Seismic data imaging method and system based on anisotropy

Abstract

The invention provides a seismic data imaging method and system based on anisotropy, and belongs to the technical field of oil and gas field development. According to the method, on the basis of the research of anisotropy, a high-order difference operator equation is improved, and a vertical transverse medium anisotropy model and a transverse medium isotropy model are established on the basis of improved difference approximation expressions of different orders; the established vertical transverse medium anisotropy and transverse medium isotropy model is adopted to carry out seismic data imaging. According to the method, the physical property characteristics of the earth medium with dramatic changes in the longitudinal and transverse directions can be effectively processed; the method has the advantages of improving the signal-to-noise ratio of seismic data, improving the seismic imaging effect, improving the imaging precision of a fracture system and highlighting the seismic reflection characteristics of a target layer.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas field development, and in particular to a seismic data imaging method and system based on anisotropy. Background Art

[0002] With the increasing efforts in oil exploration and development, the requirements for seismic data processing are becoming increasingly higher. In the face of the large changes in reservoir properties in the study area, the development of various structural forms such as flower-shaped, high-steep, and embedded, strong heterogeneity in the vertical and horizontal directions, drastic changes in velocity space, and the limitation of prestack time migration methods, the imaging accuracy is not high enough. There is an urgent need for more accurate imaging methods and technologies to obtain high-precision imaging results.

[0003] The existing seismic wave simulation methods include finite difference method, finite element method and pseudo-spectral method. Finite difference method is still the main method because of its simple and fast algorithm and its ability to automatically adapt to any changes in the velocity field. The research content mainly includes the construction of wave field extension operators, numerical dispersion suppression and boundary reflection suppression. The core of reverse time migration is the forward problem in the final analysis. It is very necessary to select an algorithm with good calculation accuracy and high efficiency.

[0004] The existing technology has at least the following deficiencies:

[0005] 1. This patent cannot effectively produce accurate results in complex strata. This patent is yet to be applied in difficult research areas.

[0006] 2. The overall benefit estimation cannot produce accurate results, and a mature system needs to be formed before estimation. Summary of the invention

[0007] In order to solve the problems existing in the prior art, the present invention provides a seismic data imaging method based on anisotropy. Based on the study of anisotropy, by improving the high-order differential operator equation, vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders; the established vertical lateral medium anisotropy and lateral medium isotropy models are used to perform seismic data imaging. The present invention can effectively process the physical properties of the earth medium with drastic changes in the vertical and horizontal directions, and has advantages in improving the signal-to-noise ratio of seismic data, improving the seismic imaging effect, improving the imaging accuracy of the fault system, and highlighting the seismic reflection characteristics of the target layer.

[0008] The present invention provides a seismic data imaging method based on anisotropy, comprising the following steps:

[0009] Determine the quantitative characterization of the complex anisotropic mechanical properties of tight conglomerate reservoirs;

[0010] Modeling elastic parameters affected by anisotropy;

[0011] Model the effects of anisotropy on Young's modulus and Poisson's ratio;

[0012] Optimize and transform the parameters of the elastic disturbance coefficient equation and establish differential approximations of different orders;

[0013] Based on the improved differential approximations of different orders, the vertical lateral medium anisotropy and lateral medium isotropy models are established;

[0014] The established vertical lateral medium anisotropy and lateral medium isotropy models are used for seismic data imaging.

[0015] Preferably, the complex anisotropic mechanical properties of the tight conglomerate reservoir include: transverse anisotropy, longitudinal anisotropy, permeability and Young's modulus mechanical property parameters.

[0016] Preferably, the index system established to meet the characteristics of tight oil reservoirs in the study area is specifically that Young's modulus and Poisson's ratio are positively correlated, and the main elastic parameters are related functions.

[0017] Preferably, the stress tensor and the strain tensor are considered to quantitatively characterize the complex anisotropic mechanical properties of the tight conglomerate reservoir.

[0018] Preferably, the following form is used to quantitatively characterize the complex anisotropic mechanical properties of tight conglomerate reservoirs:

[0019] σ=C×ε

[0020] in,

[0021] σ represents the stress tensor vector;

[0022] C represents the anisotropic mechanical property matrix;

[0023] ε is the strain tensor vector of anisotropic mechanical properties.

[0024] Preferably, the model in which elastic parameters are affected by anisotropy is quantitatively characterized in the following form:

[0025]

[0026] in,

[0027] (σ1 σ2 σ3 σ4 σ5 σ6) represents the stress tensor of anisotropic mechanical properties;

[0028] Elastic parameter matrix representing anisotropic mechanical properties;

[0029] (ε1 ε2 ε3 ε4 ε5 ε6) represents the strain tensor of anisotropic mechanical properties.

[0030] Preferably, a model in which elastic parameters are affected by anisotropy is established, specifically including:

[0031] Establish a linear relationship to express the relationship between stress and strain within a certain limit. kl is the strain tensor, σ ij represents the stress tensor, C ijkl It is called the stiffness tensor, or the elastic matrix. The elements in the elastic matrix are called elastic stiffness constants, generally referred to as elastic constants. The value range of the subscripts (i, j, k, l) in the equation is 1, 2, and 3. Each stress and strain tensor contains 9 components, so the elastic matrix has 81 components. In the generalized Hooke's law formula, the subscripts 1, 2, and 3 represent the directions of the three coordinate axes x, y, and z, respectively. The unit of stress is N / m 2 , which represents the stress per unit area, and this unit can also be expressed in terms of pressure; strain is a quantity without units, so the units of the elastic matrix components are the same as those of stress.

[0032] Preferably, a model in which Young's modulus and Poisson's ratio are affected by anisotropy is established, specifically, a relationship between the crack inclination angle and the longitudinal wave reflection coefficient is established.

[0033] Preferably, under the condition that the fracture density remains unchanged, when the fracture layer is filled with fluid, the change trend of the longitudinal wave reflection coefficient curve is similar with the change of the fracture inclination angle, and the intercept change rate does not exceed 10%; when the fracture layer is a gas-bearing fracture, as the fracture inclination angle increases, the intercept of the longitudinal wave reflection coefficient generally increases, and its gradient also changes greatly. Under the condition that the fracture property is a gas-bearing fracture, as the fracture density continues to increase, the intercept of the longitudinal wave reflection coefficient gradually decreases, and the change trend of the longitudinal wave reflection coefficient with Young's modulus and Poisson's ratio is also different.

[0034] Preferably, the elastic perturbation coefficient equation is parameter optimized and transformed to establish differential approximations of different orders, specifically including:

[0035] The elastic dynamics constitutive equation, the differential equation of motion and the geometric equation are the three basic elastic dynamics equations, which respectively describe the relationship between the displacement, strain and stress of particles in elastic media. Based on the above three elastic dynamics equations, they are modified into differential approximations of different orders, among which the first-order, second-order and third-order approximations are as follows:

[0036] The first-order approximation expressed in terms of the elastic perturbation coefficient is:

[0037]

[0038] The second-order approximation expressed in terms of the elastic perturbation coefficient is:

[0039]

[0040] The third-order approximation expressed in terms of the elastic perturbation coefficient is:

[0041]

[0042] The first-order approximation expressed in terms of reflectivity is:

[0043]

[0044] The second-order approximation expressed in reflectivity terms is:

[0045]

[0046] The third-order approximation expressed in reflectivity terms is:

[0047]

[0048] in,

[0049] R PP1 is the elastic disturbance coefficient;

[0050] R PP2 is the reflectivity term;

[0051] The weighting factors in the approximate formula expressed as elastic perturbation coefficients are expressed in the general form W ai Indicated by, where i is an integer from 1 to 19; the weighting factors in the approximate formula expressed in terms of reflectivity are expressed in the general form Represents, where j is an integer from 1 to 19;

[0052] a f 、a μ 、a ρ are the perturbation coefficients of the fluid term, shear modulus and density respectively;

[0053] Δf, Δμ and Δρ are the differences in fluid term, shear modulus and density between the upper and lower layers of the medium at the interface, respectively;

[0054] f, μ and ρ are the average values ​​of the fluid term, shear modulus and density of the upper and lower layers of the medium at the interface, respectively.

[0055] Preferably, the specific expressions of the weighting factors in the approximate formula represented by the elastic perturbation coefficient are:

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] in,

[0076] θ0 is the average value of the incident angle and the transmission angle;

[0077] (γ0) sat , (γ0) dry are the average values ​​of the longitudinal and shear wave velocity ratios in saturated media and dry media, respectively.

[0078] Preferably, the specific expressions of the weighting factors in the approximate formula represented by the reflectivity term are:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] in,

[0099] θ0 is the average value of the incident angle and the transmission angle;

[0100] (γ0) sat , (γ0) dry are the average values ​​of the longitudinal and shear wave velocity ratios in saturated media and dry media, respectively.

[0101] Preferably, different fracture densities and different fracture inclination conditions are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders.

[0102] Preferably, different fracture densities and different fracture inclination conditions are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders, specifically including:

[0103] Preferably, different fracture densities and different fracture inclination conditions are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders, specifically including:

[0104] First-order linear approximation:

[0105] The first-order linear approximation contains a first-order correction term The second-order approximation includes the correction term The third-order approximation includes the correction term

[0106] The approximation includes an approximation of a perturbation coefficient representation and an approximation of a reflectivity term representation;

[0107] in, and Respectively, the reflection coefficient R PP The first-order, second-order, and third-order terms of

[0108] Substitute each weighting factor into different approximate formulas to obtain the fitting results.

[0109] The present invention provides an anisotropy-based seismic data imaging system, comprising an imaging module. The imaging module uses any of the above-mentioned anisotropy-based seismic data imaging methods to perform imaging processing on seismic data.

[0110] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0111] 1. Based on the study of anisotropy, the present invention improves the high-order differential operator equations and establishes vertical lateral medium anisotropy and lateral medium isotropy models based on improved differential approximations of different orders; the established vertical lateral medium anisotropy and lateral medium isotropy models are used to perform seismic data imaging. The present invention can effectively process the earth medium physical properties with drastic changes in the vertical and horizontal directions, and has advantages in improving the signal-to-noise ratio of seismic data, improving the seismic imaging effect, improving the imaging accuracy of the fracture system, and highlighting the seismic reflection characteristics of the target layer. It can not only outline the hidden cracks, but also improve the resolution of the cracks. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 (a) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=1 and conglomerate transverse elasticity CL=100; Figure 1 (b) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=10 and conglomerate transverse elastic parameter CL=100; Figure 1 (c) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=20 and conglomerate transverse elastic parameter CL=100; Figure 1(d) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=30 and conglomerate transverse elastic parameter CL=100; Figure 1 (e) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=40 and conglomerate transverse elastic parameter CL=100; Figure 1 (f) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=50 and conglomerate transverse elastic parameter CL=100; Figure 1 (g) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=10 and conglomerate transverse elastic parameter CL=50; Figure 1 (h) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=10 and conglomerate transverse elastic parameter CL=200; Figure 1 (i) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=10 and conglomerate transverse elastic parameter CL=500; Figure 1 (j) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=10 and conglomerate transverse elastic parameter CL=1000; Figure 1 (k) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr=10 and conglomerate transverse elastic parameter CL=5000; Figure 1 (i) is a schematic diagram of Young's modulus distribution when Poisson's ratio vr = 10 and conglomerate transverse elastic parameter CL = 10000;

[0113] Figure 2 (a) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 1 and conglomerate lateral elastic parameter CL = 100; Figure 2 (b) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 100; Figure 2 (c) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 20 and conglomerate lateral elastic parameter CL = 100; Figure 2 (d) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 30 and conglomerate lateral elastic parameter CL = 100; Figure 2 (e) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 40 and conglomerate lateral elastic parameter CL = 100; Figure 2 (f) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 50 and conglomerate lateral elastic parameter CL = 100; Figure 2 (g) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 50; Figure 2 (h) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 200; Figure 2 (i) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 500; Figure 2(j) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 1000; Figure 2 (k) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 5000; Figure 2 (i) is a schematic diagram of Poisson's ratio distribution when Poisson's ratio vr = 10 and conglomerate lateral elastic parameter CL = 10000;

[0114] Figure 3 (a) is a schematic diagram of a vertical transverse medium (VTI medium) anisotropic model under a crack density e=0 according to an embodiment of the present invention. Figure 3 (b) is a schematic diagram of a vertical transverse medium (VTI medium) anisotropic model of an embodiment of the present invention at a crack density e=0.02. Figure 3 (c) is a schematic diagram of a vertical transverse medium (VTI medium) anisotropic model of an embodiment of the present invention at a crack density e=0.05. Figure 3 (d) is a schematic diagram of a vertical transverse medium (VTI medium) anisotropic model under a crack density e=0.10 according to an embodiment of the present invention;

[0115] Figure 4 (a) is a schematic diagram of a transverse medium (TTI medium) anisotropic model under a crack density e=0 according to an embodiment of the present invention. Figure 4 (b) is a schematic diagram of a transverse medium (TTI medium) anisotropic model under a crack density e=0.01 according to an embodiment of the present invention. Figure 4 (c) is a schematic diagram of a transverse medium (TTI medium) anisotropic model under a crack density of e=0.03 according to an embodiment of the present invention. Figure 4 (d) is a schematic diagram of a transverse medium (TTI medium) anisotropic model under a crack density of e=0.07 according to an embodiment of the present invention;

[0116] Figure 5 (a) is a diagram showing the effect of processing the seismic data of the gather 200 using the anisotropy-based seismic data imaging method of the present invention; Figure 5 (b) is a diagram showing the effect of processing the seismic data of the gather 300 using the anisotropy-based seismic data imaging method of the present invention; Figure 5 (c) is a diagram showing the effect of processing the seismic data of gather 400 using the anisotropy-based seismic data imaging method of the present invention; Figure 5 (d) is a diagram showing the effect of processing the seismic data of gather 500 using the anisotropy-based seismic data imaging method of the present invention;

[0117] Figure 6The present invention is a flowchart of a method for anisotropy-based seismic data imaging according to an embodiment of the present invention. DETAILED DESCRIPTION

[0118] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0119] The present invention provides a seismic data imaging method based on anisotropy, comprising the following steps:

[0120] Determine the quantitative characterization of the complex anisotropic mechanical properties of tight conglomerate reservoirs;

[0121] Modeling elastic parameters affected by anisotropy;

[0122] Model the effects of anisotropy on Young's modulus and Poisson's ratio;

[0123] Optimize and transform the parameters of the elastic disturbance coefficient equation and establish differential approximations of different orders;

[0124] Based on the improved differential approximations of different orders, the vertical lateral medium anisotropy and lateral medium isotropy models are established;

[0125] The established vertical lateral medium anisotropy and lateral medium isotropy models are used for seismic data imaging.

[0126] According to a specific embodiment of the present invention, the complex anisotropic mechanical properties of the tight conglomerate reservoir include: transverse anisotropy, longitudinal anisotropy, permeability and Young's modulus mechanical property parameters.

[0127] According to a specific implementation scheme of the present invention, the index system established to meet the characteristics of tight oil reservoirs in the study area is specifically that Young's modulus and Poisson's ratio are positively correlated, and the main elastic parameters are related functions.

[0128] According to a specific embodiment of the present invention, the stress tensor and the strain tensor are considered to quantitatively characterize the complex anisotropic mechanical properties of the tight conglomerate reservoir.

[0129] According to a specific embodiment of the present invention, the following form is used to quantitatively characterize the complex anisotropic mechanical properties of tight conglomerate reservoirs:

[0130] σ=C×ε

[0131] in,

[0132] σ represents the stress tensor vector;

[0133] C represents the anisotropic mechanical property matrix;

[0134] ε is the strain tensor vector of anisotropic mechanical properties.

[0135] According to a specific embodiment of the present invention, the model in which elastic parameters are affected by anisotropy is quantitatively characterized in the following form:

[0136]

[0137] in,

[0138] (σ1 σ2 σ3 σ4 σ5 σ6) represents the stress tensor of anisotropic mechanical properties;

[0139] Elastic parameter matrix representing anisotropic mechanical properties;

[0140] (ε1 ε2 ε3 ε4 ε5 ε6) represents the strain tensor of anisotropic mechanical properties.

[0141] According to a specific embodiment of the present invention, a model in which elastic parameters are affected by anisotropy is established, which specifically includes:

[0142] Establish a linear relationship to express the relationship between stress and strain within a certain limit. kl is the strain tensor, σ ij represents the stress tensor, C ijkl It is called the stiffness tensor, or the elastic matrix. The elements in the elastic matrix are called elastic stiffness constants, generally referred to as elastic constants. The value range of the subscripts (i, j, k, l) in the equation is 1, 2, and 3. Each stress and strain tensor contains 9 components, so the elastic matrix has 81 components. In the generalized Hooke's law formula, the subscripts 1, 2, and 3 represent the directions of the three coordinate axes x, y, and z, respectively. The unit of stress is N / m 2 , which represents the stress per unit area, and this unit can also be expressed in terms of pressure; strain is a quantity without units, so the units of the elastic matrix components are the same as those of stress.

[0143] According to a specific embodiment of the present invention, a model in which Young's modulus and Poisson's ratio are affected by anisotropy is established, specifically, the relationship between the crack inclination angle and the longitudinal wave reflection coefficient is established.

[0144] According to a specific embodiment of the present invention, under the condition that the fracture density remains unchanged, when the fracture layer is filled with fluid, as the fracture inclination changes, the change trend of the longitudinal wave reflection coefficient curve is similar, and its intercept change rate does not exceed 10%; when the fracture layer is a gas-containing fracture, as the fracture inclination increases, the longitudinal wave reflection coefficient intercept generally shows an increasing trend, and its gradient also changes greatly. Under the condition that the fracture property is a gas-containing fracture, as the fracture density continues to increase, the intercept of the longitudinal wave reflection coefficient gradually decreases, and the longitudinal wave reflection coefficient also has different changing trends with Young's modulus and Poisson's ratio.

[0145] According to a specific embodiment of the present invention, the elastic perturbation coefficient equation is parameter optimized and transformed to establish differential approximations of different orders, specifically including:

[0146] The elastic dynamics constitutive equation, the differential equation of motion and the geometric equation are the three basic elastic dynamics equations, which respectively describe the relationship between the displacement, strain and stress of particles in elastic media. Based on the above three elastic dynamics equations, they are modified into differential approximations of different orders, among which the first-order, second-order and third-order approximations are as follows:

[0147] The first-order approximation expressed in terms of the elastic perturbation coefficient is:

[0148]

[0149] The second-order approximation expressed in terms of the elastic perturbation coefficient is:

[0150]

[0151] The third-order approximation expressed in terms of the elastic perturbation coefficient is:

[0152]

[0153] The first-order approximation expressed in terms of reflectivity is:

[0154]

[0155] The second-order approximation expressed in reflectivity terms is:

[0156]

[0157] The third-order approximation expressed in reflectivity terms is:

[0158]

[0159]

[0160] in,

[0161] R PP1is the elastic disturbance coefficient;

[0162] R PP2 is the reflectivity term;

[0163] The weighting factors in the approximate formula expressed as elastic perturbation coefficients are expressed in the general form W ai represents, where i is an integer from 1 to 19;

[0164] The weighting factors in the approximation expressed in terms of reflectivity are expressed in the general form Represents, where j is an integer from 1 to 19;

[0165] a f 、a μ 、a ρ are the perturbation coefficients of the fluid term, shear modulus and density respectively;

[0166] Δf, Δμ and Δρ are the differences in fluid term, shear modulus and density between the upper and lower layers of the medium at the interface, respectively;

[0167] f, μ and ρ are the average values ​​of the fluid term, shear modulus and density of the upper and lower layers of the medium at the interface, respectively.

[0168] According to a specific embodiment of the present invention, the specific expression of each weighting factor in the approximate formula represented by the elastic disturbance coefficient is:

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188] in,

[0189] θ0 is the average value of the incident angle and the transmission angle;

[0190] (γ0) sat , (γ0) dry are the average values ​​of the longitudinal and shear wave velocity ratios in saturated media and dry media, respectively.

[0191] According to a specific embodiment of the present invention, the specific expression of each weighting factor in the approximate formula represented by the reflectivity term is:

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211] in,

[0212] θ0 is the average value of the incident angle and the transmission angle;

[0213] (γ0) sat , (γ0) dry are the average values ​​of the longitudinal and shear wave velocity ratios in saturated media and dry media, respectively.

[0214] According to a specific embodiment of the present invention, different fracture densities and different fracture inclination conditions are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders.

[0215] According to a specific embodiment of the present invention, different fracture densities and different fracture inclination conditions are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders, specifically including:

[0216] First-order linear approximation:

[0217] The first-order linear approximation contains a first-order correction term The second-order approximation includes the correction term The third-order approximation includes the correction term

[0218] The approximation includes an approximation of a perturbation coefficient representation and an approximation of a reflectivity term representation;

[0219] in, and Respectively, the reflection coefficient R PP The first-order, second-order, and third-order terms of

[0220] Substitute each weighting factor into different approximate formulas to obtain the fitting results.

[0221] The present invention provides an anisotropy-based seismic data imaging system, comprising an imaging module. The imaging module uses any of the above-mentioned anisotropy-based seismic data imaging methods to perform imaging processing on seismic data.

[0222] Example 1

[0223] According to a specific embodiment of the present invention, the anisotropic seismic data imaging method of the present invention is described in detail below.

[0224] The present invention provides a seismic data imaging method based on anisotropy, comprising the following steps:

[0225] Determine the quantitative characterization of the complex anisotropic mechanical properties of tight conglomerate reservoirs;

[0226] Modeling elastic parameters affected by anisotropy;

[0227] Model the effects of anisotropy on Young's modulus and Poisson's ratio;

[0228] Optimize and transform the parameters of the elastic disturbance coefficient equation and establish differential approximations of different orders;

[0229] Based on the improved differential approximations of different orders, the vertical lateral medium anisotropy and lateral medium isotropy models are established;

[0230] The established vertical lateral medium anisotropy and lateral medium isotropy models are used for seismic data imaging.

[0231] Among them, the complex anisotropic mechanical properties of tight conglomerate reservoirs include: lateral anisotropy, longitudinal anisotropy, permeability and Young's modulus mechanical property parameters.

[0232] Among them, the index system established to meet the characteristics of tight oil reservoirs in the study area is specifically that Young's modulus and Poisson's ratio are positively correlated, and the main elastic parameters are related functions.

[0233] Among them, the stress tensor and strain tensor are considered to quantitatively characterize the complex anisotropic mechanical properties of tight conglomerate reservoirs.

[0234] Among them, the following form is used to quantitatively characterize the complex anisotropic mechanical properties of tight conglomerate reservoirs:

[0235] σ=C×ε

[0236] in,

[0237] σ represents the stress tensor vector;

[0238] C represents the anisotropic mechanical property matrix;

[0239] ε is the strain tensor vector of anisotropic mechanical properties.

[0240] Among them, the model in which elastic parameters are affected by anisotropy is quantitatively characterized in the following form:

[0241]

[0242] in,

[0243] (σ1 σ2 σ3 σ4 σ5 σ6) represents the stress tensor of anisotropic mechanical properties;

[0244] Elastic parameter matrix representing anisotropic mechanical properties;

[0245] (ε1 ε2 ε3 ε4 ε5 ε6) represents the strain tensor of anisotropic mechanical properties.

[0246] Among them, a model in which elastic parameters are affected by anisotropy is established, including:

[0247] Establish a linear relationship to express the relationship between stress and strain within a certain limit. kl is the strain tensor, σ ij represents the stress tensor, C ijkl It is called the stiffness tensor, or the elastic matrix. The elements in the elastic matrix are called elastic stiffness constants, generally referred to as elastic constants. The value range of the subscripts (i, j, k, l) in the equation is 1, 2, and 3. Each stress and strain tensor contains 9 components, so the elastic matrix has 81 components. In the generalized Hooke's law formula, the subscripts 1, 2, and 3 represent the directions of the three coordinate axes x, y, and z, respectively. The unit of stress is N / m 2 , which represents the stress per unit area, and this unit can also be expressed in terms of pressure; strain is a quantity without units, so the units of the elastic matrix components are the same as those of stress.

[0248] Among them, a model of the influence of anisotropy on Young's modulus and Poisson's ratio is established, specifically, the relationship between the crack inclination and the longitudinal wave reflection coefficient is established.

[0249] Among them, under the condition of constant fracture density, when the fracture layer is filled with fluid, with the change of fracture inclination, the change trend of the P-wave reflection coefficient curve is similar, and its intercept change rate does not exceed 10%; when the fracture layer is a gas-bearing fracture, with the increase of fracture inclination, the intercept of the P-wave reflection coefficient generally increases, and its gradient also changes greatly. Under the condition that the fracture property is a gas-bearing fracture, with the continuous increase of fracture density, the intercept of the P-wave reflection coefficient gradually decreases, and the change trend of the P-wave reflection coefficient with Young's modulus and Poisson's ratio is also different.

[0250] Among them, the elastic perturbation coefficient equation is optimized and transformed, and differential approximations of different orders are established, including:

[0251] The elastic dynamics constitutive equation, the differential equation of motion and the geometric equation are the three basic elastic dynamics equations, which respectively describe the relationship between the displacement, strain and stress of particles in elastic media. Based on the above three elastic dynamics equations, they are modified into differential approximations of different orders, among which the first-order, second-order and third-order approximations are as follows:

[0252] The first-order approximation expressed in terms of the elastic perturbation coefficient is:

[0253]

[0254] The second-order approximation expressed in terms of the elastic perturbation coefficient is:

[0255]

[0256] The third-order approximation expressed in terms of the elastic perturbation coefficient is:

[0257]

[0258] The first-order approximation expressed in terms of reflectivity is:

[0259]

[0260] The second-order approximation expressed in reflectivity terms is:

[0261]

[0262] The third-order approximation expressed in reflectivity terms is:

[0263]

[0264] in,

[0265] R PP1 is the elastic disturbance coefficient;

[0266] R PP2 is the reflectivity term;

[0267] The weighting factors in the approximate formula expressed as elastic perturbation coefficients are expressed in the general form W ai represents, where i is an integer from 1 to 19;

[0268] The weighting factors in the approximation expressed in terms of reflectivity are expressed in the general form Represents, where j is an integer from 1 to 19;

[0269] af 、a μ 、a ρ are the perturbation coefficients of the fluid term, shear modulus and density respectively;

[0270] Δf, Δμ and Δρ are the differences in fluid term, shear modulus and density between the upper and lower layers of the medium at the interface, respectively;

[0271] f, μ and ρ are the average values ​​of the fluid term, shear modulus and density of the upper and lower layers of the medium at the interface, respectively.

[0272] Among them, the specific expressions of each weighting factor in the approximate formula represented by the elastic perturbation coefficient are:

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279]

[0280]

[0281]

[0282]

[0283]

[0284]

[0285]

[0286]

[0287]

[0288]

[0289]

[0290]

[0291]

[0292]

[0293] Among them, the specific expressions of each weighting factor in the approximate formula represented by the reflectivity term are:

[0294]

[0295]

[0296]

[0297]

[0298]

[0299]

[0300]

[0301]

[0302]

[0303]

[0304]

[0305]

[0306]

[0307]

[0308]

[0309]

[0310]

[0311]

[0312] in,

[0313] θ0 is the average value of the incident angle and the transmission angle;

[0314] (γ0) sat , (γ0) dry are the average values ​​of the longitudinal and shear wave velocity ratios in saturated media and dry media, respectively.

[0315] Among them, different fracture densities and different fracture inclination conditions are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders, including:

[0316] First-order linear approximation:

[0317] The first-order linear approximation contains a first-order correction term The second-order approximation includes the correction term The third-order approximation includes the correction term

[0318] The approximation includes an approximation of a perturbation coefficient representation and an approximation of a reflectivity term representation;

[0319] in, and Respectively, the reflection coefficient R PP The first-order, second-order, and third-order terms of

[0320] Substitute each weighting factor into different approximate formulas to obtain the fitting results.

[0321] like Figure 5 (a)-(d) show that the accuracy of seismic data processed by the method of the present invention is effectively improved.

[0322] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A seismic data imaging method based on anisotropy, characterized in that: The steps include: Determine the quantitative characterization of the complex anisotropic mechanical properties of tight conglomerate reservoirs; Modeling elastic parameters affected by anisotropy; Model the effects of anisotropy on Young's modulus and Poisson's ratio; Optimize and transform the parameters of the elastic disturbance coefficient equation and establish differential approximations of different orders; Based on the improved differential approximations of different orders, the vertical lateral medium anisotropy and lateral medium isotropy models are established; The established vertical lateral medium anisotropy and lateral medium isotropy models are used for seismic data imaging.

2. The anisotropy-based seismic data imaging method according to claim 1, characterized in that: The complex anisotropic mechanical properties of tight conglomerate reservoirs include: lateral anisotropy, longitudinal anisotropy, permeability and Young's modulus mechanical property parameters.

3. The anisotropy-based seismic data imaging method according to claim 1, characterized in that: The index system established to meet the characteristics of tight oil reservoirs in the study area specifically shows that Young's modulus and Poisson's ratio are positively correlated, and the main elastic parameters are related functions.

4. The anisotropy-based seismic data imaging method according to claim 1, characterized in that: The stress tensor and strain tensor are considered to quantitatively characterize the complex anisotropic mechanical properties of tight conglomerate reservoirs.

5. The anisotropy-based seismic data imaging method according to claim 4, characterized in that: The quantitative characterization of the complex anisotropic mechanical properties of tight conglomerate reservoirs is carried out in the following form: σ=C×ε in, σ represents the stress tensor vector; C represents the anisotropic mechanical property matrix; ε is the strain tensor vector of anisotropic mechanical properties.

6. The anisotropy-based seismic data imaging method according to claim 5, characterized in that: The model in which elastic parameters are affected by anisotropy is quantitatively characterized in the following form: in, (σ1 σ2 σ3 σ4 σ5 σ6) represents the stress tensor of anisotropic mechanical properties; Elastic parameter matrix representing anisotropic mechanical properties; (ε1 ε2 ε3 ε4 ε5 ε6) represents the strain tensor of anisotropic mechanical properties.

7. The anisotropy-based seismic data imaging method according to claim 5, characterized in that: A model of the influence of anisotropy on Young's modulus and Poisson's ratio is established, specifically, the relationship between the crack inclination and the longitudinal wave reflection coefficient is established.

8. The anisotropy-based seismic data imaging method according to claim 1, characterized in that: The elastic perturbation coefficient equation is optimized and transformed, and differential approximations of different orders are established, including: The elastic dynamics constitutive equation, the differential equation of motion and the geometric equation are the three basic elastic dynamics equations, which respectively describe the relationship between the displacement, strain and stress of particles in elastic media. Based on the above three elastic dynamics equations, they are modified into differential approximations of different orders, among which the first-order, second-order and third-order approximations are as follows: The first-order approximation expressed in terms of the elastic perturbation coefficient is: The second-order approximation expressed in terms of the elastic perturbation coefficient is: The third-order approximation expressed in terms of the elastic perturbation coefficient is: The first-order approximation expressed in terms of reflectivity is: The second-order approximation expressed in reflectivity terms is: The third-order approximation expressed in reflectivity terms is: in, R PP1 is the elastic disturbance coefficient; R PP2 is the reflectivity term; The weighting factors in the approximate formula expressed as elastic perturbation coefficients are expressed in the general form W ai represents, where i is an integer from 1 to 19; The weighting factors in the approximation expressed in terms of reflectivity are expressed in the general form represents, where j is an integer from 1 to 19; a f 、a μ 、a ρ are the perturbation coefficients of the fluid term, shear modulus and density respectively; Δf, Δμ and Δρ are the differences in fluid term, shear modulus and density between the upper and lower layers of the medium at the interface, respectively; f, μ and ρ are the average values ​​of the fluid term, shear modulus and density of the upper and lower layers of the medium at the interface, respectively.

9. The anisotropy-based seismic data imaging method according to claim 8, characterized in that: Different fracture densities and fracture dip angles are set, and vertical lateral medium anisotropy and lateral medium isotropy models are established based on improved differential approximations of different orders.

10. A seismic data imaging system based on anisotropy, characterized in that: It comprises an imaging module, and the imaging module uses the anisotropy-based seismic data imaging method described in any one of claims 1 to 9 to perform imaging processing on seismic data.

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