Downhole distributed fiber anisotropic shale reservoir guided wave dispersion curve inversion method

Abstract

The present application relates to a kind of well down distributed fiber (Distributed Acoustic Sensing, DAS) anisotropic shale reservoir guided wave dispersion curve inversion method and medium, comprising: obtaining the guided wave seismic data of shale reservoir, and according to the guided wave seismic data obtained, the guided wave dispersion energy diagram is calculated, and the observed guided wave dispersion curve is extracted from it;Establish the initial anisotropic elastic parameter model of the shale reservoir;According to the initial or updated anisotropic elastic parameter model of the shale reservoir, theoretical guided wave dispersion curve is obtained by forward;Construct objective function, according to the residual between theoretical guided wave dispersion curve and observed guided wave dispersion curve, calculate and judge whether objective function converges;If objective function converges, then output the anisotropic elastic parameter model of current shale reservoir;If objective function does not converge, then anisotropic elastic parameter model is updated using gradient algorithm until objective function converges;Output the optimized shale reservoir anisotropic elastic parameter model.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and particularly to the field of seismic inversion technology, specifically the application of downhole distributed fiber optic sensing technology in anisotropic shale reservoirs. More specifically, this invention proposes a method and medium for inverting guided wave dispersion curves in anisotropic shale reservoirs. Background Technology

[0002] In geological exploration technology, inversion technology is a branch that uses observed physical phenomena to infer spatial or structural information about a medium.

[0003] Shale often contains flaky particles rich in clay minerals and a certain amount of organic matter. Due to sedimentation and compaction, shale has a layered structure, resulting in different elastic properties in different directions, thus exhibiting anisotropy.

[0004] In existing geological exploration inversion calculation methods, one type of algorithm typically assumes isotropy for the geological characteristics of some complex reservoirs. Clearly, this assumption cannot accurately describe the geological characteristics of these complex layers. Subsurface structure models obtained based on the isotropic medium assumption will deviate from reality, affecting reservoir evaluation and prediction.

[0005] Another class of existing inversion methods also considers anisotropy. However, existing methods for inverting guided wave dispersion curves in elastic anisotropic media are computationally inefficient. For example, the Monte Carlo stochastic inversion method requires a large number of random samples of the model, resulting in a very large computational load. Multiple iterations are usually required before finding the global optimum, leading to slow convergence. Especially when the number of iterations is insufficient, the stability of the results is poor, making it difficult to achieve rapid and high-precision imaging and characterization of shale layers. Summary of the Invention

[0006] To address the aforementioned problems, the purpose of this invention is to provide a method and medium for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs, which can calculate high-precision structural information of shale layers more quickly compared to existing inversion methods.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs, the method comprising:

[0009] S1. Obtain guided wave seismic data of shale reservoirs, calculate guided wave dispersion energy map based on the obtained guided wave seismic data, and extract the observed guided wave dispersion curve from it;

[0010] S2, Establish the initial anisotropic elastic parameter model of the shale reservoir;

[0011] S3. Based on the initial or updated anisotropic parameter model of the shale reservoir, the theoretical guided wave dispersion curve is obtained through forward modeling.

[0012] S4. Construct the objective function, calculate and determine whether the objective function converges based on the residual between the theoretical guided wave dispersion curve and the observed guided wave dispersion curve;

[0013] S5. If the objective function converges, output the anisotropic elastic parameter model of the current shale reservoir; if the objective function does not converge, update the anisotropic elastic parameter model using a gradient-based algorithm, and return to S3 and S4 until the objective function converges.

[0014] S6 outputs the optimized anisotropic elastic parameter model of the shale reservoir.

[0015] In one implementation, in S1, guided wave seismic data of the shale reservoir is acquired via a downhole distributed optical fiber system.

[0016] In one implementation, in S1, a guided wave dispersion energy map is calculated based on guided wave seismic data, and the observed guided wave dispersion curve is extracted from the guided wave dispersion energy map.

[0017] In one implementation, the formula for calculating the guided wave dispersion energy map is:

[0018]

[0019] Where N is the total number of DAS channels, Φ T,n For phase shift, This is guided wave seismic data in the space-frequency domain after amplitude normalization.

[0020] In one implementation, in S2, the anisotropic elastic parameter model of the shale reservoir is initialized based on geological information; the anisotropic elastic parameter model of the shale reservoir includes: vertical P-wave velocity, vertical S-wave velocity, density, and Thomsen anisotropic parameters ε and δ;

[0021] In one implementation, in S3, the theoretical guided wave dispersion curve is solved by the dispersion equation;

[0022] The dispersion equation is specifically as follows:

[0023]

[0024] in, The generalized reflection coefficient is related to the wave reflected from the (j-1)th interface into the j-th medium. The generalized reflection coefficient is related to the wave reflected from the j-th interface into the j-th layer of medium; the root of the dispersion equation for each layer is the theoretical guided wave dispersion curve corresponding to the current anisotropic elastic parameter model.

[0025] In one implementation, S4 includes:

[0026] The least squares function of the difference between the observed dispersion curve and the theoretical dispersion curve is used as the objective function;

[0027] In one implementation, the specific method for updating the anisotropic elastic parameters using a gradient-based algorithm in step S5 is as follows:

[0028] The gradient of the objective function with respect to the vertical shear wave velocity is calculated, and then the gradient descent direction and Wolfe criterion are used to determine the step size. The vertical shear wave velocity is updated using the linearized quasi-Newton method L-BFGS-B optimization method.

[0029] Specifically, the formula for calculating the objective function is as follows:

[0030]

[0031] Where ζ is the number of modes observed in the dispersion curve, ξ η To determine the number of dispersion points in the ηth mode of the dispersion curve, The phase velocity at frequency p of the ηth mode in the theoretical dispersion curve. To observe the phase velocity at frequency p of the ηth mode of the dispersion curve;

[0032] The formula for the gradient of the objective function is:

[0033]

[0034] in, k is the wave number, U is the group velocity, ρ is the density, and V is the group velocity. p0 It is the vertical longitudinal wave velocity, V s0 y1 is the vertical transverse wave velocity, and y2 are the horizontal and vertical component amplitudes of the harmonic solution, respectively.

[0035] The formula for calculating the gradient descent direction is:

[0036]

[0037] Where a is the iteration number, Λ (a) Is The approximate Hessian matrix at Ω (a) It is the objective function pair The gradient;

[0038] Wolfe's criterion is:

[0039]

[0040] in, For the objective function in The values ​​at the points b1, b2 ∈ (0, 1) are constants, and b1 <b2;

[0041] The linearized quasi-Newton method L-BFGS-B optimization method updates the vertical shear wave velocity formula as follows:

[0042]

[0043] in, It is the vertical shear wave velocity in the (a+1)th iteration, and β is the update step size. Is It is in the gradient descent direction.

[0044] In one implementation, after step S5, the method further includes: evaluating the reliability of the inversion result using a sensitivity function. The sensitivity function is calculated using the following formula:

[0045]

[0046] in, F ρ F ε and F δ These are the sensitivity functions of the guided wave dispersion curve to vertical shear wave velocity, vertical longitudinal wave velocity, density, and Thomsen parameters ε and δ, respectively.

[0047] Secondly, the present invention provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the downhole distributed optical fiber anisotropic shale reservoir guided wave dispersion curve inversion method described in the first aspect.

[0048] Compared with existing technologies, this invention, based on anisotropic elasticity theory, considers the influence of formation anisotropy, unlike traditional isotropic inversion methods. This allows for more accurate acquisition of the formation's vertical shear wave velocity structure. Vertical shear wave velocity is a key parameter in shale gas reservoir characterization, contributing to a deeper understanding of the reservoir's mechanical properties, stress state, and fracture distribution, thereby improving the accuracy of reservoir evaluation and the rationality of development decisions. By introducing anisotropic inversion technology, errors caused by the isotropic assumption can be effectively reduced, thus increasing the success rate and economic benefits of hydraulic fracturing in unconventional oil and gas development.

[0049] Meanwhile, this invention employs a linearized quasi-Newton method, L-BFGS-B optimization, for guided wave dispersion curve inversion. Compared to existing inversion methods, such as Monte Carlo stochastic inversion, this method can quickly determine the decreasing direction of the vertical shear wave velocity in the model, causing the objective function to converge rapidly to a local minimum. This significantly improves the convergence speed and computational efficiency, enabling rapid inversion of the vertical shear wave velocity structure of shale formations. In shale gas drilling and fracturing processes, when real-time adjustments to operational parameters are required, gradient inversion can quickly provide accurate reservoir parameter optimization results, contributing to improved operational efficiency and safety.

[0050] Meanwhile, this invention can analyze and calculate the sensitivity function of guided wave dispersion curve to formation density, vertical P-wave and S-wave velocities, and anisotropy parameters. By analyzing the sensitivity of guided wave dispersion curve to vertical S-wave velocity, the reliability of inversion results can be evaluated, which helps to interpret the inversion results and provides more accurate data support for shale gas development. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method for inverting the guided wave dispersion curve of anisotropic shale reservoirs using distributed optical fiber in a downhole embodiment of the present invention;

[0052] Figure 2 This refers to guided wave seismic data generated by hydraulic fracturing perforations and recorded by distributed optical fibers in a horizontal well, simulated by an anisotropic geological model in this embodiment of the invention.

[0053] Figure 3 The waveguide dispersion energy map and the extracted dispersion curve calculated using the phase shift method in this embodiment of the invention are shown.

[0054] Figure 4 This is a model result diagram of gradient inversion of the dispersion curve of anisotropic elastic medium guided wave in an embodiment of the present invention;

[0055] Figure 5 This is a graph showing the dispersion curve results obtained by gradient inversion of the dispersion curve of anisotropic elastic medium guided wave in an embodiment of the present invention.

[0056] Figure 6 The diagram shows the dispersion curve results obtained by inverting the dispersion curve of an isotropic elastic medium guided wave using existing technology.

[0057] Figure 7 The anisotropic elastic medium guided wave dispersion curve in this embodiment of the invention corresponds to the optimal vertical shear wave velocity V. s0 Sensitive functions;

[0058] Figure 8 The anisotropic elastic medium guided wave dispersion curve in this embodiment of the invention is shown as a function of the initial vertical longitudinal wave velocity V. p0 Sensitive functions;

[0059] Figure 9 This is the sensitivity function of the anisotropic elastic medium waveguide dispersion curve to the initial density ρ in the embodiments of the present invention;

[0060] Figure 10 The sensitivity function of the anisotropic elastic medium waveguide dispersion curve to the initial Thomsen anisotropy parameter ε in the embodiments of the present invention is used.

[0061] Figure 11 This is the sensitivity function of the anisotropic elastic medium guided wave dispersion curve to the initial Thomsen anisotropy parameter δ in the embodiments of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, this... Common technologies in the field All other embodiments obtained by personnel are within the scope of protection of this invention.

[0063] To address the problems of existing technologies, this invention provides a method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs. The method includes:

[0064] S1, acquire guided wave seismic data of shale reservoirs, and calculate the observed guided wave dispersion curve based on the acquired guided wave seismic data;

[0065] Specifically, guided wave seismic data of shale reservoirs are acquired through a downhole distributed optical fiber system; then, guided wave dispersion energy map is calculated based on the guided wave seismic data, and the observed guided wave dispersion curve is extracted from the guided wave dispersion energy map.

[0066] Shale layers, being low-velocity layers sandwiched within high-velocity surrounding rock, function as waveguide structures. P-waves and S-waves excited by hydraulic fracturing perforations or microseismic events within the shale layer are confined to propagate within the shale layer, undergoing constructive and destructive interference to form guided waves. Normal mode guided waves exhibit two modes: P-SV and SH, both possessing significant dispersion characteristics.

[0067] This invention employs a distributed fiber optic acoustic sensing system for data acquisition. The demodulator of the fiber optic device emits a series of optical signals that propagate along the fiber, encountering Rayleigh scattering at density inhomogeneities within the fiber. The acoustic disturbance in the fiber causes a phase change in the backscattered optical signal. The demodulator calculates this phase change using interferometry and converts it into the rate of change of strain. Guided waves propagating in shale reservoirs caused by hydraulic fracturing have very high frequencies and significant dispersion characteristics. Surface-deployed sensors can only detect very weak guided wave signals. Therefore, downhole sensors are needed to observe guided waves at close range in low-velocity layers. However, deploying seismic detectors downhole using existing data acquisition techniques is not only expensive but also extremely difficult. Furthermore, the high frequency of guided waves causes spurious frequency interference when recording data using traditional seismic detectors. Distributed fiber optic high-precision spatiotemporal sampling can guarantee spurious-free recording of high-frequency guided wave information, offering significant advantages in high-precision reservoir imaging.

[0068] The phase-shifting method was used to calculate the dispersion energy map of guided wave seismic data observed by downhole distributed optical fiber (DAS) and extract the observed guided wave dispersion curves. This included: transforming the seismic data to the space-frequency domain using a fast Fourier transform; for each given frequency, performing tilted superposition along different velocities in the spatial direction; and normalizing the total number of DAS channels. The formula for calculating the observed guided wave dispersion energy map is as follows:

[0069]

[0070] Where N is the total number of DAS channels, Φ T,n For phase shift, This is guided wave seismic data in the space-frequency domain after amplitude normalization.

[0071] S2, Establish the initial anisotropic elastic parameter model of the shale reservoir;

[0072] Specifically, an initial anisotropic elastic parameter model of the shale reservoir is established based on geological information, including vertical P-wave velocity, vertical S-wave velocity, density, and Thomsen anisotropic parameters ε and δ.

[0073] S3. Based on the initial or updated anisotropic elastic parameter model of the shale reservoir, the theoretical guided wave dispersion curve is obtained through forward modeling.

[0074] Specifically, the generalized reflection-transmission coefficient method is used to calculate the theoretical P-SV guided wave dispersion curves corresponding to the initial or updated anisotropic elastic parameter models. The dispersion equation expression corresponding to the generalized reflection-transmission coefficient method is:

[0075]

[0076] in, The generalized reflection coefficient is related to the wave reflected from the (j-1)th interface into the j-th medium. The generalized reflection coefficient is related to the wave of the down-going wave reflected from the j-th interface into the j-th layer of medium.

[0077] The root of the dispersion equation for each layer is the theoretical guided wave dispersion curve corresponding to the current structural information model.

[0078] S4. Construct the objective function, calculate and determine whether the objective function converges based on the residual between the theoretical guided wave dispersion curve and the observed guided wave dispersion curve;

[0079] Specifically, the formula for calculating the objective function is as follows:

[0080]

[0081] Where ζ is the number of modes observed in the dispersion curve, ξ η To determine the number of dispersion points in the ηth mode of the dispersion curve, The phase velocity at frequency p of the ηth mode in the theoretical dispersion curve. To observe the phase velocity at frequency p of the ηth mode of the dispersion curve.

[0082] S5. If the objective function converges, output the anisotropic elastic parameter model of the current shale reservoir; if the objective function does not converge, update the anisotropic elastic parameter model using a gradient-based algorithm, and return to S3 and S4 until the objective function converges.

[0083] Specifically, the steps for updating the anisotropic elastic parameter model using gradient-based algorithms are as follows: first, calculate the gradient of the objective function with respect to the vertical shear wave velocity; then, calculate the gradient descent direction and the Wolfe criterion to determine the step size; finally, use the linearized quasi-Newton method L-BFGS-B optimization method to update the vertical shear wave velocity.

[0084] The formula for the gradient of the objective function is:

[0085]

[0086] in, k is the wave number, U is the group velocity, ρ is the density, and V is the group velocity. p0 It is the vertical longitudinal wave velocity, V s0 y1 is the vertical transverse wave velocity, and y2 are the horizontal and vertical component amplitudes of the harmonic solution, respectively.

[0087] The formula for calculating the gradient descent direction is:

[0088]

[0089] Where a is the iteration number, Λ(a) Is The approximate Hessian matrix at Ω (a) It is the objective function pair The gradient;

[0090] Wolfe's criterion is:

[0091]

[0092] in, For the objective function in The values ​​at the points b1, b2 ∈ (0, 1) are constants, and b1 <b2;

[0093] The linearized quasi-Newton method L-BFGS-B optimization method updates the vertical shear wave velocity formula as follows:

[0094]

[0095] in, It is the vertical shear wave velocity in the (a+1)th iteration, and β is the update step size. Is It is in the gradient descent direction.

[0096] Furthermore, the sensitivity function of the guided wave dispersion curve to the optimized anisotropic elastic parameters is calculated, the influence of anisotropic elastic parameter perturbations on the guided wave dispersion curve is analyzed, and the reliability of the inversion results is evaluated. The formula for calculating the sensitivity function of the guided wave dispersion curve to the anisotropic elastic parameters is as follows:

[0097]

[0098] in, F ρ F ε and F δ These are the sensitivity functions of the guided wave dispersion curve to vertical transverse wave velocity, vertical longitudinal wave velocity, density, and Thomsen anisotropy parameters ε and δ, respectively.

[0099] S6 outputs the optimized anisotropic elastic parameter model of the shale reservoir.

[0100] The flow of the method provided in this application and its technical effects are illustrated below with reference to more detailed embodiments and accompanying drawings.

[0101] Example 1 discloses a method for inverting guided wave dispersion curves in downhole distributed fiber optic shale reservoirs, such as... Figure 1 As shown, the specific steps include the following:

[0102] Step 1: Obtain guided wave seismic data from horizontal well DAS observations using numerical simulation. Specifically, the model shown in Table 1 is used, and the model is discretized into 550×2260 grid points with a grid spacing of 0.5 meters and a time step of 4×10. -5 The seismic source and detectors were positioned 12.5 meters below the interface between layers 1 and 2. An interleaved grid finite difference method was used, with CPML absorbing boundaries. A vertical force source was used to simulate a hydraulic fracturing perforation. The excitation function was a Ricker wavelet with a dominant frequency of 400 Hz. The source-detector distance was 100 meters, the detector spacing was 1 meter, and there were 1000 channels. The total simulation time was 1 second, with a time sampling interval of 4 × 10⁻⁶. -4 The simulation is performed to obtain the horizontal velocity data of the detector particles in the horizontal well guided wave. Then, the simulated horizontal velocity of the detector particles is converted into DAS strain rate using a finite difference formula. The formula for converting the horizontal velocity of the detector particles into DAS strain rate is as follows:

[0103]

[0104] Where x is the distance from the DAS channel to the seismic source, and L is the DAS gauge length. In this embodiment, L is set to 2m. The guided wave seismic data from horizontal well distributed fiber optic observations obtained by converting simulated detector data is as follows: Figure 2 As shown.

[0105]

[0106] Table 1

[0107] Step 2: Calculate the guided wave dispersion energy map using the phase-shift method and extract the observed guided wave dispersion curve from it. Specifically, this involves: processing the seismic data... A one-dimensional Fourier transform along the time direction yields Transformed seismic data Normalizing the amplitude yields Then, by tilting and superimposing the given phase velocity and frequency, the guided wave dispersion energy map A is obtained. s (ω,c T ).like Figure 3 As shown, the background is a dispersion energy map, and the symbol "+" represents the extracted observation. Guided wave dispersion The expression for calculating the guided wave dispersion energy map using the phase-shift method is:

[0108]

[0109] Where N is the total number of DAS channels, Φ T,n For phase shift, This is the space-frequency domain seismic data after amplitude normalization.

[0110] Step 3: Establish the initial anisotropic elastic parameter model, including the thickness, density, vertical P-wave velocity, vertical S-wave velocity, and Thomsen anisotropic parameters ε and δ for each layer.

[0111] Step 4: Calculate the theoretical dispersion curve using the generalized reflection-transmission coefficient method. Specifically, define stress and displacement vectors for each layer. Use the relationship between the generalized reflection-transmission coefficient and the stress and displacement vectors to transmit the wave field. Starting from the upper half-infinite space, transmit the generalized reflection-transmission coefficient layer by layer. Apply boundary conditions in the lower half-space, i.e., the wave attenuates to zero at infinity, to obtain the dispersion equation. Select the frequency range of the observed dispersion curve as the frequency range for calculating the theoretical dispersion curve. Use numerical methods to derive the dispersion equation and find the phase velocity corresponding to each frequency. The expression for the dispersion equation corresponding to the generalized reflection-transmission coefficient method is:

[0112]

[0113] in, The generalized reflection coefficient is related to the wave reflected from the (j-1)th interface into the j-th medium. The generalized reflection coefficient is related to the wave reflected from the j-th interface into the j-th layer of medium; the root of the dispersion equation for each layer is the waveguide theoretical dispersion curve of the corresponding model.

[0114] Step 5: Use the least squares function of the difference between the observed dispersion curve and the theoretical dispersion curve as the objective function. Specifically, calculate the variance of the phase velocity at the corresponding modes and frequencies of the observed and theoretical dispersion curves, sum them, and then average them over the number of dispersion points and modes, respectively. The objective function expression is:

[0115]

[0116] Where ζ is the number of modes observed in the dispersion curve, ξ η To determine the number of dispersion points in the ηth mode of the dispersion curve, The phase velocity at frequency p of the ηth mode in the theoretical dispersion curve. To observe the phase velocity at frequency p of the ηth mode of the dispersion curve.

[0117] Step 6, calculate the gradient of the objective function. The expression for the gradient of the objective function is:

[0118]

[0119] in, k is the wave number, U is the group velocity, ρ is the density, and V is the group velocity. p0 It is the vertical longitudinal wave velocity, V s0 y1 is the vertical transverse wave velocity, and y2 are the horizontal and vertical component amplitudes of the harmonic solution, respectively.

[0120] Step 7: Determine whether the objective function or its gradient satisfies the given convergence condition. If the convergence condition is met, output the vertical transverse wave velocity; otherwise, proceed to the next step.

[0121] Step 8: Update the vertical shear wave velocity using the linearized quasi-Newton method L-BFGS-B optimization method. Specifically, the descent direction is calculated using the L-BFGS-B variant of the quasi-Newton method, with the step size determined based on the Wolfe criterion. The expression for updating the vertical shear wave velocity using the linearized quasi-Newton method L-BFGS-B optimization method is as follows:

[0122]

[0123] in, It is the vertical shear wave velocity in the (a+1)th iteration, and β is the update step size. Is The gradient descent direction is determined by the following expression:

[0124]

[0125] Among them, Λ (a) Is The approximate Hessian matrix at Ω (a) It is the objective function pair The gradient. The step size β satisfies the Wolfe criterion, that is:

[0126]

[0127] in, For the objective function in The values ​​at the points b1, b2 ∈ (0, 1) are constants, and b1 <b2。

[0128] Step 9: Repeat steps 4 through 7 until the objective function reaches the predetermined convergence criterion. After iteration, output the optimized vertical shear wave velocity. Figure 4 and Figure 5 The results of vertical shear wave velocity and dispersion curve obtained by gradient inversion of the dispersion curve of anisotropic elastic medium guided waves in this invention are shown. It can be seen that the inversion model and data fitting degree in this embodiment are both high. Compared with Figure 6 Based on the inversion results of isotropic theory, this invention improves the accuracy of the inversion results.

[0129] Step 10: Calculate the sensitivity function of the guided wave dispersion curve to the anisotropic elastic parameters, such as... Figures 7-11 As shown, where Figure 7 Let be the sensitivity function of the dispersion curve of an anisotropic elastic medium guided wave to the optimal vertical shear wave velocity. Figures 8-11 This is the sensitivity function of the wave dispersion curve in an anisotropic elastic medium to the initial vertical longitudinal wave velocity, density, and Thomsen anisotropy parameters ε and δ. From... Figure 7 It can be seen that the guided wave dispersion curve is more sensitive to the vertical shear wave velocity of the upper and middle layers, and less sensitive to the vertical shear wave velocity of the lower layer. Therefore, the vertical shear wave velocity of the upper and middle layers obtained by inversion has higher reliability, while the vertical shear wave velocity of the lower layer has lower reliability.

[0130] The present invention also discloses a downhole distributed optical fiber anisotropic shale reservoir guided wave dispersion curve inversion system, comprising:

[0131] The observed dispersion curve extraction module calculates the guided wave dispersion energy map using methods such as τ-p transform, fk transform, or phase shift method, and extracts the observed guided wave dispersion curve from it;

[0132] The theoretical dispersion curve forward modeling module calculates the theoretical guided wave dispersion curve using methods such as the generalized reflection-transmission coefficient method.

[0133] The objective function judgment module calculates the objective function between the observed dispersion curve and the theoretical dispersion curve. If the objective function meets the convergence condition, it outputs the vertical shear wave velocity; otherwise, it proceeds to the next module.

[0134] The objective function gradient calculation module calculates the gradient of the objective function with respect to the vertical shear wave velocity.

[0135] The vertical shear wave velocity update module calculates the step size and gradient descent direction, updates the vertical shear wave velocity using the linearized quasi-Newton method L-BFGS-B optimization method, calculates the theoretical dispersion curve corresponding to the updated model, recalculates the objective function, and determines whether it meets the convergence condition until the objective function meets the convergence condition.

[0136] The reliability assessment module calculates the sensitivity function of the guided wave dispersion curve to the inverted vertical shear wave velocity, analyzes the sensitivity of the guided wave dispersion curve to the inverted vertical shear wave velocity, and evaluates the reliability of the inverted vertical shear wave velocity.

[0137] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0138] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, etc. Read-only memory (Read- Only Memory (ROM) and Random Access Memory (RAM) RAM, magnetic disks, optical disks, and other media that can store program code.

[0139] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs, characterized in that, The method includes: S1. Obtain guided wave seismic data of shale reservoirs, calculate guided wave dispersion energy map based on the obtained guided wave seismic data, and extract the observed guided wave dispersion curve from it; S2, Establish the initial anisotropic elastic parameter model of the shale reservoir; S3. Based on the initial or updated anisotropic elastic parameter model of the shale reservoir, the theoretical guided wave dispersion curve is obtained through forward modeling. S4. Construct the objective function, calculate and determine whether the objective function converges based on the residual between the theoretical guided wave dispersion curve and the observed guided wave dispersion curve; S5. If the objective function converges, output the anisotropic elastic parameter model of the current shale reservoir; if the objective function does not converge, update the anisotropic elastic parameter model using a gradient-based algorithm, and return to S3 and S4 until the objective function converges. S6 outputs the optimized anisotropic elastic parameter model of the shale reservoir.

2. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, In S1, guided wave seismic data of shale reservoirs are acquired through a downhole distributed optical fiber system.

3. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, In S1, the guided wave dispersion energy map is calculated based on the guided wave seismic data, and the observed guided wave dispersion curve is extracted from it.

4. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, The formula for calculating the guided wave dispersion energy map is: in, It is the total number of distributed fiber optic channels. For phase shift, This is guided wave seismic data in the space-frequency domain after amplitude normalization; ω is the angular frequency of the guided wave.

5. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, In step S2, an initial anisotropic elastic parameter model of the shale reservoir is constructed based on geological information. This model includes: vertical P-wave velocity, vertical S-wave velocity, density, and Thomsen anisotropy parameters. , .

6. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, In S3, the theoretical guided wave dispersion curve is solved by the dispersion equation; The dispersion equation is as follows: in, To coincide with the passing of the rising wave The interface reflects to the first The generalized reflection coefficient related to waves in a layered medium To coincide with the downwave passing through the first The interface reflects to the first The generalized reflection coefficient related to the wave in the layered medium; the root of the dispersion equation for each layer is the theoretical guided wave dispersion curve corresponding to the current anisotropic elastic parameter model; This represents the determinant of the matrix within the parentheses. Represents the identity matrix.

7. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, In S4, the least squares function of the difference between the observed dispersion curve and the theoretical dispersion curve is used as the objective function.

8. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 1, characterized in that, In S5, the specific method for updating the anisotropic elastic parameters using a gradient-based algorithm is as follows: The gradient of the objective function with respect to the vertical shear wave velocity is calculated, and then the gradient descent direction and Wolfe criterion are used to determine the step size. The vertical shear wave velocity is updated using the linearized quasi-Newton method L-BFGS-B optimization method.

9. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 5, characterized in that, The formula for calculating the objective function is as follows: in, To determine the number of modes for observing dispersion curves, To observe the dispersion curve The number of dispersion points of a mode. For the theoretical dispersion curve Modal Phase velocity at frequency, To observe the dispersion curve Modal Phase velocity at a given frequency; The formula for the gradient of the objective function is: in, , , It is the wave number. It is group velocity. It's density. It is the vertical longitudinal wave velocity. It is the vertical transverse wave velocity. and These are the horizontal and vertical component amplitudes of the harmonic solution, respectively. The formula for calculating the gradient descent direction is: in, a It is the number of iterations. Is The approximate Hessian matrix is ​​located at [location]. The objective function is the current vertical shear wave velocity. The gradient; Wolfe's criterion is: in, For the objective function in The value at that location, It is a constant, and ; The linearized quasi-Newton method L-BFGS-B optimization method updates the vertical shear wave velocity formula as follows: in, It is the first Vertical transverse wave velocity in the next iteration It's about updating the step size. Is It is in the gradient descent direction.

10. The method for inverting guided wave dispersion curves in downhole distributed optical fiber anisotropic shale reservoirs according to claim 9, characterized in that, Following step S5, the method further includes: evaluating the reliability of the inversion results using a sensitivity function; wherein the formula for calculating the sensitivity function is: in, , , , and These represent the guided wave dispersion curves for vertical shear wave velocity, vertical longitudinal wave velocity, density, and Thomsen anisotropy parameters, respectively. and Sensitive functions; ω is the angular frequency of the guided wave.

Images