impedance inversion result data volume from the two angles.

[0061] A two-dimensional elastic impedance model is constructed based on the two-dimensional AVO reflection characteristic model. The elastic impedance inversion results from two angles are combined to perform pre-stack seismic direct inversion of sensitive fluid factors in deep reservoirs.

[0062] This embodiment provides a reservoir inversion method, apparatus, storage medium, and electronic device. It constructs a bipartite AVO reflection characteristic model, which includes fluid bulk modulus and solid rigidity parameters. The fluid bulk modulus and solid rigidity parameters are calculated based on parameters of the target area. Elastic impedance inversion is performed on seismic data and logging curves superimposed at two partial angles to obtain elastic impedance inversion result data volumes for the two angles. Based on the bipartite AVO reflection characteristic model, a bipartite elastic impedance model is constructed, and combined with the elastic impedance inversion result data volumes for the two angles, pre-stack seismic direct inversion of sensitive fluid factors in deep reservoirs is performed.

[0063] This embodiment discloses a direct inversion method for sensitive fluid factors based on deep reservoirs. The method comprises four parts: derivation of a two-dimensional AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoirs; calculation and parameter fitting of well logging curves; elastic impedance inversion; and construction and inversion of a two-dimensional elastic impedance equation based on the sensitive fluid factor of deep reservoirs. First, this invention derives a two-dimensional AVO approximation equation including the fluid bulk modulus, applicable to deep reservoirs with narrow angles, by combining rock physics theoretical models and empirical expressions. Based on this, sensitive fluid factor curves are obtained using well logging curves and parameter fitting, and elastic impedance inversion is performed. Finally, by deriving and establishing the two-dimensional elastic impedance equation and utilizing the quantitative characterization relationship between angle-dependent elastic impedance data and the sensitive fluid factor, pre-stack seismic direct inversion prediction of the sensitive fluid factor of deep reservoirs is achieved. The sensitive fluid factor obtained by this invention is only related to the elastic effect of pore fluids, which can significantly improve the accuracy of reservoir fluid identification. Simultaneously, the derived two-dimensional AVO approximation equation avoids the disadvantage of a narrow incident angle range in deep reservoirs, improving stability and making the inversion method more suitable for deep reservoirs.

[0064] Example 2

[0065] Based on the above embodiments, this embodiment provides a reservoir inversion method, a two-term AVO reflection characteristic model, including:

[0066]

[0067] Among them, R pp Where K is the reflection coefficient, θ is the incident angle, and K is the reflection coefficient. f f is the bulk modulus of the fluid. m For solid rigidity parameters, A and B are preset coefficients.

[0068] In some implementations, in the bipartite AVO reflection characteristic model, preset coefficients A and B are included, including:

[0069]

[0070]

[0071] Where, γ sat 2 γ is the square of the ratio of P-wave to S-wave velocity in saturated rock. dry 2 R is the square of the ratio of P-wave velocity to S-wave velocity in dry rock, θ is the incident angle, r1 is the fitting coefficient between the P-wave velocity reflection coefficient and the density reflection coefficient in the target area, and r2 is the fitting coefficient between the S-wave velocity reflection coefficient and the density reflection coefficient.

[0072] In regions where the consolidation is not mature enough and porosity changes drastically, conventional fluid factors result in low accuracy of inversion results and are easily affected by factors such as porosity, which can lead to artifacts in fluid identification.

[0073] The technical solution in this embodiment, based on the narrow range of incident angles in deep reservoirs, re-derives the method based on the fluid bulk modulus K. f The two-dimensional AVO approximation equation (two-dimensional AVO reflection characteristic model) can meet the accuracy and stability requirements of inversion under narrow incident angles, and can also improve the accuracy of fluid identification. Specifically, the technical solution of this embodiment first derives a two-dimensional AVO approximation equation (two-dimensional AVO reflection characteristic model) applicable to deep reservoirs with narrow incident angles, which includes the fluid bulk modulus, by combining rock physics theoretical models and empirical expressions. On this basis, the sensitive fluid factor curve is obtained by fitting well logging curves and parameters, and elastic impedance inversion is carried out. Finally, by deriving and establishing the two-dimensional elastic impedance equation and using the quantitative characterization relationship between angle-dependent elastic impedance data and sensitive fluid factor, the pre-stack seismic direct inversion prediction of sensitive fluid factor in deep reservoirs is realized. The sensitive fluid factor obtained by the inversion of this invention is only related to the elastic effect of pore fluid, which can significantly improve the accuracy of reservoir fluid identification. At the same time, the derived two-dimensional AVO approximation equation can avoid the disadvantage of narrow incident angle range in deep reservoirs, improve stability, and make the inversion method more suitable for deep reservoirs.

[0074] Example 3

[0075] Based on the above embodiments, this embodiment provides a reservoir inversion method, wherein the parameters of the target region include: shear wave velocity, density and porosity;

[0076] The steps for calculating the fluid bulk modulus and solid rigidity parameters based on the parameters of the target region include:

[0077] f m =φμ=φ·ρv s 2

[0078] Among them, f m Here are the parameters for a solid's rigidity, where φ is porosity, ρ is density, and v is... s The velocity is the transverse wave velocity.

[0079] In some implementations, the parameters of the target region include: longitudinal wave velocity, transverse wave velocity, density, and porosity;

[0080] The steps for calculating the fluid bulk modulus and solid rigidity parameters based on the parameters of the target region include:

[0081]

[0082] Among them, K f v is the bulk modulus of the fluid. p Let φ be the longitudinal wave velocity. c For critical porosity, γ dry 2 It is the square of the ratio of longitudinal to transverse wave velocities in dry rock.

[0083] The technical solution in this embodiment is based on the longitudinal wave velocity v collected in the target area. p transverse wave velocity v s Calculate the density ρ and porosity curves, and then calculate the elastic impedance curves and fluid bulk modulus K at the corresponding two angles. f Solid rigidity parameter f m .

[0084] Example 4

[0085] Based on the above embodiments, this embodiment provides a reservoir inversion method, a two-term elastic impedance model, including:

[0086] EI(θ)=K f a(θ) f m b(θ)

[0087] Among them, K f f is the bulk modulus of the fluid. m For solid rigidity parameters, α(θ) and b(θ) are preset coefficients.

[0088] In some implementations, in the biphasic elastic impedance model, the expressions for the exponential terms a(θ) and b(θ) are:

[0089]

[0090]

[0091]

[0092] Where, γ sat 2 γ is the square of the ratio of P-wave to S-wave velocity in saturated rock. dry 2 θ is the square of the ratio of P-wave velocity to S-wave velocity in dry rock, θ is the incident angle, r1 is the fitting coefficient between the P-wave velocity reflection coefficient and the density reflection coefficient in the target area, r2 is the fitting coefficient between the S-wave velocity reflection coefficient and the density reflection coefficient, and r is a parameter obtained by fitting based on the relationship between the solid rigidity parameter and porosity.

[0093] The technical solution of this embodiment is based on the construction and inversion of a two-term elastic impedance equation for sensitive fluid factors in deep reservoirs: Based on the derivation of a two-term AVO reflection characteristic equation for sensitive fluid factors in deep reservoirs, a two-term elastic impedance equation is constructed. Utilizing the quantitative characterization relationship between angle-dependent elastic impedance data and sensitive fluid factors, and combining well logging data from the actual work area with the elastic impedance inversion results from the wellbore access path, an angle-dependent weighting coefficient consistent with the target actual work area is obtained using a linear regression optimization algorithm. This enables direct pre-stack seismic inversion prediction of sensitive fluid factors in deep reservoirs.

[0094] Example 5

[0095] Figure 6 This is a schematic diagram of a reservoir inversion device provided in an embodiment of the present invention. Figure 6 As shown, based on the above embodiments, the technical solution of this embodiment provides an inversion device, including:

[0096] The building module is used to construct a bipartite AVO reflection characteristic model, which includes fluid bulk modulus and solid rigidity parameters;

[0097] The parameter acquisition module is used to calculate the fluid bulk modulus and solid rigidity parameters based on the parameters of the target area.

[0098] The elastic impedance inversion module is used to perform elastic impedance inversion on seismic data and well logging curves superimposed from two partial angles to obtain elastic impedance inversion result data volumes from the two angles.

[0099] The direct inversion module is used to construct a two-dimensional elastic impedance model based on the two-dimensional AVO reflection characteristic model, and combine the elastic impedance inversion results data volumes from two angles to perform pre-stack seismic direct inversion of sensitive fluid factors in deep reservoirs.

[0100] Other technical features and beneficial effects of this embodiment can be found in other embodiments of this specification, and will not be repeated here.

[0101] Example 6

[0102] Based on the above embodiments, this embodiment provides an application example.

[0103] The direct inversion method for sensitive fluid factors based on deep reservoirs proposed in this invention includes the following steps:

[0104] (1) Derivation of the two-term AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoir: In regions where the consolidation degree is not mature and the porosity changes drastically, the conventional fluid factor has low inversion accuracy and is easily affected by factors such as porosity, which can produce artifacts in fluid identification. Based on the narrow incident angle range of deep reservoirs, this invention re-derives the two-term AVO reflection characteristic equation based on the fluid bulk modulus K. f The two-dimensional AVO approximation equation (two-dimensional AVO reflection feature model) can meet the accuracy and stability requirements of inversion when incident at narrow angles, and can also improve the accuracy of fluid identification.

[0105] Real rocks are two-phase media containing a solid framework and pore fluids. Russell et al. (2011), based on Gassmann's (1951) theory of porosity elasticity, proposed using the Gassmann fluid term f for fluid identification through a first expression, which is as follows:

[0106]

[0107] In the first expression, f is the Gassmann fluid term, ρ is the density, and v p For the longitudinal wave velocity, γ dry 2 v is the square of the ratio of longitudinal to transverse wave velocities in dry rock. s The velocity is the transverse wave velocity.

[0108] Based on this, the Gassmann fluid term f, shear modulus μ, and density ρ are derived, resulting in a second expression representing the three-term AVO approximation equation, namely the Russell approximation:

[0109]

[0110] In the second expression, R pp (θ) is the formation reflection coefficient, θ is the incident angle, and γ is the incident angle. sat 2 γ is the square of the ratio of P-wave to S-wave velocity in saturated rock. dry 2 ρ is the square of the ratio of longitudinal to transverse wave velocities in dry rock, f is the Gassmann fluid term, μ is the shear modulus, and ρ is the density.

[0111] Based on this, a two-term AVO approximation equation based on the Gassmann fluid term f and the shear modulus u is derived, which is referred to as the third expression in this embodiment:

[0112]

[0113] In the third expression, r1 is the fitting coefficient between the longitudinal wave velocity reflection coefficient and the density reflection coefficient of the actual work area, and r2 is the fitting coefficient between the transverse wave velocity reflection coefficient and the density reflection coefficient. The meanings of other parameters can be found in the previous text and will not be repeated here.

[0114] In the technical solution of the present invention, a two-term AV0 reflection characteristic equation based on the sensitive fluid factor of deep reservoirs is derived according to the empirical expression of rock physics and the relationship between elastic parameters.

[0115] First, obtain the fourth expression from the third expression.

[0116]

[0117] The Gassmann fluid term f is the result of the combined effects of multiple factors, including both fluid and solid parameters, which are coupled together. Based on numerous rock physics experiments, expressions for the Gassmann fluid term f and the fluid bulk modulus K were obtained. f The empirical rock physics expression between these, namely the fifth expression in this embodiment, is expressed as follows:

[0118] f=G(φ)K f (5)

[0119] In the fifth expression,

[0120]

[0121]

[0122] Where φ is the rock porosity, or reservoir porosity, and K m K represents the bulk modulus of a mineral component. dry This represents the bulk modulus of dry rock.

[0123] Substituting the fourth and fifth expressions into the third expression, we obtain the sixth expression in this embodiment as follows:

[0124]

[0125] The meaning of the parameters in the sixth expression can be found in the first to fifth expressions mentioned above, and will not be repeated here.

[0126] Since the shear modulus μ is not affected by fluids, but only by solid-related factors such as the rock's framework minerals, this embodiment uses the dry rock shear modulus μ. dry This is considered equivalent to the saturated shear modulus μ. Therefore, in the technical solution of this invention, the sixth expression is simplified to the following seventh expression:

[0127]

[0128] The meaning of the parameters in the seventh expression can be found in the first to sixth expressions mentioned above, and will not be repeated here.

[0129] Some experimental data show that when the porosity of a rock is less than the critical porosity, the porosity φ, shear modulus μ, bulk modulus K, and critical porosity φ of dry rocks and minerals are affected. c The following equation, expressed as the eighth expression, represents the relationship between them:

[0130]

[0131] In the eighth expression, K represents the bulk modulus, μ represents the shear modulus, and φ... c This represents the critical porosity. The subscript "dry" indicates the elastic parameter of dry rock, and the subscript "m" indicates the elastic parameter with minerals. According to the eighth expression, the modulus of dry rock is equal to the product of the mineral matrix modulus and the porosity.

[0132] When the reservoir porosity is less than the critical porosity φ c At that time, the critical porosity model represented by the eighth expression can be used to establish the equation relationship between K, μ and φ of dry rocks and minerals.

[0133] Based on the expression for shear modulus in the eighth expression, we can further derive the following ninth expression:

[0134]

[0135] Will Substituting G(φ) into the fifth expression, we obtain the following tenth expression:

[0136]

[0137] Substituting the ninth and tenth expressions into the seventh expression, we obtain the following eleventh expression:

[0138]

[0139] Further simplification of the eleventh expression yields the twelfth expression:

[0140]

[0141] Will Substituting into the twelfth expression, we get the thirteenth expression:

[0142]

[0143] Let f m =φμ. Through extensive analysis of actual data, it was found that there is a certain exponential relationship between porosity φ and shear modulus μ. Based on the relationship of rock physics theory, the characteristics of actual work areas, and the statistical laws of rock physics, the power-law relationship between porosity φ and shear modulus μ is established as shown in the fourteenth expression:

[0144] μ=aφ α (14)

[0145] Substituting the power relationship shown in the fourteenth expression into the solid rigidity parameter f m The equation f m =φμ, to obtain the following fifteenth expression:

[0146] f m =μφ=aφ α φ=aφ 1+α (15)

[0147] Let 1 + α = r, then the fifteenth expression can be represented as the sixteenth expression:

[0148] f m =aφ r (16)

[0149] Where a and r are fitting coefficients between porosity and shear modulus established based on the characteristics of the actual work area.

[0150] Further derivation yields the relationship between the reflection coefficient of a solid's rigidity parameter and the reflection coefficient of its porosity, as shown in the seventeenth expression:

[0151]

[0152] Then the final question about K f -f m The two-term AVO approximation expression (two-term AVO reflection feature model) is shown in the eighteenth expression below:

[0153]

[0154] In the eighteenth expression,

[0155]

[0156] K fFor fluid bulk modulus,

[0157] f m =φμ,f m For solid rigidity parameters,

[0158] r is the fitting coefficient between porosity and shear modulus established based on the characteristics of the actual work area.

[0159] The model represented by the above eighteenth expression is the two-term AVO reflection characteristic model based on the sensitive fluid factor of deep reservoirs of the present invention.

[0160] (2) Well logging curve calculation and parameter fitting: Based on the P-wave velocity v collected in the target area p transverse wave velocity v s Calculate the density ρ and porosity curves, and then calculate the elastic impedance curves and fluid bulk modulus K at the corresponding two angles. f Solid rigidity parameter f m And based on the solid rigidity parameter f m The parameters a and r are obtained by fitting the relationship with porosity φ, where the longitudinal wave velocity v collected in the target area is... p transverse wave velocity v s Calculate the density ρ and porosity curves, and then calculate the elastic impedance curves and fluid bulk modulus K at the corresponding two angles. f Solid rigidity parameter f m ,include:

[0161] f m =φμ=φ·ρv s 2 ,

[0162]

[0163] Where φ is porosity, φ c Critical porosity, ρ is density, v s v is the transverse wave velocity. p For the longitudinal wave velocity, γ dry 2 It is the square of the ratio of longitudinal to transverse wave velocities in dry rock.

[0164] (3) Elastic impedance inversion: Elastic impedance inversion is performed using seismic data superimposed from two partial angles and elastic impedance logging curves obtained from different angles. This includes well-seismic calibration, establishment of the initial elastic impedance model, and elastic impedance inversion. Finally, the elastic impedance inversion result data volume from the two angles is obtained.

[0165] (4) Construction and inversion of the two-term elastic impedance equation based on the sensitive fluid factor of deep reservoir: Based on the derivation of the two-term AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoir, the two-term elastic impedance equation is constructed. The quantitative characterization relationship between the angle-dependent elastic impedance data and the sensitive fluid factor is used. Combined with the logging data in the actual work area and the elastic impedance inversion results of the well passage, the angle-dependent weighting coefficient that conforms to the actual work area is obtained by using the linear regression optimization algorithm, so as to realize the pre-stack seismic direct inversion prediction of the sensitive fluid factor of deep reservoir.

[0166] Connolly (1999) proposed the concept of elastic impedance and derived the elastic impedance equation based on the Aki-Richards approximation, which is shown in the nineteenth expression:

[0167]

[0168] In the nineteenth expression, R pp Let θ be the reflection coefficient, θ be the incident angle, and EI be the elastic impedance. The nineteenth expression establishes the relationship between the pre-stack reflection coefficient and the elastic impedance. The two-term elastic impedance equation derived in this invention based on the sensitive fluid factor of deep reservoirs is shown in expression twenty:

[0169] EI(θ)=K f a(θ) f m b(θ) (20)

[0170] The expressions for the exponents a(θ) and b(θ) are as follows:

[0171]

[0172]

[0173]

[0174] Using the Whitcombe (2002) elastic impedance normalization method, the elastic impedance equation shown in Expression 20 is normalized so that the dimensions of the elastic impedance equation are consistent with the longitudinal wave impedance, as shown in Expression 21:

[0175]

[0176] Among them, K f0 f m0 , respectively, are the average values ​​of the fluid bulk modulus and the solid rigidity parameter; A0 is the normalized parameter of elastic impedance, where A0 is shown in expression twenty-two:

[0177]

[0178] Elastic impedance inversion can be viewed as multiple implementations of post-stack seismic inversion applied to partially stacked seismic data at different angles. In Expression 21, the elastic impedance exhibits a nonlinear relationship with the fluid bulk modulus and solid rigidity parameters. To extract the fluid sensitivity factor from the elastic impedance data, Expression 21 is linearized:

[0179]

[0180] The following expression, in matrix form, represents the two deep angles θ1 and θ2, and is used for solution:

[0181]

[0182] Among them, expression twenty-three can be expressed as Ax = b, where A is the coefficient matrix, x is the elastic parameter to be determined, and b is the elastic impedance data.

[0183] To maintain the stability of the solution, the weighting coefficients a(θ) and b(θ) are calculated using the elastic impedance obtained from the well-side seismic trace inversion and the well logging data, as shown in Expression Twenty-Four:

[0184]

[0185] Substituting the wellbore data of elastic impedance at two different angles into expression 24 and combining it with well logging curves, the coefficient matrix A is calculated. Substituting the coefficient matrix A into expression 23, the fluid bulk modulus and solid rigidity parameters can be directly extracted using the least squares algorithm or the conjugate gradient algorithm, thereby enabling pre-stack seismic direct inversion prediction of sensitive fluid factors in deep reservoirs.

[0186] This invention, combining rock physics theoretical models and empirical expressions, derives a two-term AVO approximation equation incorporating fluid bulk modulus applicable to narrow angles in deep reservoirs, along with its corresponding elastic impedance equation. Through well logging parameter calculations and elastic impedance inversion, it achieves direct prediction of sensitive fluid factors in deep reservoirs. The sensitive fluid factor obtained using this invention directly reflects pore fluid characteristics and is only related to the elastic effect of pore fluid, independent of factors such as the rock skeleton. This separates the solid skeleton from the fluid elastic effect, achieving decoupling between the solid and liquid phases, thus significantly improving the accuracy of reservoir fluid identification. Furthermore, the derived two-term AVO approximation equation avoids the limitation of narrow incident angle ranges in deep reservoirs, improving stability and making the inversion method more suitable for deep reservoirs.

[0187] In this application example, using actual data from a certain work area, the method provided by this invention is used to directly invert the sensitive fluid factor based on deep reservoirs. (Appendix) Figure 1A flowchart illustrating a direct inversion method for sensitive fluid factors in deep reservoirs, provided as an embodiment of the present invention; attached. Figure 2 This is a comparison chart showing the degree to which the sensitive fluid factor and the conventional fluid factor obtained in the embodiments of the present invention are affected by porosity; (See attached image) Figure 3 This invention provides a result of a single-well sensitive fluid factor obtained using the present invention; Appendix Figure 4 This invention provides an embodiment of an elastic impedance inversion result obtained using the present invention; Appendix Figure 5 The following is an example of a sensitive fluid factor inversion result obtained using the present invention, provided as an embodiment of the present invention. From left to right, the results are seismic data, conventional fluid factor, and sensitive fluid factor obtained by inversion using the present invention.

[0188] like Figure 1 The diagram shows a flowchart of a direct inversion method for sensitive fluid factors based on deep reservoirs, according to an embodiment of the present invention.

[0189] like Figure 1 Step 1 involves deriving a two-term AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoirs. Conventional fluid factors produce low accuracy in inversion results in regions with insufficient consolidation and drastic porosity changes, and are easily affected by factors such as porosity, leading to artifacts in fluid identification. This invention, based on the narrow range of incident angles in deep reservoirs, re-derives an equation based on the fluid bulk modulus K. f The two-term AVO approximation equation can meet the accuracy and stability requirements of inversion when the incident angle is narrow, and can also improve the accuracy of fluid identification.

[0190] Real rocks are two-phase media containing a solid framework and pore fluids. Based on Gassmann's (1951) theory of porosity elasticity, Russell et al. (2011) proposed using the Gassmann fluid term f for fluid identification, with the following expression:

[0191]

[0192] Where, γ dry 2 v is the square of the ratio of longitudinal to transverse wave velocities in dry rock. p Let v be the longitudinal wave velocity. s Given the transverse wave velocity, a three-term AVO approximation equation, namely the Russell approximation, was subsequently derived based on the Gassmann fluid term (f), shear modulus (u), and density (ρ):

[0193]

[0194] Where, γ sat 2The expression represents the square of the ratio of P-wave to S-wave velocity in saturated rock. Later scholars derived a two-term AVO approximation based on the Gassmann fluid term (f) and the shear modulus (u):

[0195]

[0196] This invention derives a two-term AV0 reflection characteristic equation based on the sensitive fluid factor of deep reservoirs, using empirical expressions in rock physics and the relationship between elastic parameters.

[0197] make

[0198] The Gassmann fluid term f is the result of the combined effects of multiple factors, including both fluid and solid parameters, which are coupled together. Han and Batzle et al., based on extensive rock physics experiments, obtained f and K through statistical analysis of the data. f The empirical expression between them is:

[0199] f=G(φ)K f

[0200] in, φ represents the rock porosity. Therefore, the expression can be further expressed as:

[0201]

[0202] Since μ is not affected by fluids, but only by solid-related factors such as the rock's framework minerals, the dry rock shear modulus μ can be considered as... dry It is equivalent to the saturated shear modulus μ. This allows the expression to be simplified to the following form:

[0203]

[0204] Nur experimental data show that when the porosity of a rock is less than the critical porosity, the porosity, shear modulus, bulk modulus, and critical porosity φ of dry rocks and minerals are affected. c The following equation applies between them:

[0205]

[0206] Where K represents the bulk modulus and μ represents the shear modulus. The subscript dry represents the elastic parameters of dry rock, and the subscript m represents the elastic parameters with minerals. According to the equation, the modulus of dry rock is equal to the product of the mineral matrix modulus and the porosity.

[0207] When the reservoir porosity is less than the critical porosity φ cAt this point, the critical porosity model can be used to establish equations relating K, μ, and φ for dry rocks and minerals. Further derivation based on Nur's critical porosity model yields:

[0208]

[0209] Will Further mathematical transformations of the equation yield the following:

[0210]

[0211] Therefore, the approximate expression can be further derived as follows:

[0212]

[0213] Further simplification yields:

[0214]

[0215] Will Substituting, we get:

[0216]

[0217] Let f m =φμ. Based on extensive analysis of actual data, a certain exponential relationship exists between porosity φ and shear modulus μ. According to rock physics theory, the characteristics of actual work areas, and statistical laws of rock physics, the power-law relationship between φ and μ is established as follows:

[0218] μ=aφ α

[0219] Substitute f m The equation yields:

[0220] f m =μφ=aφ α φ=aφ 1+α

[0221] Let 1 + α = r, then we get:

[0222] f m =aφ r

[0223] Where a and r are fitting coefficients between porosity and shear modulus established based on the characteristics of the actual work area.

[0224] Further derivation yields the relationship between the reflection coefficient of a solid's rigidity parameter and the reflection coefficient of its porosity:

[0225]

[0226]

[0227] Then the final question about K f -f m The two-term AVO approximation expressions are shown below:

[0228]

[0229] in K f f represents the bulk modulus of the fluid. m =φμ, which is called the solid rigidity parameter, and r is the fitting coefficient between porosity and shear modulus established according to the characteristics of the actual working area.

[0230] The above equation is the derived two-term AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoirs.

[0231] like Figure 2 The figure shown is a comparison of the degree to which the sensitive fluid factor obtained using this invention and the conventional fluid factor are affected by porosity. Figure 2 It is evident that fluid identification using conventional fluid factors is still affected by factors such as porosity, leading to fluid identification artifacts. However, the fluid bulk modulus obtained based on porous media theory can effectively achieve solid-liquid decoupling and improve the accuracy of fluid identification.

[0232] In step 2, the calculation and parameter fitting of the logging curves were carried out. Based on the P-wave velocity, S-wave velocity, density, and porosity curves collected from the target area, the elastic impedance curves and fluid bulk modulus K corresponding to the two angles were calculated. f Solid rigidity parameter f m And based on the relationship between solid rigidity parameters and porosity, parameters a and r are obtained by fitting, where,

[0233] f m =φμ=φ·ρv s 2 ,

[0234]

[0235] like Figure 3 As shown, the results of the single-well sensitive fluid factor obtained using the present invention can be seen that the sensitive fluid factor curves in gas-bearing reservoirs all have a certain response.

[0236] In step 3, elastic impedance inversion is performed. Elastic impedance inversion is conducted using seismic data superimposed from two partial angles and calculated elastic impedance logging curves from different angles. This includes well-seismic calibration, establishment of the initial elastic impedance model, and elastic impedance inversion, ultimately yielding the elastic impedance inversion result data volume for both angles. For example... Figure 4As shown, this is one of the elastic impedance inversion results obtained using the present invention.

[0237] In step 4, a two-term elastic impedance equation based on the sensitive fluid factor of deep reservoirs is constructed and inverted. Based on the derivation of the two-term AVO reflection characteristic equation based on the sensitive fluid factor of deep reservoirs, a two-term elastic impedance equation is constructed. By utilizing the quantitative characterization relationship between angle-dependent elastic impedance data and sensitive fluid factor, and combining the logging data in the actual work area and the elastic impedance inversion results of the well passage, the angle-dependent weighting coefficient that conforms to the target work area is obtained by using a linear regression optimization algorithm, thereby realizing the pre-stack seismic direct inversion prediction of the sensitive fluid factor of deep reservoirs.

[0238] Connolly (1999) proposed the concept of elastic impedance and derived the elastic impedance equation based on the Aki-Richards approximation, which is based on the velocity and density of the P-wave and S-wave.

[0239]

[0240] Among them, R pp Let be the reflection coefficient, θ be the incident angle, and EI be the elastic impedance. The above equation establishes the relationship between the pre-stack reflection coefficient and the elastic impedance. This invention, drawing on the idea of ​​Connolly (1999) in deriving the linear impedance equation, derives a two-term elastic impedance equation based on the sensitive fluid factor of deep reservoirs:

[0241] EI(θ)=K f a(θ) f m b(θ)

[0242] The expressions for the exponents a(θ) and b(θ) are as follows:

[0243]

[0244]

[0245]

[0246] The elastic impedance equation is normalized using the Whitcombe (2002) elastic impedance normalization method, so that its dimensions are consistent with those of longitudinal wave impedance:

[0247]

[0248] Among them, K f0 f m0 , respectively, are the average values ​​of the fluid bulk modulus and the solid rigidity parameter; A0 is the normalized parameter of elastic impedance, its expression is:

[0249]

[0250] Elastic impedance inversion can be viewed as multiple applications of post-stack seismic inversion to partially stacked seismic data at different angles. In the above equation, elastic impedance exhibits a nonlinear relationship with fluid bulk modulus and solid rigidity parameters. To extract fluid sensitivity factors from the elastic impedance data, this relationship is linearized:

[0251]

[0252] The two deep angles θ1 and θ2 can be represented in matrix form using the following formula for solution:

[0253]

[0254] This formula can be expressed as Ax = b, where A is the coefficient matrix, x is the elastic parameter to be determined, and b is the elastic impedance data. To maintain the stability of the solution, the weighting coefficients a(θ) and b(θ) can be calculated using the elastic impedance obtained from the well-side seismic trace inversion and the well logging data:

[0255]

[0256] Substitute the wellbore data of elastic impedance at two different angles into the above equation and combine it with the logging curves to calculate the coefficient matrix. Then, substitute the coefficient matrix A into the previous equation and use the least squares algorithm or the conjugate gradient algorithm to directly extract the fluid bulk modulus and solid rigidity parameters, and then carry out pre-stack seismic direct inversion prediction of sensitive fluid factors in deep reservoirs.

[0257] like Figure 5 The figure shows the inversion results of sensitive fluid factors obtained using the present invention. From left to right, the results are seismic data, conventional fluid factors, and sensitive fluid factors obtained by the present invention. It can be seen that the inversion results obtained by the present invention can basically reflect the gas content of the underground strata. Compared with the conventional fluid factor inversion results, the resolution and accuracy are improved to a certain extent, which also proves the effectiveness of the method.

[0258] Example 7

[0259] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of the above embodiment.

[0260] The aforementioned storage media can be flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, app store, etc.

[0261] Example 8

[0262] This embodiment provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the method of the above embodiment.

[0263] The processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the methods in the above embodiments.

[0264] Memory can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0265] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0266] It should be noted that, in this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0267] While the embodiments disclosed in this invention are as described above, the above content is merely for the purpose of facilitating understanding of this invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this invention; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A reservoir inversion method, characterized in that, include: A two-dimensional AVO reflection characteristic model is constructed, which includes fluid bulk modulus and solid rigidity parameters; Calculate fluid bulk modulus and solid rigidity parameters based on parameters of the target region; Elastic impedance inversion was performed on the seismic data and well logging curves superimposed from two partial angles to obtain the elastic impedance inversion result data volume from the two angles. Based on the aforementioned bipartite AVO reflection characteristic model, a bipartite elastic impedance model is constructed. Combined with the elastic impedance inversion results data volume from the two angles, pre-stack seismic direct inversion of the sensitive fluid factor of deep reservoirs is performed. The two-term AVO reflectance feature model includes: in, The reflection coefficient, Let K be the angle of incidence. f Let K be the bulk modulus of the fluid. f f is the relative change in the bulk modulus of the fluid. m For the rigid parameters of a solid, Δf m denoted as the relative change of the solid rigidity parameter, r is the parameter obtained by fitting the relationship between the solid rigidity parameter and porosity, and A and B are preset coefficients; In the bivariate AVO reflection characteristic model, the preset coefficients A and B include: in, The square of the ratio of P-wave to S-wave velocity in saturated rock. R is the square of the ratio of P-wave velocity to S-wave velocity in dry rock, θ is the incident angle, r1 is the fitting coefficient between the P-wave velocity reflection coefficient and the density reflection coefficient in the target area, and r2 is the fitting coefficient between the S-wave velocity reflection coefficient and the density reflection coefficient.

2. The reservoir inversion method according to claim 1, characterized in that, The parameters of the target region include: transverse wave velocity, density, and porosity; The steps for calculating the fluid bulk modulus and solid rigidity parameters based on the parameters of the target region include: Among them, f m For solid rigidity parameters, Porosity, μ is shear modulus, ρ is density, v s The velocity is the transverse wave velocity.

3. The reservoir inversion method according to claim 1, characterized in that, The parameters of the target region include: longitudinal wave velocity, transverse wave velocity, density, and porosity; The steps for calculating the fluid bulk modulus and solid rigidity parameters based on the parameters of the target region include: Among them, K f Let f be the bulk modulus of the fluid, and G(f be the Gassmann fluid term). ) is the porosity function. Porosity is ρ, density is v p Let v be the longitudinal wave velocity. s K represents the transverse wave velocity. n The modulus ratio of dry rock. c Critical porosity It is the square of the ratio of longitudinal to transverse wave velocities in dry rock.

4. The reservoir inversion method according to claim 1, characterized in that, The biphasic elastic impedance model includes: Among them, K f f is the bulk modulus of the fluid. m For solid rigidity parameters, α(θ) and b(θ) are preset coefficients.

5. The reservoir inversion method according to claim 4, characterized in that, In the biphasic elastic impedance model, the exponential term and The expression is: in, The square of the ratio of P-wave to S-wave velocity in saturated rock. θ is the square of the ratio of P-wave velocity to S-wave velocity in dry rock, θ is the incident angle, r1 is the fitting coefficient between the P-wave velocity reflection coefficient and the density reflection coefficient in the target area, r2 is the fitting coefficient between the S-wave velocity reflection coefficient and the density reflection coefficient, and r is a parameter obtained by fitting based on the relationship between the solid rigidity parameter and porosity.

6. A reservoir inversion apparatus based on any one of the reservoir inversion methods of claims 1-5, characterized in that, include: A construction module is used to construct a bipartite AVO reflection characteristic model, which includes fluid bulk modulus and solid rigidity parameters; The parameter acquisition module is used to calculate the fluid bulk modulus and solid rigidity parameters based on the parameters of the target area. The elastic impedance inversion module is used to perform elastic impedance inversion on seismic data and well logging curves superimposed from two partial angles to obtain elastic impedance inversion result data volumes from the two angles. The direct inversion module is used to construct a two-term elastic impedance model based on the two-term AVO reflection characteristic model, and combine the elastic impedance inversion results data volume from the two angles to perform pre-stack seismic direct inversion of the sensitive fluid factor of deep reservoirs.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. An electronic device comprising a processor and a memory, characterized in that, The memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 1 to 5.

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