Dynamic matching speed inversion method based on multiband fusion constraint

Abstract

The invention relates to the technical field of seismic data processing and reservoir inversion, in particular to a dynamic matching velocity inversion method based on multi-band fusion constraint, which introduces regional multi-band velocity constraint by creating a full-band fusion algorithm, fully excavates velocity information, and updates a rock physical quantity plate in real time by using a dynamic matching algorithm. The reservoir velocity inversion is optimized through alternate iteration, high-precision and high-resolution velocity modeling is achieved, the problem of a complex and changeable velocity field in a basin area is effectively solved, and powerful support is provided for construction fine implementation and well position target research.

Description

Technical Field

[0001] This invention relates to the field of seismic data processing and reservoir inversion technology, and more specifically, to a dynamic matching velocity inversion method based on multi-band fusion constraints. Background Technology

[0002] Marine exploration is a complex and systematic project. As exploration activities continue to advance into deeper and more complex geological environments, the exploration targets have gradually shifted from traditional conventional structural oil and gas reservoirs to more complex lithologic oil and gas reservoirs, significantly increasing the requirements for seismic data quality. In certain specific exploration areas, due to the influence of rugged seafloor topography, diapiric structures, and other special geological conditions, the quality of seismic data in some areas is poor, restricting the search for exploration targets. Despite multiple rounds of acquisition and processing, formation velocity recognition still faces a dual bottleneck: First, due to significant differences in frequency bands between seismic and well logging data—seismic data typically covers a lower frequency range, while well logging data contains more high-frequency information—this frequency band difference between seismic and well logging data makes velocity calibration difficult, resulting in a mismatch between imaging velocity and formation velocity. Second, reservoir inversion speed is limited by constraints on petrophysical parameters and insufficient fusion of multi-frequency velocity information, leading to low inversion accuracy and making it difficult to meet the needs of detailed characterization of lithologic oil and gas reservoirs. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, which has low reservoir inversion velocity accuracy and is difficult to meet the requirements for fine characterization of lithologic oil and gas reservoirs, this invention provides a dynamic matching velocity inversion method based on multi-band fusion constraints, which achieves high-precision and high-resolution velocity modeling and meets the requirements for fine characterization of lithologic oil and gas reservoirs.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a dynamic matching velocity inversion method based on multi-band fusion constraints, comprising the following steps: Step 1: Based on the initial rock physical quantity scale of the region, construct the Zoeppritz forward modeling equation with rock property factor constraints including shear wave velocity and density; Step 2: Using seismic data and regional multi-frequency velocity information, a regional multi-frequency constraint operator is introduced by creating a full-frequency fusion algorithm. At the same time, initial rock physical quantity constraints and stability operators are introduced to construct the objective functional. Step 3: Based on the objective functional, construct an augmented Lagrangian function and use a dynamic matching algorithm to update the rock physical quantity plate in real time. Alternately iterate and invert to optimize the formation velocity, and achieve high-precision and high-resolution velocity inversion.

[0005] This method, through the synergistic effect of steps one through three, forms a complete high-precision velocity inversion technology system. First, based on the existing initial rock physics scale for the region, the constructed Zoeppritz forward modeling equation can more accurately simulate the reflection characteristics of seismic waves at complex stratigraphic interfaces, reducing indirect errors caused by the lack of rock property constraints. Specifically, the initial rock physics scale generally includes the statistical relationships of P-wave velocity, S-wave velocity, and density of sandstone and mudstone at different depths. Then, by fusing low-frequency imaging velocities and high-frequency logging velocities from the region's seismic data, a multi-band fusion algorithm is used to weighted integrate velocity information from different frequency bands. Simultaneously, constraints from the initial rock physics scale and stability operators are introduced to ensure that the inversion results conform to regional lithological patterns and suppress high-frequency noise during the inversion process. The constructed objective functional balances the degree of data fit with the model's rationality. Finally, a dynamic matching algorithm is used to update the rock physics scale in real time. For example, when there is a deviation between the actual lithology of the stratigraphy and the initial scale, the correspondence between P-wave velocity, S-wave velocity, and density in the scale is adjusted iteratively to continuously approximate the actual stratigraphic velocity.

[0006] Preferably, in step one, the Zoeppritz equation is first linearized using the Shuey approximation, and then the Zoeppritz forward modeling equation is constructed.

[0007] Preferably, different incident angles are obtained by linearizing the Zoeppritz equation using the Shuey approximation. The longitudinal wave reflection coefficient is as follows:

[0008] in, For the longitudinal wave velocity, For transverse wave velocity, For density; Approximately And after simplification, we get:

[0009] in, It is a constant.

[0010] Preferably, based on different incident angles The relationship between the longitudinal wave reflection coefficients is given by introducing rock property factor constraints. , This yields a constraint term including rock physical property factors. , The formula for the longitudinal wave reflection coefficient will include rock physical property factor constraints. , The relationship of the longitudinal wave reflection coefficient is matrixed, and a wavelet matrix is ​​introduced. Construct the Zoeppritz forward modeling equation.

[0011] The preferred Zoeppritz forward equation is:

[0012] in, It is a first-order difference matrix. , , , , , .

[0013] Preferred target functional for:

[0014] in, Represents the stability operator term. Indicates low-frequency constraint terms. Represents high-frequency constraint terms. , These represent the rock physical property factor constraint terms. , coefficient, , Representing the physical quantities of rocks , initial value, Indicates the coefficients of the low-frequency constraint term. Represents the coefficients of high-frequency constraint terms. , These represent high-frequency logging speed and low-frequency imaging speed, respectively. Represents a stability operator.

[0015] Preferred, Choose a second-order stable operator: .

[0016] Preferably, the augmented Lagrangian function includes a Lagrangian multiplier vector, an adjoint variable, and a penalty factor.

[0017] Preferably, based on the objective functional Constructing augmented Lagrangian functions And use the Alternating Multiplier Method (ADMM) to dynamically match and update. , To update the rock physical quantity scale and optimize the iterative solution, we obtain:

[0018] in, Denotes the Lagrangian multiplier vector. Indicates the accompanying variable. Representing the penalty factor, the augmented Lagrangian function The solution yields:

[0019] in, , , For the identity matrix, By taking the natural index, updated rock physical property factors and high-precision velocity are obtained: .

[0020] Preferably, this method is applied to exploration areas within the same basin.

[0021] Compared with the prior art, the beneficial effects of the present invention are: 1. By creating a full-band fusion algorithm to introduce regional multi-band velocity constraints, fully mining velocity information, and using a dynamic matching algorithm to update the rock physical quantity plate in real time, the reservoir velocity inversion was iteratively optimized, achieving high-precision and high-resolution velocity modeling. This effectively addressed the complex and variable velocity field problem in the basin and provided strong support for the precise implementation of structural design and well location target research. 2. This invention fully integrates existing multi-band velocities such as imaging velocity and logging velocity in the reservoir velocity inversion process, which can achieve higher accuracy and higher resolution velocity inversion; 3. The dynamic updating of regional rock physical quantities in the reservoir velocity inversion process of this invention breaks through the limitation of fixed rock physical quantities compared with the traditional two-step velocity inversion method based on wave impedance and rock properties. 4. This invention utilizes a dynamic matching algorithm to complete iterative updates, ensuring efficiency while achieving high-precision and high-speed calculation. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a dynamic matching velocity inversion method based on multi-band fusion constraints according to the present invention. Figure 2 This is the implementation effect of the conventional two-step velocity inversion method based on wave impedance and rock properties in Example 3; Figure 3 This is an implementation effect diagram of the dynamic matching velocity inversion method based on multi-band fusion constraints of the present invention. Detailed Implementation

[0023] The accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. To better illustrate this embodiment, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings. The positional relationships described in the drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0024] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "long," and "short" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0025] The technical solution of the present invention will be further described in detail below through specific embodiments and in conjunction with the accompanying drawings: Example 1 like Figure 1 As shown, a dynamic matching velocity inversion method based on multi-band fusion constraints includes the following steps: Step 1: Based on the initial rock physical quantity scale of the region, construct the Zoeppritz forward modeling equation with rock property factor constraints including shear wave velocity and density; Step 2: Using seismic data and regional multi-frequency velocity information, a regional multi-frequency constraint operator is introduced by creating a full-frequency fusion algorithm. At the same time, initial rock physical quantity constraints and stability operators are introduced to construct the objective functional. Step 3: Based on the objective functional, construct an augmented Lagrangian function and use a dynamic matching algorithm to update the rock physical quantity plate in real time. Alternately iterate and invert to optimize the formation velocity, and achieve high-precision and high-resolution velocity inversion.

[0026] This method, through the synergistic effect of steps one through three, forms a complete high-precision velocity inversion technology system. First, based on the existing initial rock physics scale for the region, the constructed Zoeppritz forward modeling equation can more accurately simulate the reflection characteristics of seismic waves at complex stratigraphic interfaces, reducing indirect errors caused by the lack of rock property constraints. Specifically, the initial rock physics scale generally includes the statistical relationships of P-wave velocity, S-wave velocity, and density of sandstone and mudstone at different depths. Then, by fusing low-frequency imaging velocities and high-frequency logging velocities from the region's seismic data, a multi-band fusion algorithm is used to weighted integrate velocity information from different frequency bands. Simultaneously, constraints from the initial rock physics scale and stability operators are introduced to ensure that the inversion results conform to regional lithological patterns and suppress high-frequency noise during the inversion process. The constructed objective functional balances the degree of data fit with the model's rationality. Finally, a dynamic matching algorithm is used to update the rock physics scale in real time. For example, when there is a deviation between the actual lithology of the stratigraphy and the initial scale, the correspondence between P-wave velocity, S-wave velocity, and density in the scale is adjusted iteratively to continuously approximate the actual stratigraphic velocity.

[0027] The beneficial effects of this embodiment are as follows: by creating a full-band fusion algorithm to introduce regional multi-band velocity constraints, fully mining velocity information, and using a dynamic matching algorithm to update the rock physical quantity plate in real time, and iteratively optimizing the reservoir velocity inversion, high-precision and high-resolution velocity modeling is achieved, which effectively addresses the problem of complex and variable velocity fields in the basin and provides strong support for the precise implementation of the structure and the study of well location targets.

[0028] Example 2 This embodiment further defines the features of Embodiment 1, and its difference from Embodiment 1 lies in: Furthermore, this method can be applied to exploration areas within the same basin.

[0029] The method includes the following steps: Step 1: The reflection coefficient can be calculated using the precise Zoeppritz equation: (1) Although the Zoeppritz equation provides an accurate reflection coefficient, its numerical solution is overly complex and suffers from severe nonlinearity, resulting in poor applicability. Under the assumption that small changes in the elastic parameters are accurate, the Zoeppritz equation is linearized using the Shuey approximation, yielding: (2) in, For different incident angles The longitudinal wave reflection coefficient is below. For the longitudinal wave velocity, For transverse wave velocity, For density. Approximately ,get: (3) Simplifying, we get: (4) in, It is a constant.

[0030] Using the initial rock physical quantity scale for the region, rock property factor constraint terms are introduced. , ,get: (5) Simplifying, we get: (6) Consider different incident angles And by matrixing the reflection coefficients, we get: (7) in, Indicates the angle of incidence The reflection coefficient sequence below, , , , (8) A first-order difference matrix (9) Introducing the wavelet matrix Construct the forward equation, and we have (10) Step 2: Using seismic data and regional multi-frequency velocity information, multi-frequency constraints, initial rock physical quantity constraints, and stability operators are introduced to construct the objective functional using equation (10). ,get (11) in, Represents the stability operator term. Indicates low-frequency constraint terms. Represents high-frequency constraint terms. , These represent the rock physical property factor constraint terms. , coefficient, , Representing the physical quantities of rocks , initial value, Indicates the coefficients of the low-frequency constraint term. Represents the coefficients of high-frequency constraint terms. , These represent high-frequency logging speed and low-frequency imaging speed, respectively. To represent the stability operator, we choose the following second-order stability operator: (12) Step 3: Construct the augmented Lagrangian function based on equation (11) Meanwhile, to ensure computational efficiency, the Alternating Multiplier Method (ADMM) is used for dynamic matching and updating. , To achieve the updating of rock physical quantity scales and optimize iterative solutions, we have: (13) in, Denotes the Lagrangian multiplier vector. Indicates the accompanying variable. This represents the penalty factor. The final solution yields: (14) in, , , It is the identity matrix. For By taking the natural index, the updated rock physical property factors and high-precision velocity volume can be obtained: (15).

[0031] The remaining features and working principles of this embodiment are the same as those of Embodiment 1.

[0032] Example 3 In this embodiment, we use the traditional two-step velocity inversion method based on wave impedance and rock properties and the method of the present invention to perform velocity inversion on the target stratigraphy of a certain marine basin's three-dimensional seismic data, to demonstrate the effectiveness of the present invention.

[0033] Traditional two-step velocity inversion methods based on wave impedance and rock properties, such as Figure 2As shown, the upper part is the pure wave seismic profile; the middle part is the inverted P-wave impedance; and the lower part is the inverted P-wave velocity. The figure shows that the conventional method, based on the inverted wave impedance, combines regional petrophysical scales to construct the relationship between wave impedance and velocity, and then inverts the formation velocity. The inverted velocity results show a similar trend to the wave impedance inversion results. This method is affected by the accuracy of the inverted wave impedance and regional petrophysical scales, resulting in certain indirect errors. Furthermore, it does not integrate the low-frequency imaging velocity obtained from regional X-ray tomography with the well logging velocity, leading to relatively low accuracy in the velocity inversion results.

[0034] like Figure 3 As shown, the upper part is the pure wave seismic profile; the middle part is the seismic imaging velocity; and the lower part is the inverted P-wave velocity. The figure illustrates the velocity inversion results using dynamic matching velocity inversion based on multi-band fusion constraints. As discussed above, this invention fully integrates regional multi-band velocity information during the velocity inversion process and updates the regional rock physical quantities during the inversion solution. The implementation results show that, compared to conventional methods, this invention achieves higher velocity inversion accuracy and more precise well-seismic velocity matching, strongly supporting detailed structural analysis and well location target research. In conclusion, the dynamic matching velocity inversion method based on multi-band fusion constraints is considered effective, providing more accurate reservoir velocities and supporting exploration research.

[0035] In the specific implementation of the above embodiments, the technical features can be combined in any non-contradictory way. For the sake of brevity, not all possible combinations of the above technical features are described. However, as long as the combination of these technical features is not contradictory, it should be considered to be within the scope of this specification.

[0036] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A dynamic matching velocity inversion method based on multi-band fusion constraints, characterized in that, Includes the following steps: Step 1: Based on the initial rock physical quantity scale of the region, construct the Zoeppritz forward modeling equation with rock property factor constraints including shear wave velocity and density; Step 2: Using seismic data and regional multi-frequency band velocity information, a regional multi-frequency band constraint operator is introduced by creating a full-frequency band fusion algorithm. At the same time, initial rock physical quantity constraints and stability operators are introduced to construct the objective functional. Step 3: Based on the objective functional, construct an augmented Lagrangian function and use a dynamic matching algorithm to update the rock physical quantity plate in real time. Alternately iterate and invert to optimize the formation velocity, and achieve high-precision and high-resolution velocity inversion.

2. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 1, characterized in that: In step one, the Zoeppritz equation is first linearized using the Shuey approximation, and then the Zoeppritz forward equation is constructed.

3. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 2, characterized in that: Different incident angles were obtained by linearizing the Zoeppritz equation using the Shuey approximation. The longitudinal wave reflection coefficient is as follows: in, For the longitudinal wave velocity, For transverse wave velocity, For density; Approximately And after simplification, we get: in, It is a constant.

4. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 3, characterized in that: Based on different incident angles The relationship between the longitudinal wave reflection coefficients is given by introducing rock property factor constraints. , This yields a constraint term including rock physical property factors. , The formula for the longitudinal wave reflection coefficient will include rock physical property factor constraints. , The relationship of the longitudinal wave reflection coefficient is matrixed, and a wavelet matrix is ​​introduced. Construct the Zoeppritz forward modeling equation.

5. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 4, characterized in that: The Zoeppritz forward modeling equation is: in, It is a first-order difference matrix. , , , , , .

6. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 5, characterized in that: Target functional for: in, Represents the stability operator term. Indicates low-frequency constraint terms. Represents high-frequency constraint terms. , These represent the rock physical property factor constraint terms. , coefficient, , Representing the physical quantities of rocks , initial value, Indicates the coefficients of the low-frequency constraint term. Represents the coefficients of high-frequency constraint terms. , These represent high-frequency logging speed and low-frequency imaging speed, respectively. Represents a stability operator.

7. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 6, characterized in that: Choose a second-order stable operator: 。 8. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 6, characterized in that: The augmented Lagrangian function includes a Lagrangian multiplier vector, an adjoint variable, and a penalty factor.

9. The dynamic matching velocity inversion method based on multi-band fusion constraints according to claim 8, characterized in that: Based on the objective functional Constructing augmented Lagrangian functions And use the Alternating Multiplier Method (ADMM) to dynamically match and update. , To update the rock physical quantity scale and optimize the iterative solution, we obtain: in, Denotes the Lagrangian multiplier vector. Indicates the accompanying variable, Representing the penalty factor, the augmented Lagrangian function The solution yields: in, , , For the identity matrix, By taking the natural index, updated rock physical property factors and high-precision velocity are obtained: 。 10. A dynamic matching velocity inversion method based on multi-band fusion constraints according to any one of claims 1-9, characterized in that: This method is applicable to exploration areas within the same basin.

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