Prestack inversion method and system based on pore structure parameters

Abstract

The present invention provides a pre-stack inversion method and system based on pore structure parameters, the method comprising: obtaining angle gather data and oil well logging data of a work area; inputting the angle gather data and oil well logging data of the work area into an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to a Sun model and a Russell approximate formula; iteratively solving the objective function to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters. The present invention can reduce the influence of porosity and pore structure on fluid identification by constructing an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters, thereby further improving the accuracy of reservoir fluid prediction.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas geophysical exploration, and in particular to a pre-stack inversion method and system based on pore structure parameters. Background Art

[0002] Seismic exploration reservoir prediction technology is a geophysical exploration method that aims to accurately identify reservoir fluid response. At present, reservoir prediction technology has developed from identifying indirect fluid parameter items such as impedance and multiple elastic parameters to identifying direct fluid parameter items such as Gassmann fluid item and Russell fluid factor. However, as the exploration and development objects become increasingly complex, higher requirements are placed on geophysical exploration technology. Faced with target reservoirs with deep burial and strong heterogeneity, especially for lithologic reservoirs such as carbonate rocks and igneous rocks, the seismic response of the reservoir is more complex, and the acoustic response of the reservoir is obviously affected by porosity and pore type. However, there is currently no fluid identification inversion technology that can eliminate pore-related effects (including porosity and pore structure, etc.). Summary of the invention

[0003] One object of the present invention is to provide a pre-stack inversion method based on pore structure parameters, by constructing an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters, to reduce the influence of porosity and pore structure on fluid identification, and further improve the accuracy of reservoir fluid prediction. Another object of the present invention is to provide a pre-stack inversion system based on pore structure parameters. Another object of the present invention is to provide a computer device. Another object of the present invention is to provide a readable medium.

[0004] In order to achieve the above objectives, the present invention discloses a prestack inversion method based on pore structure parameters, comprising:

[0005] Obtain the angle gather data and oil well logging data of the work area;

[0006] Inputting the work area angle gather data and the work area oil well logging data into the objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to the Sun model and Russell approximation formula;

[0007] The objective function is iteratively solved to obtain fluid parameters such as bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0008] Preferably, the method further comprises the step of obtaining the objective function according to the Sun model and the Russell approximation formula.

[0009] Preferably, the objective function obtained according to the Sun model and the Russell approximation formula specifically includes:

[0010] The Sun model is simplified based on the bulk modulus of rock matrix and fluid;

[0011] The Gassmann fluid term formula is obtained based on the simplified Sun model and Biot-Gassmann theory;

[0012] Substitute the Gassmann fluid term formula into the Russell approximation formula, and obtain the reflection coefficient formula including the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the differential property formula;

[0013] The objective function is established according to the Bayesian theory and the reflection coefficient formula.

[0014] Preferably, establishing the objective function according to the Bayesian theory and the reflection coefficient formula specifically includes:

[0015] The reflection coefficient formula is simplified into a matrix form;

[0016] The multivariate Cauchy distribution constraints are obtained based on Bayesian theory;

[0017] The objective function is formed according to the reflection coefficient formula in matrix form and the multivariate Cauchy distribution constraint.

[0018] Preferably, the fluid parameters including bulk modulus, shear modulus, density, porosity and pore structure parameters obtained by iteratively solving the objective function specifically include:

[0019] Let the first-order partial derivative of the objective function be 0 to obtain the model to be solved;

[0020] The model to be solved is solved by iterative reweighted least square method to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0021] Preferably, the fluid parameters including bulk modulus, shear modulus, density, porosity and pore structure parameters obtained by solving the model to be solved by iterative reweighted least squares method specifically include:

[0022] Solving the model to be solved to obtain the longitudinal and transverse wave velocity ratios of saturated rock;

[0023] If the iteration end condition is not met, repeating the steps of obtaining iterative values ​​of the fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameter through channel integral, re-establishing the model to be solved according to the iterative values ​​and solving the model until the iteration end condition is met;

[0024] The fluid bulk modulus, shear modulus, density, porosity and pore structure parameters are obtained according to the relationship among the fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameters.

[0025] The present invention also discloses a pre-stack inversion system based on pore structure parameters, comprising:

[0026] A data acquisition module, used to acquire the work area angle gather data and the work area oil well logging data;

[0027] An objective function forming module is used to input the work area angle gather data and the work area oil well logging data into an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to the Sun model and Russell approximation formula;

[0028] The iterative solution module is used to iteratively solve the objective function to obtain fluid parameters such as fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0029] Preferably, it also includes an objective function construction module, which is used to obtain the objective function according to the Sun model and the Russell approximation formula.

[0030] Preferably, the objective function construction module is specifically used to simplify the Sun model based on the rock matrix bulk modulus and the fluid bulk modulus; obtain the Gassmann fluid term formula according to the simplified Sun model and the Biot-Gassmann theory; substitute the Gassmann fluid term formula into the Russell approximation formula, and obtain the reflection coefficient formula including the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the differential property formula; establish the objective function according to the Bayesian theory and the reflection coefficient formula.

[0031] Preferably, the objective function construction module is further used to simplify the reflection coefficient formula into a matrix form; obtain the multivariate Cauchy distribution constraint according to Bayesian theory; and form the objective function according to the reflection coefficient formula in matrix form and the multivariate Cauchy distribution constraint.

[0032] Preferably, the iterative solution module is specifically used to set the first-order partial derivative of the objective function to 0 to obtain the model to be solved; and to solve the model to be solved by iterative reweighted least squares method to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0033] Preferably, the iterative solution module is further used to solve the model to be solved to obtain the longitudinal and transverse wave velocity ratios of saturated rock; if the iteration end condition is not met, repeatedly obtain iterative values ​​of fluid bulk modulus, solid rigidity parameters, structural density parameters, porosity and pore structure parameters through channel integration, and re-establish the model to be solved according to the iterative values ​​and solve the steps until the iteration end condition is met; obtain the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the relationship among the fluid bulk modulus, solid rigidity parameters, structural density parameters, porosity and pore structure parameters.

[0034] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0035] When the processor executes the program, the method described above is implemented.

[0036] The present invention also discloses a computer readable medium having a computer program stored thereon.

[0037] When the program is executed by a processor, the method described above is implemented.

[0038] The present invention constructs an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the Sun model and Russell approximation formula, and iteratively solves the objective function to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters. The fluid bulk modulus obtained by solving is a fluid term that eliminates the influence of porosity and pore structure, and the fluid identification accuracy for heterogeneous reservoirs is significantly improved. The present invention can reduce the influence of porosity and pore structure on fluid identification, and further improve the accuracy of reservoir fluid prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flowchart showing a specific embodiment of the prestack inversion method based on pore structure parameters of the present invention;

[0041] Figure 2 A flowchart showing a specific embodiment S000 of the prestack inversion method based on pore structure parameters of the present invention;

[0042] Figure 3A flowchart showing a specific embodiment S040 of the prestack inversion method based on pore structure parameters of the present invention;

[0043] Figure 4 A flowchart showing a specific embodiment S300 of the prestack inversion method based on pore structure parameters of the present invention;

[0044] Figure 5 A flowchart showing a specific embodiment S320 of the prestack inversion method based on pore structure parameters of the present invention;

[0045] Figure 6 A flowchart showing the objective function solution of a specific embodiment of the prestack inversion method based on pore structure parameters of the present invention;

[0046] Figure 7 A comparison diagram of a fluid parameter curve obtained by inversion and an actual well curve according to a specific embodiment of the pre-stack inversion method based on pore structure parameters of the present invention is shown;

[0047] Figure 8(a)-Figure 8(e) A cross-sectional diagram showing fluid parameters obtained by inversion according to a specific embodiment of the pre-stack inversion method based on pore structure parameters of the present invention;

[0048] Fig. 9 A comparison diagram of a parameter curve obtained by inversion conversion and an actual well curve according to a specific embodiment of the pre-stack inversion method based on pore structure parameters of the present invention is shown;

[0049] Fig.10 A comparison diagram between a parameter curve obtained by inverting the Russell approximate formula and an actual well curve in a specific embodiment of the pre-stack inversion method based on pore structure parameters of the present invention is shown;

[0050] Figure 11(a)-Figure 11(c) A cross-sectional diagram showing parameters obtained by inversion and conversion according to a specific embodiment of the pre-stack inversion method based on pore structure parameters of the present invention;

[0051] Figure 12(a)-Figure 12(c) A cross-sectional diagram showing parameters obtained by inverting the Russell approximate formula in a specific embodiment of the pre-stack inversion method based on pore structure parameters of the present invention;

[0052] Fig.13 A structural diagram showing a specific embodiment of the prestack inversion system based on pore structure parameters of the present invention;

[0053] Fig.14 A structural diagram showing a specific embodiment of the pre-stack inversion system based on pore structure parameters of the present invention includes an objective function building module;

[0054] Fig.15 A schematic diagram showing the structure of a computer device suitable for implementing an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0056] At present, in order to solve the influence of porosity and pore type on reservoir seismic response in deeply buried and highly heterogeneous target reservoirs, there are mainly two solutions to the problem of reservoir prediction with strong heterogeneity in the existing technology: (1) In order to reflect the relationship between fluid response and rock pores, a reflection coefficient formula including fluid bulk modulus and porosity is proposed. This method can effectively reduce the fluid identification error caused by porosity by obtaining the fluid bulk modulus as a fluid indicator. However, this method can only reduce the influence of porosity on reservoir prediction, and cannot eliminate the interference of pore structure factors on fluid identification. (2) In order to introduce the influencing factors of pore structure into reservoir prediction, it is proposed to apply a rock physics model containing pore structure parameters, such as the Eshelby-Walsh model or the Keys-Xu model, etc., and to construct an empirical formula between the pore structure parameters and various elastic parameters through core and logging data to achieve fluid prediction that eliminates the influence of pore structure. However, since this method only applies the fitting theory to obtain the empirical formula and does not construct a systematic inversion formula, it can only be applied to a specific work area or specific rock formation and is not universal. Therefore, there is no systematic fluid identification inversion technology in the prior art that can eliminate pore-related influences (including porosity and pore structure, etc.).

[0057] Based on this, according to one aspect of the present invention, this embodiment discloses a pre-stack inversion method based on pore structure parameters. Figure 1 As shown, in this embodiment, the method includes:

[0058] S100: Acquire the angle gather data and oil well logging data of the work area.

[0059] S200: Inputting the work area angle gather data and the work area oil well logging data into an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to the Sun model and Russell approximation formula.

[0060] S300: Iteratively solving the objective function to obtain fluid parameters such as bulk modulus, shear modulus, density, porosity and pore structure parameters of the fluid.

[0061] The present invention constructs an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the Sun model and Russell approximation formula, and iteratively solves the objective function to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters. The fluid bulk modulus obtained by solving is a fluid term that eliminates the influence of porosity and pore structure, and the fluid identification accuracy for heterogeneous reservoirs is significantly improved. The present invention can reduce the influence of porosity and pore structure on fluid identification, and further improve the accuracy of reservoir fluid prediction.

[0062] In a preferred embodiment, the method further comprises a step S000 of obtaining the objective function according to the Sun model and the Russell approximation formula. It is understandable that by predicting the construction of the objective function according to the Sun model and the Russell approximation formula, and then using the reservoir work area angle gather data and the work area oil well logging data, the pre-stack amplitude variation with offset (AVO) inversion can be performed to obtain the fluid parameters. Compared with the empirical formulas between pore structure parameters and various elastic parameters in the prior art, which can only be applied to a specific work area or a specific rock formation, the present invention provides a systematic inversion formula with universal applicability.

[0063] In a preferred embodiment, Figure 2 As shown, the objective function obtained by S000 according to the Sun model and the Russell approximation formula specifically includes:

[0064] S010: Simplify the Sun model based on the bulk modulus of the rock matrix and the bulk modulus of the fluid.

[0065] S020: The Gassmann fluid term formula is obtained based on the simplified Sun model and Biot-Gassmann theory.

[0066] S030: Substitute the Gassmann fluid term formula into the Russell approximation formula, and obtain the reflection coefficient formula including the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the differential property formula.

[0067] S040: Establishing the objective function according to the Bayesian theory and the reflection coefficient formula.

[0068] Specifically, it can be understood that the relevant formula of the Sun model is:

[0069]

[0070] Where K is the saturation bulk modulus, φ is the porosity, and K s is the rock matrix bulk modulus, Kf is the bulk modulus of the fluid, and γ is the pore structure parameter.

[0071] When the porosity is small or the target reservoir is buried deep, K s >>K f ,but, Among them, the rock matrix bulk modulus needs to be much smaller than the fluid bulk modulus. Those skilled in the art can obtain specific parameter values ​​of the rock matrix bulk modulus and the fluid bulk modulus according to specific geological conditions and determine whether the rock matrix bulk modulus is much smaller than the fluid bulk modulus. The present invention will not repeat them here. Therefore, the Sun model can be simplified based on the rock matrix bulk modulus and the fluid bulk modulus. The simplified Sun model is:

[0072] K=(1-φ) γ K s +[1-(1-φ) γ ]K f

[0073] Furthermore, according to the Biot-Gassmann theory, we can get:

[0074] K=K dry +f

[0075] Among them, K dry is the bulk modulus of rock skeleton, and f is the Gassmann fluid term.

[0076] Substituting the simplified Sun model into the above formula, we can deduce:

[0077] K dry =(1-φ) γ K s , f=[1-(1-φ) γ ]K f

[0078] When the porosity is small, the Gassmann fluid term formula can be simplified as:

[0079] f=[1-(1-γφ)]K f

[0080] Russell's approximate formula is:

[0081]

[0082] Among them, R pp is the longitudinal wave reflection coefficient, θ is the incident angle, γ dry is the ratio of P-wave velocity to S-wave velocity in dry rock, is the ratio of the longitudinal and transverse wave velocities of saturated rock, μ is the shear modulus, and ρ is the density.

[0083] Substitute the simplified Gassmann fluid term f formula into the Russell approximate formula, and according to the differential property formula The reflection coefficient formula R including the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters can be obtained: PP :

[0084]

[0085] Among them, f m =μφ is the solid rigidity parameter, f s =ργ is the structural density parameter.

[0086] Finally, the objective function can be established according to the Bayesian theory and the above reflection coefficient formula to perform inversion.

[0087] In a preferred embodiment, Figure 3 As shown, the S040 establishes the objective function according to the Bayesian theory and the reflection coefficient formula, specifically including:

[0088] S041: Simplify the reflection coefficient formula into a matrix form.

[0089] S042: Obtain multivariate Cauchy distribution constraints based on Bayesian theory.

[0090] S043: Form the objective function according to the reflection coefficient formula in matrix form and the multivariate Cauchy distribution constraint.

[0091] Specifically, it can be understood that for the reflection coefficient formula obtained above, the reflection coefficient formula R PP In matrix form:

[0092]

[0093] That is, Gm=D.

[0094]

[0095] According to Bayesian theory, multivariate Cauchy distribution constraints are introduced, and the above formula is used to construct the objective function F(m):

[0096]

[0097] in, is the multivariate Cauchy constraint term, K is the number of time sampling points, α is the weight coefficient, ψ is the correlation matrix, and D i is a 5K×5K matrix, D i Its specific expression is:

[0098]

[0099] In a preferred embodiment, Figure 4 As shown, the fluid parameters obtained by iteratively solving the objective function in S300, including bulk modulus, shear modulus, density, porosity and pore structure parameters of the fluid, may specifically include:

[0100] S310: Setting the first-order partial derivative of the objective function to 0 obtains the model to be solved.

[0101] S320: Solving the model to be solved by iterative reweighted least squares method to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0102] Specifically, it can be understood that in order to obtain the minimum value of the objective function, the first-order partial derivative of F(m) with respect to m is set to 0, and then it can be solved using the iterative reweighted least squares method to obtain the optimal solution of the fluid parameters of the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0103] In a preferred embodiment, Figure 5 As shown, the S320 solves the model to be solved by iterative reweighted least squares method to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters, which may specifically include:

[0104] S321: Solve the model to be solved to obtain the longitudinal and transverse wave velocity ratios of saturated rock.

[0105] S322: If the iteration end condition is not met, repeatedly obtain iterative values ​​of fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameter through channel integration, and re-establish the model to be solved according to the iterative values ​​and solve it until the iteration end condition is met.

[0106] S323: According to the relationship between the fluid bulk modulus, the solid rigidity parameter, the structural density parameter, the porosity and the pore structure parameter, the fluid bulk modulus, the shear modulus, the density, the porosity and the pore structure parameter are obtained.

[0107] Specifically, it is understandable that the iterative process of iterative reweighted least squares method needs to continuously update γ sat , get K f ,f m ,f s ,φ,γ5 parameters of reflection coefficients, and use the Dow integral to obtain K f ,f m ,f s The values ​​of the five parameters, φ, and γ, are used in the next iteration. f ,f m ,fs ,φ,γ, iterative calculation is performed, and the iteration can be stopped when convergence is completed. Finally, the K f ,f m ,f s ,φ,γ. In practical applications, it is usually based on K f ,f m ,f s ,φ,γ parameter relationship, the final output K f ,μ,ρ,φ,γ, such as Figure 6 shown.

[0108] The present invention uses the Sun model to link the three parameters of pore structure parameters, porosity, and fluid bulk modulus with the Gassmann fluid term of the Russell approximation formula, constructs a reflection coefficient formula containing pore structure parameters, porosity, fluid bulk modulus, shear modulus and density, and uses the reflection coefficient formula to construct an objective function for AVO inversion. The fluid bulk modulus obtained by inversion is a fluid term that has eliminated the effects of porosity and pore structure, and the fluid identification accuracy for heterogeneous reservoirs is significantly improved. Among them, Bayesian theory is introduced into the objective function, and multivariate Cauchy distribution constraints are used to ensure inversion sparsity and improve inversion suitability.

[0109] The present invention has been applied in actual seismic data, and the actual inversion results show that the fluid bulk modulus K calculated by the present invention f Separated from the porosity φ and the void structure parameter γ, the fluid bulk modulus K f No longer affected by these two pore-related parameters, a reservoir fluid prediction result with higher prediction accuracy can be obtained. f , μ, ρ, φ, γ are converted into the three parameters f, μ, ρ of the conventional computational fluid term, and then the results are compared with the three parameters inverted by the Russell approximate formula. It can be found that the inversion results of the present invention are consistent with the inversion results of Russell, which shows that the results of the present invention have high reliability.

[0110] The present invention is further described below by a specific example. In this specific example, the present embodiment is a carbonate reservoir in an oil field. The target reservoir is buried at a depth of about 5,000 meters and is an oil and gas reservoir with strong heterogeneity and deep burial depth. The region is affected by the changes in the sedimentary environment and the diversity of diagenesis, forming extremely complex pore space types. These pore structures seriously affect the accuracy of reservoir fluid identification in the region.

[0111] The angular gather data of this work area is a partial stack gather, with 500 seismic traces in each line, and each seismic trace has 6 incident angles, namely 5°, 10°, 15°, 20°, 25° and 30°, and the number of time sampling points K = 211. The logging data of a well in this work area includes the longitudinal wave velocity v P , shear wave velocity v S , density ρ, porosity φ and pore structure parameter γ, the solid rigidity parameter f can be calculated m =μφ and structural density parameter f s =ργ. Preset dry rock P-wave velocity ratio γ dry is a constant, according to the formula f = v P 2 ·ρ-γ dry 2 ·ρ·v s 2 and f = [1-(1-γφ)]K f Get the fluid bulk modulus K f =(v P 2 ·ρ-γ dry 2 ·ρ·v s 2 ) / [1-(1-φ) γ ]. K obtained from logging data f ,f m ,f s ,φ,γ are obtained through low-pass filtering and interpolation to obtain the low-frequency model of the actual work area.

[0112] From the logging data, we know that the porosity of this work area is relatively small, that is, φ<0.08, so according to K s >>K f Simplify the Sun model and get the simplified formula:

[0113] K=(1-φ) γ K s +[1-(1-φ) γ ]K f

[0114] Substituting the simplified Sun model formula into the Biot-Gassmann theoretical formula, we get the Gassmann fluid term expression:

[0115] f=[1-(1-γφ)]K f

[0116] Substitute the Gassmann fluid term expression into the Russell approximation formula to construct the reflection coefficient formula:

[0117]

[0118] According to Bayesian theory, the multivariate Cauchy distribution constraint is introduced, and the objective function is constructed from the above formula to obtain:

[0119]

[0120] in, The second term on the right side of the equation is the multivariate Cauchy constraint term, K is the number of time sampling points, α is the weight coefficient, ψ is the correlation matrix, and D i is a 5K×5K matrix, and its specific expression is:

[0121]

[0122] The minimum value of the objective function is obtained, that is, the first-order partial derivative of F(m) with respect to m is set to 0, and the iterative reweighted least squares method is used to solve it.

[0123] Continuously update γ during the iteration process sat , get K f ,f m ,f s The reflection coefficients of the five parameters ,φ,γ are calculated, and the values ​​of the five parameters at each iteration are obtained by using the Dow integration for the next iteration.

[0124] When the iterative calculation is completed and converged, the iteration can be stopped. Finally, the accurate values ​​of the five parameters are output by using the channel integral. The comparison between the inversion curve and the actual well curve is as follows: Figure 7 As shown, Figure 7 The dotted line in the figure is the low-frequency model, the solid line is the actual well curve, and the X-line is the inversion curve. According to the relationship between the five parameters, the final output K f ,μ,ρ,φ,γ these five parameters inversion profile, such as Figure 8(a)-8(e) shown.

[0125] In order to compare the reliability and accuracy of the inversion results of the present invention, the final output parameter K f ,μ,ρ,φ,γ by the formula f=[1-(1-φ) γ ]K f The inversion curve is converted into the same three parameters f, μ, ρ as those obtained by inversion using the Russell approximate formula. The comparison between the inversion curve and the actual well curve is as follows: Fig. 9 and Fig.10 As shown, Fig. 9 The solid line in the middle is the actual well curve, and the X-line is the inversion curve. Fig.10 The middle dashed line: low-frequency model; the solid line: actual well curve; the X-line: inversion curve). At the same time, the Russell approximation formula is used to invert this work area, and the inversion results are shown in Figure 11(a)-11(c) and Figure 12(a)-12(c)As shown. By comparing the inversion results converted by the present invention with the results of the inversion using the Russell approximate formula, it can be found that the inversion results of the two inversion methods are consistent in terms of f, μ, and ρ parameters, which indicates the reliability of the reservoir prediction of the present invention. Since the present invention can obtain a fluid bulk modulus that is not affected by porosity and pore structure, the bulk modulus can avoid fluid identification illusions caused by changes in porosity and pore structure, indicating that the present invention has higher fluid identification accuracy and provides more powerful data support for subsequent reservoir evaluation and development.

[0126] Based on the same principle, this embodiment also discloses a pre-stack inversion system based on pore structure parameters. Fig.13 As shown, in this embodiment, the system includes a data acquisition module 11, an objective function forming module 12 and an iterative solution module 13.

[0127] The data acquisition module 11 is used to acquire the angle gather data of the work area and the well logging data of the oil wells in the work area.

[0128] The objective function forming module 12 is used to input the work area angle gather data and the work area oil well logging data into an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to the Sun model and Russell approximation formula.

[0129] The iterative solution module 13 is used to iteratively solve the objective function to obtain fluid parameters such as fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0130] In a preferred embodiment, Fig.14 As shown, the system further comprises an objective function building module 10. The objective function building module 10 is used to obtain the objective function according to the Sun model and the Russell approximation formula.

[0131] In a preferred embodiment, the objective function construction module 10 is specifically used to simplify the Sun model based on the rock matrix bulk modulus and the fluid bulk modulus; obtain the Gassmann fluid term formula according to the simplified Sun model and the Biot-Gassmann theory; substitute the Gassmann fluid term formula into the Russell approximation formula, and obtain the reflection coefficient formula including the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the differential property formula; establish the objective function according to the Bayesian theory and the reflection coefficient formula.

[0132] In a preferred embodiment, the objective function construction module 10 is further used to simplify the reflection coefficient formula into a matrix form; obtain the multivariate Cauchy distribution constraint according to Bayesian theory; and form the objective function according to the reflection coefficient formula in matrix form and the multivariate Cauchy distribution constraint.

[0133] In a preferred embodiment, the iterative solution module 13 is specifically used to set the first-order partial derivative of the objective function to 0 to obtain the model to be solved; and solve the model to be solved by iterative reweighted least squares method to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters.

[0134] In a preferred embodiment, the iterative solution module 13 is further used to solve the model to be solved to obtain the longitudinal and transverse wave velocity ratio of saturated rock; if the iteration end condition is not met, repeatedly obtain the iterative values ​​of fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameter through channel integration, and re-establish the model to be solved according to the iterative values ​​and solve it until the iteration end condition is met; obtain the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the relationship between the fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameters.

[0135] Since the principle of solving the problem by this system is similar to that of the above method, the implementation of this system can refer to the implementation of the method and will not be repeated here.

[0136] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, and specifically, the computer device may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0137] In a typical example, the computer device specifically includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method executed by the client as described above is implemented; or, when the processor executes the program, the method executed by the server as described above is implemented.

[0138] Reference below Fig.15 , which shows a schematic diagram of the structure of a computer device 600 suitable for implementing an embodiment of the present application.

[0139] like Fig.15As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0140] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal feedback device (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed as needed as the storage section 608.

[0141] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 609, and / or installed from the removable medium 611.

[0142] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0143] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0144] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0147] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0148] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0149] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0150] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0151] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A prestack inversion method based on pore structure parameters, characterized in that: include: Obtain the angle gather data and oil well logging data of the work area; Inputting the work area angle gather data and the work area oil well logging data into the objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to the Sun model and Russell approximation formula; Iteratively solving the objective function to obtain fluid parameters including bulk modulus, shear modulus, density, porosity and pore structure parameters of the fluid; Obtaining the objective function according to the Sun model and the Russell approximate formula specifically includes: simplifying the Sun model based on the rock matrix bulk modulus and the fluid bulk modulus; obtaining the Gassmann fluid term formula according to the simplified Sun model and the Biot-Gassmann theory; substituting the Gassmann fluid term formula into the Russell approximate formula, and obtaining the reflection coefficient formula including the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the differential property formula; establishing the objective function according to the Bayesian theory and the reflection coefficient formula; The establishment of the objective function according to the Bayesian theory and the reflection coefficient formula specifically includes: abbreviating the reflection coefficient formula into a matrix form; obtaining a multivariate Cauchy distribution constraint according to the Bayesian theory; forming the objective function according to the reflection coefficient formula in the matrix form and the multivariate Cauchy distribution constraint; The iterative solution of the objective function to obtain the fluid parameters including the bulk modulus, shear modulus, density, porosity and pore structure parameters of the fluid specifically includes: setting the first-order partial derivative of the objective function to 0 to obtain the model to be solved; solving the model to be solved by iterative reweighted least squares method to obtain the fluid parameters including the bulk modulus, shear modulus, density, porosity and pore structure parameters of the fluid; Wherein, the Sun model is: Where K is the saturation bulk modulus, f is the porosity, and K s is the rock matrix bulk modulus, K f is the bulk modulus of the fluid, and g is the pore structure parameter.

2. The prestack inversion method based on pore structure parameters according to claim 1, characterized in that: The fluid parameters including bulk modulus, shear modulus, density, porosity and pore structure parameters obtained by solving the model to be solved by iterative reweighted least squares method specifically include: Solving the model to be solved to obtain the longitudinal and transverse wave velocity ratios of saturated rock; If the iteration end condition is not met, repeating the steps of obtaining iterative values ​​of the fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameter through channel integral, re-establishing the model to be solved according to the iterative values ​​and solving the model until the iteration end condition is met; The fluid bulk modulus, shear modulus, density, porosity and pore structure parameters are obtained according to the relationship among the fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameters.

3. A pre-stack inversion system based on pore structure parameters, characterized in that: include: A data acquisition module, used to acquire the work area angle gather data and the work area oil well logging data; An objective function forming module is used to input the work area angle gather data and the work area oil well logging data into an objective function including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters obtained according to the Sun model and Russell approximation formula; An iterative solution module, used for iteratively solving the objective function to obtain fluid parameters such as bulk modulus, shear modulus, density, porosity and pore structure parameters of the fluid; The objective function building module is used to simplify the Sun model based on the rock matrix bulk modulus and fluid bulk modulus; the Gassmann fluid term formula is obtained according to the simplified Sun model and Biot-Gassmann theory; Substituting the Gassmann fluid term formula into the Russell approximation formula, and obtaining a reflection coefficient formula including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the differential property formula; establishing the objective function according to the Bayesian theory and the reflection coefficient formula; The objective function construction module is further used to simplify the reflection coefficient formula into a matrix form; obtain a multivariate Cauchy distribution constraint according to Bayesian theory; and form the objective function according to the reflection coefficient formula in the matrix form and the multivariate Cauchy distribution constraint; The iterative solution module is specifically used to make the first-order partial derivative of the objective function equal to 0 to obtain the model to be solved; solve the model to be solved by iterative reweighted least squares method to obtain fluid parameters including fluid bulk modulus, shear modulus, density, porosity and pore structure parameters; Wherein, the Sun model is: Where K is the saturation bulk modulus, f is the porosity, and K s is the rock matrix bulk modulus, K f is the bulk modulus of the fluid, and g is the pore structure parameter.

4. The prestack inversion system based on pore structure parameters according to claim 3, characterized in that: The iterative solution module is further used to solve the model to be solved to obtain the longitudinal and transverse wave velocity ratio of saturated rock; if the iteration end condition is not met, repeatedly obtain the iterative values ​​of fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameter through channel integration, and re-establish the model to be solved according to the iterative values ​​and solve the steps until the iteration end condition is met; obtain the fluid bulk modulus, shear modulus, density, porosity and pore structure parameters according to the relationship between the fluid bulk modulus, solid rigidity parameter, structural density parameter, porosity and pore structure parameters.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 2 is implemented.

6. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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