Seismic shear wave inversion model construction method, porosity prediction method and device

Abstract

The present application relates to the field of oil and gas field development, in particular to a method and device for constructing a seismic shear transverse wave inversion model and predicting porosity. The method comprises the following steps: constructing a seismic shear transverse wave inversion initial model according to well logging data and seismic data; determining a synthetic seismic record according to the seismic shear transverse wave inversion initial model and a transverse wave reflection coefficient approximation equation; determining an objective function of the seismic shear transverse wave inversion initial model according to the seismic data, the synthetic seismic record and the seismic shear transverse wave inversion initial model; iteratively updating parameters in the seismic shear transverse wave inversion initial model by using the objective function; and determining a final obtained model as a seismic shear transverse wave inversion model. The present application uses a seismic shear transverse wave prestack simultaneous inversion method to obtain reliable shear modulus inversion results; a linear relationship between shear modulus and porosity is obtained by linear regression analysis of well logging data, and the shear modulus inversion results are used to predict the porosity of underground media, thereby providing guidance for subsequent reservoir quantitative evaluation.

Description

Technical Field

[0001] This article relates to the field of oil and gas field development, and in particular to the construction method of seismic shear shear wave inversion model, porosity prediction method and device. Background Technology

[0002] Currently, the application of seismic P-wave information in oil and gas seismic exploration presents uncertainties and multiple solutions in areas such as gas cloud imaging, multi-parameter stratigraphic inversion, lithological identification of oil and gas reservoirs, and fluid prediction.

[0003] Currently, seismic inversion and reservoir prediction for oil and gas primarily utilize seismic P-wave data. While P-waves offer advantages in structural imaging and fault interpretation, predicting reservoir characteristics and identifying pore fluid variations requires inverting multiple formation parameters using P-wave data at different angles (offsets). This inversion results are highly ambiguous, with density seismic inversion being particularly challenging. Furthermore, relying solely on P-wave properties and related elastic parameters for lithological identification and fluid prediction in complex and unconventional oil and gas reservoirs also presents uncertainties and ambiguities. Although joint inversion of P-wave and converted shear wave data helps improve the inversion results for formation shear wave velocity and density, converted shear waves cannot fully reflect the wavefield characteristics of pure shear waves. Additionally, the current application of converted shear waves is limited by complex processing procedures and low data quality. Compared to converted shear waves, pure shear waves (SH waves) offer more complete and clearer wavefield characteristics and a higher signal-to-noise ratio, demonstrating significant advantages and potential in practical applications.

[0004] Existing technologies commonly use methods based on P-wave isotropic AVO analysis and inversion for oil and gas reservoir prediction and fluid identification. However, due to the large number of parameters to be inverted in the P-wave, simultaneous multi-parameter inversion exhibits high ill-posedness.

[0005] To address the problem of highly ill-posed multi-parameter inversion in current technologies, a method for constructing a seismic shear shear wave inversion model, a porosity prediction method, and a device are proposed. Summary of the Invention

[0006] To address the problems of the prior art, this paper provides a method for constructing a seismic shear shear wave inversion model, a porosity prediction method, and an apparatus.

[0007] This paper provides a method for constructing a seismic shear shear wave inversion model. The method includes: constructing an initial seismic shear shear wave inversion model based on well logging data and seismic data; determining a synthetic seismic record based on the initial seismic shear shear wave inversion model and the approximate equation for the shear wave reflection coefficient; determining an objective function for the initial seismic shear shear wave inversion model based on the seismic data, the synthetic seismic record, and the initial seismic shear shear wave inversion model; and iteratively updating the parameters in the initial seismic shear shear wave inversion model using the objective function, and finally determining the obtained model as the seismic shear shear wave inversion model.

[0008] According to one aspect of the embodiments herein, determining the synthetic seismic record based on the initial seismic shear shear wave inversion model and the approximate equation for the shear wave reflection coefficient includes: extracting seismic wavelets at different angles from the seismic data and constructing a multi-angle wavelet convolution matrix; and calculating the shear wave reflection coefficients at different incident angles using the approximate equation for the shear wave reflection coefficient based on the initial seismic shear shear wave inversion model and the angles of the seismic data.

[0009] R SH (φ i )=A(φ i )Δlnμ+B(φ i )Δln V S , Where, φ i R represents the incident angle of the i-th transverse wave. SH (φ i ) represents the transverse wave reflection coefficient corresponding to the incident angle, Δlnμ=lnμ i+1 -lnμ i , μ i μ i+1 These represent the shear moduli of the upper and lower media at the i-th reflection interface, respectively, calculated based on the initial model of seismic shear shear wave inversion. Let represent the shear wave velocities of the upper and lower media at the i-th reflection interface, respectively; and determine the synthetic seismic record by multiplying the shear wave reflection coefficient with the multi-angle wavelet convolution matrix.

[0010] According to one aspect of the embodiments herein, the method includes determining an objective function using the following formula:

[0011] J(m)=[G(m)-d] T [G(m)-d]+λ(mm prior ) T (mm prior ), where J(m) is the objective function, G(m) is the synthetic seismic record, and d = [d1...d2]. N ] T, represents shear wave seismic data including N angles, and m represents model parameters; m prior This is a priori model.

[0012] According to one aspect of the embodiments herein, iteratively updating the parameters in the initial model of the seismic shear shear wave inversion using the objective function, and determining the final model as the seismic shear shear wave inversion model, includes: determining the update amount of the model parameters and the data residuals of the model training by differentiating the model parameters in the objective function; when the data residuals are less than a certain threshold or the maximum number of iterations is reached, determining that the trained model is the seismic shear shear wave inversion model.

[0013] According to one aspect of the embodiments herein, constructing an initial seismic shear shear wave inversion model based on well logging data and seismic data includes: obtaining shear wave velocity from the well logging data and converting the seismic data into angular domain seismic data; performing partial angular stacking on the angular domain seismic data to obtain a shear wave sub-angular stacking profile; performing well-seismic calibration using the sub-angular stacking profile and the shear wave velocity, and converting the well logging data into time domain well logging data; combining the converted time domain well logging data with seismic stratigraphic information and using an interpolation extrapolation method to obtain the lateral distribution of the subsurface medium, and constructing an initial seismic shear shear wave inversion model, wherein the initial seismic shear shear wave inversion model includes shear wave velocity and shear modulus parameters.

[0014] This embodiment provides a porosity prediction method. The porosity prediction method uses the seismic shear shear wave inversion model to obtain the shear modulus, including: determining the shear modulus inversion result based on the seismic shear shear wave inversion model; obtaining the porosity in well logging data; determining the linear relationship between the shear modulus and porosity by performing regression analysis on the porosity and the shear modulus inversion result; and predicting the porosity of the seismic profile based on the linear relationship and the shear modulus obtained by seismic inversion.

[0015] This embodiment provides a seismic shear shear wave inversion model construction device. The device includes: a seismic shear shear wave inversion initial model construction unit, used to construct a seismic shear shear wave inversion initial model based on well logging data and seismic data; an approximate equation and synthetic seismic record determination unit, used to determine the reflection coefficient approximate equation and synthetic seismic record based on the seismic shear shear wave inversion model; an objective function determination unit, used to determine the objective function of the seismic shear shear wave inversion model based on seismic data, synthetic seismic record and the seismic shear shear wave inversion initial model; and a seismic shear shear wave inversion model determination unit, used to train the parameters in the seismic shear shear wave inversion initial model using the objective function, and determine the trained model as the seismic shear shear wave inversion model.

[0016] This embodiment provides a porosity prediction device, which includes: a shear modulus inversion result determination unit, used to determine the shear modulus based on a seismic shear shear wave inversion model; a porosity acquisition unit, used to acquire porosity from well logging data; a linear relationship determination unit, used to determine the linear relationship between shear modulus and porosity through regression analysis; and a porosity prediction unit, used to predict the porosity of a seismic profile based on the linear relationship and the shear modulus obtained from seismic inversion.

[0017] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the seismic shear wave inversion model construction method and the porosity prediction method.

[0018] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the seismic shear wave inversion model construction method and the porosity prediction method.

[0019] This paper utilizes the pre-stack synchronous inversion method of seismic shear shear waves to obtain reliable shear modulus inversion results. Based on linear regression analysis of well logging data, the linear relationship between shear modulus and porosity is obtained. Finally, the shear modulus inversion results are used to predict the porosity of the subsurface medium, providing guidance for subsequent quantitative reservoir evaluation. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments or prior art described herein, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this article. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The diagram shown is a flowchart of a method for constructing a seismic shear shear wave inversion model according to an embodiment of this paper.

[0022] Figure 2 The diagram shown is a flowchart of a method for determining synthetic seismic records according to an embodiment of this paper;

[0023] Figure 3 The diagram shown is a flowchart of a method for determining a seismic shear wave inversion model according to an embodiment of this paper.

[0024] Figure 4 The diagram shown is a flowchart of a method for constructing an initial model for seismic shear wave inversion according to an embodiment of this paper.

[0025] Figure 5The diagram shown is a flowchart of a porosity prediction method according to an embodiment of this paper;

[0026] Figure 6 The diagram shown is a structural schematic of a seismic shear shear wave inversion model construction device according to an embodiment of this paper.

[0027] Figure 7 The diagram shown is a structural schematic of a porosity prediction device according to an embodiment of this paper.

[0028] Figure 8A The figure shown is a schematic diagram of the shear modulus parameters in an initial model for seismic shear shear wave inversion according to an embodiment of this paper;

[0029] Figure 8B The figure shown is a schematic diagram of the shear wave velocity parameters in an initial model for seismic shear wave inversion according to an embodiment of this paper;

[0030] Figure 9A The image shown is a schematic diagram of a 10° superimposed cross-section of an embodiment described in this paper.

[0031] Figure 9B The image shown is a schematic diagram of a 20° superimposed cross-section of an embodiment described in this paper.

[0032] Figure 9C The image shown is a schematic diagram of a 30° superimposed cross-section of an embodiment described in this paper.

[0033] Figure 9D The diagram shown is a schematic diagram of wavelet extraction using transverse wave angle-division superposition profiles in an embodiment of this paper;

[0034] Figure 10A The diagram shown is a comparison of an approximate formula and an exact formula for the transverse wave reflection coefficient in an embodiment of this paper.

[0035] Figure 10B The diagram shown is a comparison of an approximate formula and an exact formula for the transverse wave reflection coefficient in another embodiment of this paper.

[0036] Figure 11A The figure shown is a schematic diagram of a shear modulus inversion profile according to an embodiment of this paper;

[0037] Figure 11B The image shown is a schematic diagram of a porosity prediction profile according to an embodiment of this paper.

[0038] Figure 11C This involves comparing the actual porosity data in well logging data with the predicted porosity results and identifying the errors.

[0039] Figure 12 The diagram shown is a structural schematic of a computer device according to an embodiment of this article.

[0040] Explanation of symbols in the attached drawings:

[0041] 601. Initial model building unit for seismic shear shear wave inversion;

[0042] 602. Approximate equation for shear wave reflection coefficient and determination of unit in synthetic seismic record;

[0043] 603. Objective function determination unit;

[0044] 604. Seismic shear shear wave inversion model determines the unit;

[0045] 701. Unit for determining the inversion results of shear modulus;

[0046] 702. Porosity Acquisition Unit;

[0047] 703. Linear relationship determination unit;

[0048] 704. Porosity prediction unit;

[0049] 1202. Computer equipment;

[0050] 1204, Processor;

[0051] 1206. Memory;

[0052] 1208. Drive mechanism;

[0053] 1210. Input / output module;

[0054] 1212. Input devices;

[0055] 1214. Output devices;

[0056] 1216. Presentation equipment;

[0057] 1218. Graphical User Interface;

[0058] 1220. Network interface;

[0059] 1222. Communication link;

[0060] 1224. Communication bus. Detailed Implementation

[0061] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments herein will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments herein, and not all of the embodiments. Based on the embodiments herein, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this document.

[0062] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings herein are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0063] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0064] Figure 1 The diagram shows a flowchart of the method for constructing a seismic shear shear wave inversion model as described in this paper, which specifically includes the following steps:

[0065] Step 101: Construct an initial seismic shear wave inversion model based on well logging data and seismic data. In this step, well logging data and seismic data are first acquired. Well logging data primarily reflects stratigraphic and geological information. In some embodiments of this specification, well logging data are response curves related to subsurface lithology and physical properties within the wellbore area based on depth measurements. Specifically, well logging data includes P-wave velocity, S-wave velocity, density, clay content, porosity, and water saturation curves. Seismic data is a time-domain response to subsurface reflection interfaces.

[0066] This step first constructs a velocity field from the shear wave velocity in the well logging curves, converts the processed shear wave common reflection point gathers into shear wave angle gathers, and superimposes the shear wave angle gathers within a certain angle range to obtain shear wave sub-angle stacking profiles at different angles. Further, wavelets are extracted from the sub-angle stacking profiles at different angles to obtain wavelet convolution matrices at different angles. Then, well-seismic calibration is performed on the seismic data and well logging data. Further, combined with seismic horizon information, an initial model for shear wave inversion is constructed using interpolation extrapolation methods. This initial model is a shear modulus and shear wave velocity initial model, referred to as the seismic shear shear wave inversion initial model. This seismic shear shear wave inversion initial model can be derived from m0=[lnμ0,ln V S0 ] TIn this representation, m0 represents the initial model, μ0 represents the shear modulus parameter in the initial model, and V... S0 This represents the shear wave velocity parameters in the initial model, such as... Figure 8A and Figure 8B As shown. Figure 8A , Figure 8B These represent the shear modulus parameter and shear wave velocity parameter in the initial model for seismic shear wave inversion, respectively.

[0067] In this step, a detailed description of constructing the initial model for seismic shear wave inversion is provided below. Figure 5 .

[0068] Step 102: Based on the initial seismic shear wave inversion model and the approximate equation for the shear wave reflection coefficient, determine the synthetic seismic record. In this step, the approximate equation for the shear wave reflection coefficient is obtained based on the precise formula for the shear wave reflection coefficient. The precise formula for the shear wave reflection coefficient is the reflection coefficient equation for a single interface. The process of obtaining the precise formula for the shear wave reflection coefficient, determining the approximate equation for the shear wave reflection coefficient, and synthesizing the seismic record will be discussed later. Figure 2 Detailed description is provided.

[0069] Step 103: Based on the seismic data, the synthetic seismic record, and the initial seismic shear shear wave inversion model, determine the objective function of the seismic shear shear wave inversion model. This step uses the following formula to determine the objective function:

[0070] J(m)=[G(m)-d] T [G(m)-d]+λ(mm prior ) T (mm prior ), where J(m) is the objective function, G(m) is the synthetic seismic record, and d = [d1...d2]. N ] T Let be the shear wave seismic data including N angles, λ represent the weight coefficient of the regularization term proportional to the noise level, and m = [lnμ, lnV]. S ] T The model parameters, including shear modulus and shear wave velocity, are represented by λ (mm). prior ) T (mm prior ) represents the regularization term; m prior For the prior model, G(m)-d represents the difference between the synthetic seismic record and the real seismic data.

[0071] In this step, the objective function is used to analyze the synthetic seismic data G(m) and the real seismic data d = [d1...d2]. N ] TThe error between the objective function and the model parameters are continuously adjusted based on the error to make the error of the objective function converge until the error value converges to a certain range, and then the model parameters in the current objective function are solved. In some embodiments of this specification, a least squares error function is constructed as the objective function of the inversion model, and the objective function is solved to obtain the model parameters.

[0072] Step 104: Using the objective function, iteratively update the parameters in the initial seismic shear shear wave inversion model to determine the final model as the seismic shear shear wave inversion model. In this step, the model parameters include shear modulus and shear wave velocity. Each calculation of the objective function iteratively updates the shear modulus and shear wave velocity parameters in the initial seismic shear shear wave inversion model. When the value of the objective function converges to a preset threshold range, the iteration of the objective function is considered complete. The model parameters at this point are then solved, and the model is determined as the seismic shear shear wave inversion model.

[0073] Figure 2 The diagram shown is a flowchart of a method for determining synthetic seismic records according to an embodiment of this paper, including the following steps:

[0074] Step 201: Extract seismic wavelets at different angles from the seismic data and construct a multi-angle wavelet convolution matrix. Seismic data with different incident angles, such as 10°, 20°, and 30°, can be selected from the seismic data in Step 101. Using the seismic data at different angles, a shear wave angle-multiplied stacking profile is constructed. Figure 9A , Figure 9B , Figure 9C As shown, the shear wave cross-sections at 10°, 20°, and 30° are stacked at different angles. The horizontal axis CDP in the figure represents the horizontal coordinate of the seismic data, indicating the set of points collected along the horizontal direction of the ground. The vertical axis represents the time when the seismic wave was received, further reflecting the vertical depth underground. The black line in the figure represents the location of the well. Figure 9A , Figure 9B , Figure 9C It can provide feedback on various characteristic waveforms of seismic waves, further reflecting underground lithology and strata morphology.

[0075] In this step, wavelets are extracted from shear wave profiles at different angles using sub-angle stacking, resulting in shear wave wavelet matrices for different angles. In some embodiments of this specification, the wavelets extracted from the sub-angle stacking shear wave profiles are as follows: Figure 9D As shown. In some embodiments of this specification, the extracted transverse wavelet matrix can be represented by the following matrix.

[0076] Where W(φ1) represents the wavelet corresponding to the incident angle φ1, W(φ2) represents the wavelet corresponding to the incident angle φ2, and W(φ m) represents the incident angle φ m The corresponding wavelet.

[0077] Assuming the length of the transverse wavelet at different angles is s, the wavelet convolution matrix at different angles can be expressed as follows:

[0078]

[0079] Step 202: Based on the initial seismic shear wave inversion model and the angle of the seismic data, calculate the shear wave reflection coefficient at different incident angles using the approximate equation for the shear wave reflection coefficient:

[0080] R SH (φ i )=A(φ i )Δlnμ+B(φ i )Δln V S , Where, φ i R represents the incident angle of the i-th transverse wave. SH (φ i ) represents the transverse wave reflection coefficient corresponding to the incident angle, Δlnμ=lnμ i+1 -lnμ i , μ i μ i+1 These represent the shear moduli of the upper and lower media at the i-th reflection interface, respectively, calculated based on the initial model of seismic shear shear wave inversion. Let represent the shear wave velocities of the upper and lower media at the i-th reflecting interface, respectively. In this step, the approximate equation for the shear wave reflection coefficient is derived from the exact equation for the shear wave reflection coefficient.

[0081] Specifically, assuming a transverse wave is incident at an angle φ1 onto an interface, a reflected transverse wave and a transmitted transverse wave will be generated. The reflection coefficient equation for the reflected transverse wave at this interface can be expressed as:

[0082]

[0083] In formula (1), φ1 represents the incident angle of the SH wave, φ2 represents the transmission angle, and V S1 ρ1 and V represent the transverse wave velocity and density of the upper medium, respectively; S2 ρ1 and ρ2 represent the transverse wave velocity and density of the underlying medium, respectively. Furthermore, the shear modulus can be expressed as... Therefore, equation (1) above can be rewritten as:

[0084]

[0085] Let V S =(V S1 +V S2) / 2, ΔV S =V S2 -V S1 Given μ = (μ1 + μ2) / 2 and Δμ = μ2 - μ1, the elastic parameters of the upper and lower media can be re-expressed as:

[0086]

[0087] The transmission angle can be calculated using Snell's law:

[0088]

[0089] And assume |ΔV S / V S From |<<1 and |Δμ / μ|<<1, we can obtain:

[0090]

[0091] Based on the derivation, an approximate formula for the transverse wave reflection coefficient can be obtained:

[0092]

[0093] Within the range of incident angles less than 30 degrees, the approximate formula (6) for the transverse wave reflection coefficient is basically consistent with the exact formula (1), such as... Figure 10A and Figure 10B As shown, Figure 10A , Figure 10B The diagrams show a comparison between the approximate and exact formulas for the transverse wave reflection coefficient. Therefore, this specification uses the approximate formula for the transverse wave reflection coefficient to calculate the reflection coefficient corresponding to each incident angle.

[0094] According to the formula ΔV S / V S ≈Δln V S And given Δμ / μ≈Δlnμ, equation (6) above can be expressed in the following form:

[0095]

[0096] Therefore, an approximate equation for the transverse wave reflection coefficient is determined.

[0097] Step 203: The synthetic seismic record is determined by multiplying the shear wave reflection coefficient with the multi-angle wavelet convolution matrix. In some embodiments of this specification, the synthetic seismic record is a seismic record obtained by artificial synthesis and conversion from well logging data and seismic profile data. The synthetic seismic record is the result of the convolution of the seismic wavelet and the reflection coefficient. The synthetic seismic record can be determined according to the formulas for the seismic wavelet and reflection coefficient determined in the above steps.

[0098] In this specification, the synthetic seismic record is denoted by G(m). Here, G represents the linear forward modeling operator constructed based on the approximate formula for the shear wave reflection coefficient, incorporating the effects of the incident angle and the wavelet. It can be expressed as...

[0099] G = WFD (8)

[0100] In the formula, W is the wavelet convolution matrix, F is the coefficient matrix, and D is the difference matrix. The coefficient matrix F and the difference matrix D can be expressed as:

[0101]

[0102] The synthetic seismic record can be represented as G(m) = WR, where W is the multi-angle wavelet convolution matrix determined in step 201, and R is the shear wave reflection coefficient for different incident angles determined in step 202. Multiplying the multi-angle wavelet convolution matrix by the shear wave reflection coefficients corresponding to different incident angles yields the synthetic seismic record for each incident angle.

[0103] Because in the approximate equation (8) for the transverse wave reflection coefficient, the logarithmic differences Δlnμ and ΔlnV between the reflection coefficient and the parameters on both sides of the interface are... S This is relevant, therefore for the model parameters m=[lnμ,ln V] S ] T Multiplying by the difference matrix yields Δlnμ and ΔlnV. S .

[0104] For a given model parameter m = [lnμ, lnV] S ] T According to the linear forward modeling operator G, the synthetic seismic record can be obtained as follows:

[0105] G(m)=WFDm=WR (10)

[0106] In the formula, R is the reflection coefficient of different incident angles and reflection interfaces obtained according to the approximate formula (10) of the reflection coefficient of SH wave. The composite seismic record can be obtained by multiplying the wavelet convolution matrix W with the reflection coefficient R.

[0107] Figure 3 The diagram shown is a flowchart of a method for determining a seismic shear wave inversion model according to an embodiment of this paper, which includes the following steps:

[0108] Step 301: Differentiate the model parameters in the objective function. In this step, the optimal solution of the objective function J(m) is obtained by differentiating the objective function J(m) with respect to the model parameter m and setting it to 0. The numerical solution of the model can then be obtained.

[0109] Step 302: Determine the update amount of the model parameters and the residual data from model training. After determining the optimal solution of the objective function in step 301, the update amount of the model parameters Δm = m - m0 can be further obtained as:

[0110] Δm=(G T G+μI) -1 G T Δd (11)

[0111] In the formula, Δd = dG(m0) represents the data residuals, and I represents the identity matrix. The model parameter vector can be solved using an iterative inversion method.

[0112] m i =m0+Δm (12)

[0113] Step 303: When the data residual is less than a certain threshold or the maximum number of iterations is reached, the seismic shear shear wave inversion model is determined. When the data residual Δd is less than a certain threshold or the objective function reaches the maximum number of iterations, the model corresponding to the model parameters in the current objective function is determined to be the seismic shear shear wave inversion model.

[0114] Figure 4 The diagram shown is a flowchart of a method for constructing an initial model for seismic shear wave inversion according to an embodiment of this paper, which specifically includes the following steps:

[0115] Step 401: Obtain the shear wave velocity from the well logging data and convert the seismic data into angle domain seismic data.

[0116] In some embodiments of this specification, shear wave velocity is converted into time-domain data, the processed shear wave common reflection point (CRP) gathers are converted into shear wave angle gathers, and shear wave angle gathers within a certain angle range are superimposed to obtain shear wave angle-separated stacked profiles at 10°, 20°, and 30°. In this specification, the process of inverting shear modulus and shear wave velocity using shear waves only requires small to medium angle data to reliably invert parameters such as shear modulus. Therefore, in one embodiment of this specification, the shear wave angle-separated stacked profiles at 10°, 20°, and 30° are used as inputs to the seismic shear shear wave inversion model in this specification.

[0117] Step 402 involves performing partial angular stacking on the angular domain seismic data to obtain shear wave sub-angular stacked profiles. This step first constructs a velocity field based on the shear wave velocities in the well logging curves, then converts the processed shear wave common reflection point gathers into shear wave angle gathers. The shear wave angle gathers within a certain angular range are then stacked to obtain shear wave sub-angular stacked profiles at different angles. Furthermore, wavelets are extracted from the shear wave sub-angular stacked profiles at different angles to obtain wavelet matrices for those angles.

[0118] Step 403: Well-seismic calibration is performed using the shear wave angular stacking profile and the shear wave velocity, converting the logging data into time-domain logging data. In this specification, well-seismic calibration can be performed based on seismic data and logging data. Specifically, the logging data includes shear wave velocity. A velocity field is constructed based on the shear wave velocity in the logging data. The depth-domain logging data is converted into time-domain data. Furthermore, the time-domain logging data is synthesized into a seismic record and matched with the time-domain seismic data at the corresponding location. That is, the depth-domain logging data is matched with the time-domain seismic data, completing the well-seismic calibration.

[0119] Step 404: Combine the well logging data converted to the time domain with seismic stratigraphic information, and use interpolation extrapolation to obtain the lateral distribution of the subsurface medium, constructing an initial seismic shear wave inversion model. The initial seismic shear wave inversion model includes shear wave velocity and shear modulus parameters.

[0120] In this step, wavelets are further extracted from the shear wave angle-stacked profile to obtain shear wave seismic wavelets at different angles, which are used to construct the wavelet convolution matrix. Seismic horizon information is obtained by manually picking continuous phase axes from the seismic profile based on the marker horizon information. Seismic horizon information is typically used to describe the lateral distribution of strata.

[0121] In some embodiments of this specification, the shear wave angle-multiplying profile can be represented by the following matrix:

[0122] Where φ1 and φ2 represent the first and second incident angles, respectively, φ M Let t represent the Mth incident angle, t1 and t2 represent the 1st and 2nd moments respectively, and t N This represents the Nth time point.

[0123] In some embodiments of this specification, the initial model constructed is m0 = [lnμ0, ln V] S0 ] T The initial model represents the longitudinal and transverse distribution of the subsurface medium across multiple channels and interfaces. The longitudinal and transverse distributions can be represented in matrix form, respectively:

[0124]

[0125] Where μ0 represents the shear modulus of a cross section, μ ii μ represents the shear modulus of the i-th reflection interface in this profile. 1N This represents the shear modulus of the first and Nth reflection interfaces of the cross-section, μ. MN V represents the shear modulus of the M-th and N-th reflecting interfaces of this profile. S0 V represents the transverse wave velocity of a cross-section. SiiV represents the transverse wave velocity at the i-th reflection interface in this profile. S1N V represents the transverse wave velocity at the first and Nth reflecting interfaces of this profile. SMN This represents the transverse wave velocity at the M-th and N-th reflecting interfaces of the profile.

[0126] Figure 5 The illustration shows a porosity prediction method according to an embodiment of this paper. Porosity is an important parameter describing oil and gas reservoirs. The shear modulus obtained by inverting using a seismic shear shear wave inversion model is insensitive to fluid variations, and the shear modulus can be a relatively effective elastic parameter for predicting porosity. The porosity prediction method specifically includes the following steps:

[0127] Step 501: Determine the shear modulus inversion results based on the seismic shear shear wave inversion model. In this step, reliable shear modulus inversion results are obtained using the SH wave synchronous inversion method.

[0128] Step 502: Obtain porosity from well logging data. In this step, well logging data for the target work area is obtained, and porosity is extracted from the well logging data for the target work area.

[0129] Step 503: By performing regression analysis on the inversion results of the porosity and the shear modulus, the linear relationship between the shear modulus and porosity is determined. In this step, it is assumed that there is a linear relationship between the shear modulus and porosity, and the linear relationship formula is as follows:

[0130] μ=a+bΦ (13)

[0131] Here, μ and Φ represent the shear modulus and porosity at different sampling depths, respectively. The coefficients a and b in this linear relationship can be determined through linear regression analysis. Specifically, coefficients a and b are obtained by minimizing the mean square prediction error.

[0132]

[0133] In the formula, the mean square prediction error is the sum of the prediction errors for all depth sampling points. A quantitative relationship between shear modulus and porosity can be obtained through linear regression analysis.

[0134] Step 504: Based on the linear relationship and the shear modulus obtained from seismic inversion, predict the porosity of the seismic profile. In this step, the seismic shear shear wave inversion model determines the shear modulus inversion profile, such as... Figure 11A As shown. Further linear regression analysis was used to obtain the linear relationship between shear modulus and porosity, predicting the porosity of the entire profile. The porosity prediction profile is shown below. Figure 11B As shown, the inversion results of the shear modulus and the predicted porosity of the well passage are compared with the actual values, for example... Figure 11C As shown.

[0135] In this step, the specific porosity prediction formula is as follows:

[0136] Φ inv =(μ inv -a) / b (15)

[0137] In the formula, μ inv This represents the shear modulus inversion result for the entire cross-section, Φ. inv This represents the porosity prediction result for the entire profile, where a and b are the coefficients obtained from the linear regression analysis of the logging data in step 503.

[0138] like Figure 6 The diagram shown is a structural schematic of a seismic shear shear wave inversion model construction device according to an embodiment of this paper. The basic structure of the device is illustrated in the diagram. The functional units and modules can be implemented using software, general-purpose chips, or specific chips to construct the seismic shear shear wave inversion model. The device specifically includes:

[0139] Seismic shear shear wave inversion initial model building unit 601 is used to build an initial seismic shear shear wave inversion model based on well logging data and seismic data;

[0140] The approximate equation for shear wave reflection coefficient and the synthetic seismic record determination unit 602 are used to determine the synthetic seismic record based on the initial model for seismic shear shear wave inversion and the approximate equation for shear wave reflection coefficient.

[0141] The objective function determination unit 603 is used to determine the objective function of the seismic shear shear wave inversion model based on the seismic data, the synthetic seismic record and the initial seismic shear shear wave inversion model.

[0142] The seismic shear shear wave inversion model determination unit 604 is used to train the parameters in the initial model of the seismic shear shear wave inversion using the objective function, and determine the trained model as the seismic shear shear wave inversion model.

[0143] like Figure 7 The diagram shown is a schematic representation of a porosity prediction device according to an embodiment of this paper. The basic structure of the porosity prediction device is illustrated in this diagram. The functional units and modules can be implemented using software, or using general-purpose chips or specific chips to achieve porosity prediction. The device specifically includes:

[0144] Shear modulus inversion result determination unit 701 is used to determine the shear modulus inversion result based on the seismic shear shear wave inversion model;

[0145] Porosity acquisition unit 702 is used to acquire porosity from well logging data;

[0146] The linear relationship determination unit 703 is used to perform regression analysis on the porosity and shear modulus inversion results to determine the linear relationship between shear modulus and porosity;

[0147] The porosity prediction unit 704 is used to predict the porosity of the seismic profile based on the linear relationship and the shear modulus obtained by seismic inversion.

[0148] This scheme utilizes the pre-stack synchronous inversion method of shear waves to obtain more reliable shear modulus inversion results. Furthermore, since the shear modulus is largely unaffected by fluid flow, it is a relatively effective elastic parameter for porosity prediction. Combined with well logging data from the work area, linear regression analysis reveals the linear relationship between shear modulus and porosity. The shear modulus inversion results are then used to predict the porosity of the subsurface medium, providing guidance for subsequent quantitative reservoir evaluation.

[0149] Figure 9A , Figure 9B and Figure 9C These are schematic diagrams showing superimposed cross-sections at 10°, 20°, and 30°, respectively.

[0150] Figure 9D This is a schematic diagram of wavelet extraction using angle-based superposition profiles in the embodiments described in this paper. The diagram includes wavelets extracted from superposition profiles at different angles and their corresponding spectrum diagrams.

[0151] Figure 10A and Figure 10B These diagrams illustrate a comparison between an approximate formula and an exact formula for the transverse wave reflection coefficient in the embodiments described in this paper. Figure 10A A schematic diagram comparing the approximate and exact formulas for the reflection coefficient when the parameter differences on both sides of the reflective interface are small. Figure 10B This diagram illustrates the comparison between the approximate and precise formulas for the reflection coefficient when there are significant differences in parameters on both sides of the reflective interface. Figure 10A and Figure 10B The solid black line represents the exact formula, and the dashed black line represents the linear approximation formula. It can be seen that the linear approximation formula maintains high approximation accuracy within 50° under weak impedance difference conditions, and within 40° under strong impedance difference conditions.

[0152] Figure 11A This is the result of shear modulus inversion. Figure 11B This is a schematic diagram of the porosity prediction results. Figure 11C This section compares the actual porosity data in well logging data with the predicted porosity results and examines the resulting errors.

[0153] in, Figure 11C The black solid line in the graph represents the actual curve, and the gray solid line represents the inversion result of the seismic shear shear wave inversion model. From... Figure 11CAs can be seen, the porosity prediction results are in good agreement with the actual porosity data in the well logging data. Figure 11C In the study, the correlation coefficient and relative error between predicted porosity and logging porosity were 0.7867 and 7.38%, respectively.

[0154] like Figure 12 As shown, a computer device provided in this embodiment is used to construct a seismic shear wave inversion model and predict porosity. The computer device 1202 may include one or more processors 1204, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 1202 may also include any memory 1206 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the memory 1206 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Furthermore, any memory can provide volatile or non-volatile retention of information. Furthermore, any memory can represent a fixed or removable component of the computer device 1202. In one case, when the processor 1204 executes associated instructions stored in any memory or combination of memories, the computer device 1202 can perform any operation of the associated instructions. The computer device 902 also includes one or more drive mechanisms 908 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0155] Computer device 1202 may also include an input / output module 1210 (I / O) for receiving various inputs (via input device 1212) and providing various outputs (via output device 1214). A specific output mechanism may include a presentation device 1216 and an associated graphical user interface (GUI) 1218. In other embodiments, the input / output module 1210 (I / O), input device 1212, and output device 1214 may be omitted, and the device may function solely as a computer device within a network. Computer device 1202 may also include one or more network interfaces 1220 for exchanging data with other devices via one or more communication links 1222. One or more communication buses 1224 couple the components described above together.

[0156] Communication link 1222 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 1222 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0157] Corresponding to Figures 1 to 5 In addition to the methods described above, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described methods.

[0158] This embodiment also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the following: Figures 1 to 5 The method shown.

[0159] It should be understood that in the various embodiments of this document, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0160] It should also be understood that, in the embodiments herein, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0161] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0162] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0163] In the embodiments provided herein, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units 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 couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described herein, depending on actual needs.

[0165] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this paper. 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.

[0167] This document uses specific embodiments to illustrate the principles and implementation methods of this document. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this document. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this document. Therefore, the content of this specification should not be construed as a limitation of this document.

Claims

1. A method for constructing a seismic shear shear wave inversion model, characterized in that, The method includes: An initial model for seismic shear wave inversion was constructed based on well logging data and seismic data. Based on the initial model for seismic shear shear wave inversion and the approximate equation for shear wave reflection coefficient, the synthetic seismic record is determined, which includes: Seismic wavelets at different angles are extracted from the seismic data to construct a multi-angle wavelet convolution matrix; Based on the initial seismic shear wave inversion model and the angle of the seismic data, the shear wave reflection coefficients at different incident angles are calculated using the approximate equation for the shear wave reflection coefficient: , , ,in, This represents the incident angle of the i-th transverse wave. The transverse wave reflection coefficient is the value corresponding to the incident angle. , , , These represent the results calculated based on the initial model of seismic shear wave inversion. Shear modulus of the upper and lower media at a reflective interface; , They represent the first Transverse wave velocities of the upper and lower media at the reflective interface; The synthetic seismic record is determined by multiplying the shear wave reflection coefficient with the multi-angle wavelet convolution matrix. Based on the earthquake data, the synthetic seismic record, and the initial model for earthquake shear shear wave inversion, determine the objective function of the initial model for earthquake shear shear wave inversion. Using the objective function, the parameters in the initial model of the seismic shear shear wave inversion are iteratively updated, and the final model is determined as the seismic shear shear wave inversion model.

2. The method for constructing a seismic shear shear wave inversion model according to claim 1, characterized in that, The method includes determining the objective function using the following formula: ,in, Let the objective function be... For the aforementioned synthetic seismic record, This refers to shear wave seismic data including N angles. These are model parameters; As a priori model, The regularization term weight coefficient is proportional to the noise level, and the superscript T indicates matrix transpose.

3. The method for constructing a seismic shear shear wave inversion model according to claim 1, characterized in that, Using the objective function, the parameters in the initial model for seismic shear shear wave inversion are iteratively updated to determine the final model as the seismic shear shear wave inversion model, including: Differentiate the model parameters in the objective function; Determine the update amount of model parameters and the residual data from model training; When the data residual is less than a preset threshold or the maximum number of iterations is reached, the seismic shear wave inversion model is determined.

4. The method for constructing a seismic shear shear wave inversion model according to claim 1, characterized in that, The initial model for seismic shear wave inversion, constructed based on well logging data and seismic data, includes: Obtain shear wave velocity from the well logging data and convert the seismic data into angular domain seismic data; By performing partial angle stacking on the angle domain seismic data, a shear wave angle-stacked profile is obtained; Well-seismic calibration is performed using the shear wave angular superposition profile and the shear wave velocity, and the logging data is converted into time-domain logging data. By combining well logging data converted to the time domain with seismic stratigraphic information and using interpolation extrapolation to obtain the lateral distribution of the subsurface medium, an initial seismic shear wave inversion model is constructed, which includes shear wave velocity and shear modulus parameters.

5. A porosity prediction method, characterized in that, The porosity prediction method uses the seismic shear shear wave inversion model according to any one of claims 1-4 to obtain the shear modulus, including: The shear modulus inversion results are determined based on the seismic shear shear wave inversion model. Obtain porosity from well logging data; By performing regression analysis on the inversion results of the porosity and the shear modulus, the linear relationship between the shear modulus and porosity is determined. Based on the linear relationship and the shear modulus obtained from seismic inversion, the porosity of the seismic profile is predicted.

6. A device for constructing a seismic shear shear wave inversion model, characterized in that, The device employs the method according to any one of claims 1 to 5, comprising: The initial model building unit for seismic shear shear wave inversion is used to build an initial model for seismic shear shear wave inversion based on well logging data and seismic data. The approximate equation for shear wave reflection coefficient and the unit for determining synthetic seismic records are used to determine synthetic seismic records based on the initial model for seismic shear shear wave inversion and the approximate equation for shear wave reflection coefficient. The objective function determination unit is used to determine the objective function of the initial seismic shear shear wave inversion model based on the seismic data, synthetic seismic records, and the initial seismic shear shear wave inversion model. The seismic shear shear wave inversion model determination unit is used to train the parameters in the initial seismic shear shear wave inversion model using the objective function, and determine the trained model as the seismic shear shear wave inversion model.

7. A porosity prediction device, characterized in that, The device obtains the shear modulus using the seismic shear shear wave inversion model of any one of claims 1 to 5, including: The shear modulus inversion result determination unit is used to determine the shear modulus inversion result based on the seismic shear shear wave inversion model. Porosity acquisition unit is used to acquire porosity from well logging data; The linear relationship determination unit is used to determine the linear relationship between shear modulus and porosity by performing regression analysis on the inversion results of porosity and shear modulus; The porosity prediction unit is used to predict the porosity of the seismic profile based on the linear relationship and the shear modulus obtained by seismic inversion.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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