An elastic modulus inversion method based on diffracted wave information

Abstract

This invention discloses a method for elastic modulus inversion based on diffraction wave information. The method includes: acquiring a three-dimensional seismic data volume of diffraction waves; obtaining near-, middle-, and far-field angularly stacked data of diffraction waves based on the three-dimensional seismic data volume; obtaining a P-wave diffraction coefficient formula suitable for pre-stack inversion based on the P-wave reflection coefficient formula; determining the diffraction coefficient based on the P-wave diffraction coefficient formula; obtaining near-, middle-, and far-field angularly stacked data of the whole wavefield based on layer interpretation; obtaining wavelet data corresponding to near-, middle-, and far-field gathers based on the near-, middle-, and far-field angularly stacked data of the whole wavefield; converting the layer velocity in the whole-wavefield seismic processing into layer velocity P-wave and S-wave modulus and density; using the layer velocity P-wave and S-wave modulus and density as background parameters of the inversion objective function; constructing and inverting the diffraction wave elastic modulus inversion objective function based on the wavelet data and diffraction coefficients; obtaining the elastic modulus anomaly in the target layer caused by heterogeneous disturbance by solving the objective function; and using the elastic modulus anomaly to identify and predict heterogeneous bodies in the strata.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of oil and gas geophysical exploration technology, and in particular to an elastic modulus inversion method based on diffraction wave information. Background Technology

[0002] Heterogeneous bodies such as fractured-cavity bodies in carbonate reservoirs and fractures in buried-hill fractured reservoirs are important hydrocarbon reservoirs and have been a focus of exploration in recent years. The seismic response of heterogeneous bodies exhibits weak-energy diffraction wave characteristics of varying magnitudes and energies, which merge with the strong-energy layered reflection wave characteristics of large stratigraphic interfaces to form a full-wavefield seismic profile. Domestic and international scholars have conducted research on the differences in wavefield characteristics, developing diffraction wave separation and imaging methods, which have been successfully applied to practical production. Compared to reflected wave data, diffraction wave data provides higher resolution imaging of heterogeneous bodies. Combining seismic attributes such as coherence, curvature, tensor, and amplitude variation rate can enable qualitative prediction of heterogeneous bodies. However, the diffraction wave elastic parameter inversion technique for quantitative prediction of heterogeneous bodies using diffraction wave data still lacks a fully developed inversion theory and urgently needs further development.

[0003] Conventional elastic parameter inversion methods, under the assumption of a horizontally homogeneous layered medium, describe the propagation laws of seismic wave reflection and transmission when seismic waves are incident on the elastic parameter interface using the exact Zoeppritz equation or its approximation. This allows for the inversion of formation elastic parameter information using pre-stack seismic gathers. For layered formations such as tight sandstone, clastic rocks, and shale, this layered medium assumption is reasonable, and conventional elastic parameter inversion methods are applicable. However, for highly heterogeneous formations such as carbonate fracture-vuggy reservoirs and buried hill fractured reservoirs, seismic waves encountering heterogeneous bodies during propagation will generate diffracted waves. This means that the reflection wave equation established under the homogeneous layered medium assumption cannot meet the requirements for inverting elastic parameters from heterogeneous bodies using diffracted wave data. Summary of the Invention

[0004] To address the aforementioned technical problems, at least one embodiment of the present invention provides a method for inverting the elastic modulus based on diffraction wave information, thereby solving the problems.

[0005] In some optional embodiments, the method includes the following steps:

[0006] Diffraction wave separation is performed on the full-wavefield 3D seismic data volume to obtain the diffraction wave 3D seismic data volume;

[0007] Based on the three-dimensional seismic data volume of the diffraction wave, angular superposition data of the near, middle and far parts of the diffraction wave are obtained;

[0008] Based on the formula for the longitudinal wave reflection coefficient, a formula for the longitudinal wave diffraction coefficient applicable to pre-stack inversion is obtained, and the diffraction coefficient is determined based on the formula for the longitudinal wave diffraction coefficient.

[0009] Based on the layer interpretation, near, middle and far part angular superposition data of the whole wavefield are obtained, and wavelet data corresponding to near, middle and far gathers are obtained respectively based on the near, middle and far part angular superposition data of the whole wavefield.

[0010] Based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area, the layer velocity in the full-wavefield seismic processing is converted into layer velocity P-wave and S-wave modulus and density.

[0011] The layer velocity P- and S-wave moduli and density are used as background parameters of the inversion objective function. Based on the wavelet data and diffraction coefficient, the diffracted wave elastic modulus inversion objective function is constructed and inverted. By solving the objective function, the elastic modulus anomaly caused by heterogeneous body disturbance in the target layer is obtained.

[0012] The elastic modulus anomaly is used to identify and predict heterogeneous bodies in the formation.

[0013] In some optional embodiments, the step of performing diffraction wave separation on the full-wavefield three-dimensional seismic data volume to obtain a diffraction wave three-dimensional seismic data volume includes:

[0014] Time-domain full-field 3D seismic data volume obtained by pre-stack time migration or pre-stack depth migration;

[0015] The full-wavefield three-dimensional seismic data volume is subjected to diffraction wave separation to obtain the diffraction wave three-dimensional seismic gather data volume.

[0016] In some optional embodiments, obtaining the near-, middle-, and far-field angular stacking data of the diffracted wave based on the three-dimensional seismic data volume of the diffracted wave includes:

[0017] Based on full-wave field gather data, the angular stacking scheme of the full-wave field 3D seismic data volume is determined according to the range of P-wave incident angles of the target layer.

[0018] Based on the angular stacking scheme of the full-field three-dimensional seismic data volume, the diffracted wave three-dimensional seismic data volume is processed to obtain angular stacking data of the near, middle and far parts of the diffracted wave.

[0019] In some optional embodiments, the step of obtaining full-wavelength near-, mid-, and far-field partial angular stacked data based on torsional interpretation, and obtaining wavelet data corresponding to near-, mid-, and far-field gathers based on the full-wavelength near-, mid-, and far-field partial angular stacked data respectively, includes:

[0020] Based on the contact relationships of the strata and the characteristics of the wave group, stratigraphic interpretation is carried out according to the wave crests or troughs to obtain the stratigraphic position of the target segment;

[0021] Well-seismic calibration is performed based on well logging data to determine the correspondence between the full-wavefield angle-separated superimposed gathers and the phase axis of the well-logged synthetic seismic record. Wavelet data corresponding to near, middle and far gathers are extracted based on the correspondence.

[0022] In some optional embodiments, the step of converting the layer velocity in the full-wavefield seismic processing into layer velocity P-wave modulus and density based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area includes:

[0023] Cross-interaction analysis and fitting were performed on the P-wave velocity and shear wave velocity and density in the study area to obtain the conversion relationship between P-wave velocity and shear wave velocity and density.

[0024] Based on the aforementioned conversion relationship, the layer velocity in the full-wavefield seismic processing is converted into layer velocity P-wave modulus and density.

[0025] In some alternative embodiments, vv uses a Bayesian inversion method to solve the objective function.

[0026] In some alternative embodiments, the elastic modulus anomaly includes the diffracted wave P-wave modulus and density.

[0027] At least one embodiment of the present invention also provides an elastic modulus inversion device based on diffraction wave information, characterized in that it comprises:

[0028] The diffraction wave separation module is used to perform diffraction wave separation on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic data volume;

[0029] The angle-splitting overlay module is used to obtain angle-splitting overlay data of the near, middle and far parts of the diffraction wave based on the three-dimensional seismic data volume of the diffraction wave.

[0030] The diffraction coefficient determination module is used to obtain a longitudinal wave diffraction coefficient formula applicable to pre-stack inversion based on the longitudinal wave reflection coefficient formula, and to determine the diffraction coefficient based on the longitudinal wave diffraction coefficient formula.

[0031] The wavelet data extraction module is used to obtain near-field, mid-field, and far-field partial angular superposition data of the whole wave field based on the stratigraphic interpretation, and to obtain the wavelet data corresponding to the near-field, mid-field, and far-field gathers respectively based on the near-field, mid-field, and far-field partial angular superposition data of the whole wave field.

[0032] The elastic parameter conversion module is used to convert the layer velocity in the full-wavefield seismic processing into layer velocity P-wave modulus and density based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area.

[0033] The objective function inversion module is used to use the layer velocity P-wave modulus and density as background parameters of the inversion objective function, construct and invert the diffracted wave elastic modulus inversion objective function based on the wavelet data and diffraction coefficient, and obtain the elastic modulus anomaly caused by heterogeneous body disturbance in the target layer by solving the objective function.

[0034] The heterogeneity prediction module is used to identify and predict heterogeneity in the formation using the elastic modulus anomaly.

[0035] At least one embodiment of the present invention also provides an electronic device, characterized in that it comprises:

[0036] At least one processor; and,

[0037] A memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the elastic modulus inversion method based on diffraction wave information as described above.

[0039] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the elastic modulus inversion method based on diffraction wave information as described above.

[0040] At least one embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the elastic modulus inversion method based on diffraction wave information as described above.

[0041] Compared with the prior art, the elastic modulus inversion method based on diffraction wave information provided by the embodiments of the present invention has the following beneficial effects:

[0042] This invention combines the high-resolution imaging advantages of diffraction wave data for heterogeneous bodies, constructs an objective function for diffraction wave elastic modulus inversion based on the diffraction coefficient, solves the objective function using the Bayesian method, and uses the layer velocity modulus as the background value of the objective function to form a diffraction wave elastic modulus inversion method. This allows for the acquisition of elastic modulus anomalies in the target strata caused by heterogeneous disturbances, enabling quantitative prediction of heterogeneous bodies and providing strong data support for oil and gas geophysical exploration of heterogeneous geological anomalies in the study area. Attached Figure Description

[0043] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0044] Figure 1 This is a flowchart of the steps of the method used in the embodiments of the present invention;

[0045] Figure 2 This is a schematic diagram of a time-domain full-wavefield seismic overlay profile used in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of a time-domain diffraction wave seismic full-stack profile used in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the near-angle superposition profile of the time-domain diffracted wave used in an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the angle superposition profile in the time-domain diffracted wave used in an embodiment of the present invention;

[0049] Figure 6 This is a schematic diagram of the far-angle superposition profile of the time-domain diffracted wave used in an embodiment of the present invention;

[0050] Figure 7 This is a schematic diagram of the time-domain layer velocity profile used in an embodiment of the present invention;

[0051] Figure 8 This is a schematic diagram of the time-domain layer velocity longitudinal wave modulus profile used in an embodiment of the present invention;

[0052] Figure 9 This is a schematic diagram of the time-domain layer velocity shear wave modulus profile used in an embodiment of the present invention;

[0053] Figure 10 This is a schematic diagram of the temporal layer velocity density profile used in an embodiment of the present invention;

[0054] Figure 11 This is a schematic diagram of the longitudinal wave modulus profile obtained by time-domain diffraction wave inversion used in an embodiment of the present invention;

[0055] Figure 12 This is a schematic diagram of the time-domain diffraction wave inversion transverse wave modulus profile used in an embodiment of the present invention;

[0056] Figure 13 This is a schematic diagram of the time-domain diffraction wave inversion density profile used in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0058] As mentioned earlier, conventional elastic parameter inversion methods are based on the assumption of horizontally homogeneous layered media, and the established reflection wave equations cannot meet the requirements for elastic parameter inversion of heterogeneous bodies using diffraction wave data. Addressing the inapplicability of conventional elastic parameter inversion methods and the lack of theoretical support for diffraction wave elastic parameter inversion, and leveraging the high-resolution imaging advantages of diffraction wave data for heterogeneous bodies, a new elastic modulus inversion method based on diffraction wave information is proposed. This method first derives a diffraction coefficient formula applicable to heterogeneous reservoirs and constructs an objective function for diffraction wave elastic modulus inversion. Then, a Bayesian method is used to solve the objective function. Finally, the elastic modulus obtained from seismic layer velocity conversion is used as the background modulus of the inversion objective function to perform diffraction wave elastic modulus inversion, obtaining the elastic modulus anomalies caused by heterogeneous disturbances in the target formation, thus achieving quantitative prediction of heterogeneous bodies.

[0059] The implementation details of the above method are described in detail below through examples. The following content is only for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0060] Example 1:

[0061] like Figure 1 As shown, this embodiment provides a method for inverting the elastic modulus based on diffraction wave information. This method mainly constructs an objective function for inverting the elastic modulus of diffraction waves based on diffraction coefficients, and uses a Bayesian method to solve the objective function, thereby achieving the inversion of the elastic modulus based on diffraction wave information. The method of this invention includes three processes: constructing the objective function for inverting the elastic modulus of diffraction waves, solving the objective function, and inverting the elastic modulus of diffraction waves.

[0062] like Figure 1 As shown, the method includes the following steps:

[0063] (1) Diffraction wave separation is performed on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic angle gather data volume;

[0064] (2) Based on the angle-separated superposition scheme of the full-wave field gather data, the diffraction wave angle gather is processed to obtain the near, middle and far part superimposed diffraction wave data after angle-separated superposition.

[0065] (3) Based on the full-wave field superimposed data, perform stratigraphic interpretation to obtain the stratigraphic position of the target segment; based on the near, middle and far part superimposed data of the full-wave field, perform well-seismic calibration to obtain the comprehensive wavelet corresponding to the near, middle and far gathers respectively;

[0066] (4) Cross-intersection analysis and fitting were performed on the P-wave velocity and S-wave velocity and density of the well logging in the study area to obtain the conversion relationship between P-wave velocity and S-wave velocity and density. Based on the conversion relationship, the layer velocity in the whole wave field seismic processing was converted into layer velocity P-wave and S-wave modulus and density.

[0067] (5) Obtain the formula for the longitudinal wave diffraction coefficient applicable to pre-stack inversion based on the formula for the longitudinal wave reflection coefficient, and determine the diffraction coefficient based on the formula for the longitudinal wave diffraction coefficient; (step (5) can also be performed after step (2))

[0068] (6) Select the Bayesian inversion method to solve the objective function of the elastic modulus inversion of the diffracted wave;

[0069] (7) The layer velocity P-wave modulus and density are used as background parameters of the inversion objective function and participate in the objective function construction and inversion process. Diffraction wave elastic modulus inversion is carried out to obtain the elastic modulus anomaly caused by heterogeneous body disturbance in the target layer.

[0070] (8) By utilizing the elastic modulus anomaly caused by the disturbance of the heterogeneous body in the formation (in this embodiment, the elastic modulus of diffraction waves, such as the longitudinal and transverse wave modulus and density of diffraction waves), the heterogeneous body of the strong heterogeneous strata can be identified and predicted.

[0071] In this embodiment, step (1) above is implemented as follows:

[0072] Given time-domain full-wavelength seismic data obtained by pre-stack time migration or pre-stack depth migration, diffraction wave separation is performed on the full-wavelength 3D seismic data volume to obtain the diffraction wave 3D seismic gather data volume.

[0073] In this embodiment, step (2) above is implemented as follows:

[0074] Based on the full-wave field gather data, and according to the range of P-wave incident angles in the target layer, a segmented stacking scheme for the full-wave field gather data is determined, resulting in segmented stacked full-wave field data for near, middle, and far segments. Following the same segmented stacking scheme, segmented stacked diffracted wave data for near, middle, and far segments are obtained.

[0075] In this embodiment, step (3) above is implemented as follows:

[0076] Based on full-field, full-stack data, and according to the contact relationships and wave group characteristics of the strata, stratigraphic interpretation is performed according to wave crests or troughs to obtain the stratigraphic position of the target segment. Fine-grained well-seismic calibration is performed based on well logging data to determine the correspondence between the phase axes of the full-field, angle-separated stacked gathers and the synthetic well-logged seismic records. Synthetic wavelets corresponding to near, middle, and far gathers are extracted for use in the inversion of full-field and diffraction wave data.

[0077] In this embodiment, step (4) above is implemented as follows:

[0078] Intersection analysis of actual well logging curves in the study area yielded the fitting relationships between P-wave velocity and S-wave velocity and density, and further, the conversion relationships between layer velocity and P-wave modulus, S-wave modulus, and density were obtained. These conversion relationships allow the layer velocity data volume generated during full-field seismic data processing to be converted into layer velocity P-wave modulus, S-wave modulus, and density.

[0079] m vel =f vel_m (Vel)

[0080] μ vel =f vel_μ (Vel)

[0081] ρ vel =f vel_ρ (Vel)

[0082] In the formula: Vel represents the longitudinal wave velocity (layer velocity), and the unit is m / s;

[0083] f vel_m (Vel), f vel_μ (Vel), f vel_ρ (Vel) represents the conversion relationship between layer velocity and P-wave modulus, S-wave modulus, and density, respectively.

[0084] m vel μ vel ρ vel These are the layer velocity longitudinal wave modulus, transverse wave modulus, and density, respectively.

[0085] In this embodiment, step (5) above is implemented as follows:

[0086] Derivation of the formula for longitudinal wave diffraction coefficient:

[0087] According to the generation mechanism and propagation law of diffraction waves, when a reflected wave propagates in a highly heterogeneous stratum, it will generate a diffracted wave when it is incident at a certain angle at the location of the heterogeneous body (the diffraction point). This diffracted wave then propagates outwards from that location as a new seismic source. When the reflected wave is incident at the diffraction point at an angle θ, the P-wave diffraction coefficient R... diff (θ) is shown in the following formula:

[0088]

[0089]

[0090] In the formula, m, μ, and ρ are the longitudinal wave modulus, transverse wave modulus, and density of the non-uniform medium, m0, μ0, and ρ0 are the longitudinal wave modulus, transverse wave modulus, and density of the background medium, and Δm, Δμ, and Δρ are the longitudinal wave modulus, transverse wave modulus, and density disturbances caused by the non-uniform medium.

[0091] To facilitate inversion, based on the P-wave and S-wave transformation relationship, we obtain...

[0092] R diff (θ)=A(θ)L M +B(θ)L μ +C(θ)L ρ

[0093]

[0094] In this embodiment, step (6) above is implemented as follows:

[0095] Within the framework of Bayesian theory, the model parameters to be inverted are assumed to be the ratio of the longitudinal wave modulus, transverse wave modulus, and density of the perturbed medium to the background medium. It is assumed that the model parameters follow a Cauchy distribution and the likelihood function follows a Gaussian distribution. A posterior probability distribution function is established, and the initial model constraint of layer velocity is adopted and multiple iterations are performed to improve the inversion stability.

[0096] When the number of offsets (or incident angles) is M and the number of time sampling points is N, the longitudinal wave diffraction coefficient is in the form of a system of equations:

[0097]

[0098] For the first offset distance (incident angle)

[0099]

[0100] T represents the transpose of the matrix, and the second to Mth offsets (incident angles) follow the same pattern.

[0101] Considering the influence of wavelets, the equations for the longitudinal wave diffraction coefficients can be simplified as follows:

[0102] DNM×1 =G NM×3N ·L 3N×1

[0103] Where G is the forward operator matrix and L is the parameter to be inverted.

[0104] Assuming the likelihood function follows a Gaussian distribution and the parameters to be inverted follow a Cauchy distribution, then the posterior probability distribution of the parameters to be inverted is:

[0105]

[0106] Where D represents the pre-stack earthquake record. For noise variance, Let M be the variance of the parameters to be inverted, M be the number of model parameters, n be the number of data sampling points, and L be the variance of the parameters to be inverted. i Let be the parameters of the i-th model to be inverted.

[0107] The objective function that maximizes the posterior probability distribution in the above equation is:

[0108]

[0109] By adding a smooth initial model constraint and minimizing the objective function, we can obtain...

[0110]

[0111] The above formula can be simplified to KL = ψ. Where, λ ρ , λ μ , λ m These are the longitudinal wave modulus, the transverse wave modulus, and the density constraint coefficient, respectively. For Cauchy constraint terms,

[0112]

[0113] For Cauchy term, P m P μ P ρ Represents the model parameter constraint matrix, η m =m0, η μ =μ0, η ρ =ρ0 is the initial model for smoothing the parameters to be inverted.

[0114] Damped singular value decomposition is a relatively accurate method for solving geophysical inversion problems. The matrix K in equation (20) is decomposed into SVD as follows:

[0115] K = UΛV T

[0116] Where Λ is a diagonal matrix, and its diagonal elements are the non-zero singular values ​​of matrix K, i.e.

[0117]

[0118] The above formula can be derived through simple mathematical derivation.

[0119] L est =VΛ -1 U T Ψ

[0120] Among them, L est These are the estimated values ​​of the parameters to be inverted.

[0121] From the above formula, we can obtain the parameters L to be inverted in the first iteration. m1 ,L μ1 and L ρ1 Taking the longitudinal wave modulus as an example, the initial iteration result of the longitudinal wave modulus parameter m1 in a non-uniform medium can be expressed as:

[0122] m1 = L m1 m0 + m0 = m0(1 + L) m1 )

[0123] Where m0 represents the longitudinal wave modulus parameter of the background medium. To improve the inversion accuracy, a cyclic iteration strategy can be adopted, that is, the previous inversion result is used as the background medium parameter for the next iteration. Then, after the t-th iteration, the longitudinal wave modulus parameter m0 of the non-uniform medium is obtained. t for

[0124]

[0125] Similarly, the transverse wave modulus and density parameters of the non-uniform medium can be obtained as follows:

[0126]

[0127] In this embodiment, step (7) above is implemented as follows:

[0128] By using the layer velocity P-wave modulus and density as background elastic parameters for the inversion objective function, the strip-like anomalies and "bull's-eye" anomalies present in well logging interpolation models are avoided. These background elastic parameters participate in the objective function construction and inversion process, enabling diffraction wave elastic modulus inversion to obtain elastic modulus anomalies in the target formation caused by heterogeneous disturbances.

[0129] The P-wave diffraction coefficient in step (4) includes the P-wave and S-wave moduli and density terms of the background medium. The background values ​​of elastic parameters are also needed in the process of solving the objective function in step (5). First, the P-wave and S-wave moduli and density of the layer velocity obtained in step (3) are used in the objective function construction and inversion process. Then, the Bayesian inversion method in step (5) is used to solve the objective function of diffraction elastic parameter inversion, and the elastic modulus inversion of diffraction is carried out to obtain the elastic modulus anomaly caused by the disturbance of heterogeneous bodies in the target layer.

[0130] In this embodiment, step (8) above is implemented as follows:

[0131] Using the diffraction wave elastic modulus obtained by inversion in step (6), i.e. the elastic modulus anomaly caused by the disturbance of the heterogeneous body of the formation, geological anomalies are identified and predicted for reservoirs with strong heterogeneity, such as fracture-cavity bodies in carbonate reservoirs, fracture-type buried hill reservoirs, and fracture identification in tight sandstone reservoirs.

[0132] Example 2

[0133] The technical solution and its beneficial effects of the present invention will be further illustrated below with a specific example. This embodiment takes a carbonate fracture-vuggy reservoir in the Tarim Basin of Northwest China as an example, and uses the method provided by the present invention to perform diffraction impedance inversion and fracture-vuggy body identification.

[0134] To fully utilize the high-resolution detail information of diffraction wave data and improve the accuracy of identifying and predicting inhomogeneous bodies, a method for inverting elastic modulus wave impedance based on diffraction wave information is proposed, along with its specific implementation process, such as... Figure 1 As shown.

[0135] In step (1), Figure 2 The example demonstrates a full-wavefield seismic overlay data profile with a time domain range of 3.4s-3.9s and a duration of 0.5s. The profile shows that the fractures and cavities in the carbonate reservoir exhibit a "beaded" diffraction seismic response characteristic, which is highly heterogeneous. Figure 3 The paper presents a fully superimposed diffraction wave data profile obtained using relevant mature diffraction wave separation methods. The diffraction waves have high resolution and rich detail information, which can better characterize the detailed features of the development of the cavities.

[0136] In step (2), the incident angle range of the target layer full-wave field gather data is 1–34°, which can be superimposed according to the angle-splitting scheme of 1–14°, 11–24°, and 21–34°. The diffracted wave gather storage is processed according to the angle-splitting scheme of the full-wave field to obtain the near-field diffracted wave data. Figure 4 ),middle( Figure 5 ),Far( Figure 6 ) Data profile superimposed at different angles.

[0137] In step (3), based on the full-wavefield three-dimensional seismic data, the stratigraphic interpretation is carried out according to the contact relationship of the strata and the wave group characteristics, according to the wave crests. Figures 2 to 3 The blue curve represents the T74 layer, the top boundary of the Ordovician Yijianfang Formation, as interpreted from full-wavelength seismic data. From... Figure 2 Full-field imaging profiles show that there are high-energy layered reflection waves near T74, which mask the diffraction wave characteristics of the heterogeneous body. This is due to the large impedance difference between the Yijianfang Formation and the overlying strata. Figure 3The diffraction wave imaging profile shown removes the shielding of high-energy layered reflected waves, making the detailed features of the heterogeneous body clearer. Fine-grained well-seismic calibration was performed based on well-logging data from the study area to determine the correspondence between the phase axes of the original full-wavefield seismic record and the synthesized well-logging seismic record. The composite wavelet was extracted from the near, middle, and far-angle stacked data of the full-wavefield. For both full-wavefield and diffraction wave data, the wavelet is only source-dependent, while the composite wavelet is applicable to both; therefore, the composite wavelet can be used for diffraction wave impedance inversion.

[0138] In step (4), an intersection analysis is performed on the actual well logging curves of the study area to obtain the fitting relationship between P-wave velocity and S-wave velocity and density, and then the conversion relationship between layer velocity and P-wave modulus, S-wave modulus, and density is obtained. Through the conversion relationship, the layer velocity data volume generated during the full-field seismic data processing can be transformed into a single data volume. Figure 7 ) converted to layer velocity longitudinal wave modulus ( Figure 8 ), transverse wave modulus ( Figure 9 ),density( Figure 10 ).

[0139] In step (5), based on the diffraction coefficient, a diffraction wave impedance inversion objective function is constructed. The diffraction wave elastic modulus in fractured-vuggy reservoirs represents the elastic modulus anomaly relative to the background medium caused by the disturbance of fractured-vuggy bodies. The Bayesian method is used to solve the diffraction wave elastic modulus inversion objective function. During the solution process, based on Bayesian theory, a least squares error function is established to make the synthesized diffraction wave seismic record close to the actual diffraction wave imaging data.

[0140] In step (6), the P-wave modulus, S-wave modulus, and density of the layer velocity from step (4) are used as background values ​​for the fractured-vuggy reservoir and substituted into the diffraction coefficients in step (5). Based on the synthesized wavelet from step (3) and using the Bayesian method from step (5), the objective function for diffraction elastic modulus inversion in step (5) is solved to perform diffraction elastic modulus inversion and obtain the elastic modulus anomaly caused by heterogeneous disturbance in the target layer.

[0141] In step (7), according to Figures 8 to 10 The layer velocity, P-wave modulus, S-wave modulus, and density shown are used as background values ​​for fractured-vuggy reservoirs. Figures 4 to 6 The diffracted wave near-, mid-, and far-field angular superposition data profiles are shown. The elastic modulus of the diffracted wave is inverted to obtain the longitudinal wave modulus (P / E). Figure 11 ), transverse wave modulus ( Figure 12 ),density( Figure 13 Inversion results.

[0142] In step (8), Figures 11 to 13The fractured cavity exhibits a low-resistivity anomaly on the diffracted wave P-wave modulus profile. Based on the fact that the layer velocity modulus is consistent with the actual conditions of carbonate formations, and by effectively removing strong energy reflection interfaces and the layered reflection phase axes of the underlying strata, the anomaly in the elastic modulus caused by heterogeneous disturbance is highlighted, enabling quantitative prediction of the heterogeneous body. The prediction results are consistent with the actual drilling conditions, improving the accuracy of elastic modulus inversion in heterogeneous reservoirs.

[0143] The above embodiments fully illustrate that the method provided by the present invention combines the advantages of high-resolution imaging of heterogeneous bodies with diffraction wave data, constructs a diffraction wave elastic modulus inversion objective function based on the diffraction coefficient, solves the inversion objective function using the Bayesian method, and uses the layer velocity modulus as the background value of the inversion objective function to form a diffraction wave elastic modulus inversion method. This allows for the acquisition of elastic modulus anomalies in the target strata caused by heterogeneous disturbances, enabling quantitative prediction of heterogeneous bodies and providing strong data support for the geophysical exploration of oil and gas anomalies in the study area.

[0144] Example 3

[0145] Another embodiment of the present invention relates to an elastic modulus inversion device based on diffraction wave information, comprising:

[0146] The diffraction wave separation module is used to perform diffraction wave separation on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic data volume;

[0147] The angle-splitting overlay module is used to obtain angle-splitting overlay data of the near, middle and far parts of the diffraction wave based on the three-dimensional seismic data volume of the diffraction wave.

[0148] The diffraction coefficient determination module is used to obtain a longitudinal wave diffraction coefficient formula applicable to pre-stack inversion based on the longitudinal wave reflection coefficient formula, and to determine the diffraction coefficient based on the longitudinal wave diffraction coefficient formula.

[0149] The wavelet data extraction module is used to obtain near-field, mid-field, and far-field partial angular superposition data of the whole wave field based on the stratigraphic interpretation, and to obtain the wavelet data corresponding to the near-field, mid-field, and far-field gathers respectively based on the near-field, mid-field, and far-field partial angular superposition data of the whole wave field.

[0150] The elastic parameter conversion module is used to convert the layer velocity in the full-wavefield seismic processing into layer velocity P-wave modulus and density based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area.

[0151] The objective function inversion module is used to use the layer velocity P-wave modulus and density as background parameters of the inversion objective function, construct and invert the diffracted wave elastic modulus inversion objective function based on the wavelet data and diffraction coefficient, and obtain the elastic modulus anomaly caused by heterogeneous body disturbance in the target layer by solving the objective function.

[0152] The heterogeneity prediction module is used to identify and predict heterogeneity in the formation using the elastic modulus anomaly.

[0153] Example 4:

[0154] Another embodiment of the present invention relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the elastic modulus inversion method based on diffraction wave information in the above embodiments.

[0155] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0156] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0157] Example 5:

[0158] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the elastic modulus inversion method based on diffraction wave information described in the above embodiments.

[0159] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] Example 6

[0161] Another embodiment of the present invention relates to a computer program product, including a computer program that, when executed by a processor, implements the steps of the elastic modulus inversion method based on diffraction wave information described in the above embodiments.

[0162] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for inverting the elastic modulus based on diffraction wave information, characterized in that, include: Diffraction wave separation is performed on the full-wavefield 3D seismic data volume to obtain the diffraction wave 3D seismic data volume; Based on the three-dimensional seismic data volume of the diffraction wave, angular superposition data of the near, middle and far parts of the diffraction wave are obtained; Based on the formula for the longitudinal wave reflection coefficient, a formula for the longitudinal wave diffraction coefficient applicable to pre-stack inversion is obtained, and the diffraction coefficient is determined based on the formula for the longitudinal wave diffraction coefficient. Based on the layer interpretation, near, middle and far part angular superposition data of the whole wavefield are obtained, and wavelet data corresponding to near, middle and far gathers are obtained respectively based on the near, middle and far part angular superposition data of the whole wavefield. Based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area, the layer velocity in the full-wavefield seismic processing is converted into layer velocity P-wave and S-wave modulus and density. The layer velocity P- and S-wave moduli and density are used as background parameters of the inversion objective function. Based on the wavelet data and diffraction coefficient, the diffracted wave elastic modulus inversion objective function is constructed and inverted. By solving the objective function, the elastic modulus anomaly caused by heterogeneous body disturbance in the target layer is obtained. The elastic modulus anomaly is used to identify and predict heterogeneous bodies in the formation.

2. The elastic modulus inversion method based on diffraction wave information according to claim 1, characterized in that, The process of performing diffraction wave separation on the full-wavefield 3D seismic data volume to obtain a diffracted 3D seismic data volume includes: Time-domain full-field 3D seismic data volume obtained by pre-stack time migration or pre-stack depth migration; The full-wavefield three-dimensional seismic data volume is subjected to diffraction wave separation to obtain the diffraction wave three-dimensional seismic gather data volume.

3. The elastic modulus inversion method based on diffraction wave information according to claim 1, characterized in that, The step of obtaining angularly stacked data of the near, middle, and far portions of the diffracted wave based on the three-dimensional seismic data volume of the diffracted wave includes: Based on full-wave field gather data, the angular stacking scheme of the full-wave field 3D seismic data volume is determined according to the range of P-wave incident angles of the target layer. Based on the angular stacking scheme of the full-field three-dimensional seismic data volume, the diffracted wave three-dimensional seismic data volume is processed to obtain angular stacking data of the near, middle and far parts of the diffracted wave.

4. The elastic modulus inversion method based on diffraction wave information according to claim 1, characterized in that, The process involves obtaining near-, mid-, and far-field partial angular stacking data of the full-wavelength field based on torsional interpretation, and then obtaining wavelet data corresponding to the near-, mid-, and far-field gathers based on the near-, mid-, and far-field partial angular stacking data, including: Based on the contact relationships of the strata and the characteristics of the wave group, stratigraphic interpretation is carried out according to the wave crests or troughs to obtain the stratigraphic position of the target segment; Well-seismic calibration is performed based on well logging data to determine the correspondence between the full-wavefield angle-separated superimposed gathers and the phase axis of the well-logged synthetic seismic record. Wavelet data corresponding to near, middle and far gathers are extracted based on the correspondence.

5. The elastic modulus inversion method based on diffraction wave information according to claim 1, characterized in that, The process of converting layer velocities in the full-wavefield seismic processing into layer velocity P-wave and S-wave moduli and densities based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area includes: Cross-interaction analysis and fitting were performed on the P-wave velocity and shear wave velocity and density in the study area to obtain the conversion relationship between P-wave velocity and shear wave velocity and density. Based on the aforementioned conversion relationship, the layer velocity in the full-wavefield seismic processing is converted into layer velocity P-wave modulus and density.

6. The elastic modulus inversion method based on diffraction wave information according to claim 1, characterized in that, The objective function is solved using the Bayesian inversion method.

7. The elastic modulus inversion method based on diffraction wave information according to claim 1, characterized in that, The elastic modulus anomaly includes the diffracted wave modulus and density.

8. An elastic modulus inversion device based on diffraction wave information, characterized in that, include: The diffraction wave separation module is used to perform diffraction wave separation on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic data volume; The angle-splitting overlay module is used to obtain angle-splitting overlay data of the near, middle and far parts of the diffraction wave based on the three-dimensional seismic data volume of the diffraction wave. The diffraction coefficient determination module is used to obtain a longitudinal wave diffraction coefficient formula applicable to pre-stack inversion based on the longitudinal wave reflection coefficient formula, and to determine the diffraction coefficient based on the longitudinal wave diffraction coefficient formula. The wavelet data extraction module is used to obtain near-field, mid-field, and far-field partial angular superposition data of the whole wave field based on the stratigraphic interpretation, and to obtain the wavelet data corresponding to the near-field, mid-field, and far-field gathers respectively based on the near-field, mid-field, and far-field partial angular superposition data of the whole wave field. The elastic parameter conversion module is used to convert the layer velocity in the full-wavefield seismic processing into layer velocity P-wave modulus and density based on the conversion relationship between P-wave velocity and S-wave velocity and density in the well logging of the study area. The objective function inversion module is used to use the layer velocity P-wave modulus and density as background parameters of the inversion objective function, construct and invert the diffracted wave elastic modulus inversion objective function based on the wavelet data and diffraction coefficient, and obtain the elastic modulus anomaly caused by heterogeneous body disturbance in the target layer by solving the objective function. The heterogeneity prediction module is used to identify and predict heterogeneity in the formation using the elastic modulus anomaly.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the elastic modulus inversion method based on diffraction wave information as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the elastic modulus inversion method based on diffraction wave information as described in any one of claims 1 to 7.

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