Sweet spot prediction method and device

Abstract

The invention provides a dessert prediction method and device. The method comprises the steps of 1, performing pre-stack seismic inversion according to a VTI medium longitudinal wave anisotropic reflection coefficient equation; 2, calculating anisotropic parameters based on an isotropic inversion formula through a step-by-step inversion algorithm; and step 3, performing sweet spot prediction according to a pre-stack seismic inversion result and the anisotropy parameters. According to the method, the precision problem caused by isotropic reflection coefficient equation inversion is solved, the sweet spot prediction precision can be improved, and a new basis is provided for shale oil and gas reservoir description and sweet spot prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and in particular to a sweet spot prediction method and device. Background Art

[0002] Sweet spot (i.e., the rock most favorable for oil or gas extraction) prediction is a comprehensive research method that uses geophysical methods such as seismic and logging, combines artificial seismic exploration with logging core sampling, and predicts the enriched areas of oil and gas reservoirs through data-driven methods. This method mainly identifies sweet spot areas based on two aspects: geological factors and engineering factors. These main controlling factors include preservation conditions (burial depth, temperature, pressure, and thickness), organic matter type, content, and maturity, geological factors such as porosity, permeability, and fracture development, as well as engineering factors such as brittleness index, fracturing fluid injection volume, proppant injection volume, number of fracturing stages, and horizontal section length.

[0003] During the sweet spot prediction process, it is necessary to statistically analyze these main controlling factor parameters and manually determine the sweet spot advantageous areas. However, this method has certain subjectivity and uncertainty in parameter selection for determining the sweet spot area, thus affecting the accuracy of sweet spot area prediction.

[0004] On the other hand, shale is a sedimentary rock mainly composed of clay minerals (such as kaolinite, hydromica, etc.) and has obvious thin bedding structures. Due to its organic matter content, clay, and microfracture orientation arrangement, shale exhibits VTI characteristics. At present, the application of prestack seismic inversion still remains in the stage of inversion based on the isotropic reflection coefficient equation, which poses great challenges to sweet spot prediction in shale reservoirs. Summary of the Invention

[0005] Aiming at the deficiencies of the above sweet spot prediction method for VTI media, the present invention proposes a sweet spot prediction method based on seismic inversion of an improved VTI reflection coefficient equation, which can improve the accuracy of sweet spot prediction and provide a new basis for shale oil and gas reservoir description and sweet spot prediction.

[0006] According to one aspect of the present invention, a sweet spot prediction method is proposed, and the method includes:

[0007] Step 1: Perform prestack seismic inversion according to the VTI medium P-wave anisotropic reflection coefficient equation;

[0008] Step 2: Calculate anisotropic parameters based on the isotropic inversion formula through a step-by-step inversion algorithm;

[0009] Step 3: Perform sweet spot prediction based on the results of the prestack seismic inversion and the anisotropic parameters.

[0010] Preferably, the VTI medium P-wave anisotropic reflection coefficient equation is:

[0011]

[0012] Among them, represents the anisotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, and V P0 represents the vertical phase velocity of the longitudinal wave, and V S0 represents the vertical phase velocity of the shear wave. Term A represents the square of the longitudinal wave impedance, term B represents the anisotropic shear modulus term of the shear wave, and term C represents the square term of the anisotropic longitudinal wave velocity;

[0013] Among them, the pre-stack seismic inversion result is expressed as:

[0014]

[0015] Among them, Z p represents the longitudinal wave impedance, σ is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, μ represents the shear modulus of the shear wave, and δ and ε represent the Thomsen weak anisotropic parameters.

[0016] Preferably, the isotropic inversion formula is:

[0017]

[0018] Among them, represents the isotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, and V P0 represents the vertical phase velocity of the longitudinal wave, and V S0 represents the vertical phase velocity of the shear wave. Term A' represents the square of the longitudinal wave impedance, term B' represents the isotropic shear modulus term of the shear wave, and term C' represents the square term of the isotropic longitudinal wave velocity;

[0019] Among them,

[0020] Preferably, the anisotropic parameter is:

[0021]

[0022] On the other hand, the present invention provides a sweet spot prediction device, including:

[0023] A pre-stack seismic inversion module for performing pre-stack seismic inversion according to the VTI medium longitudinal wave anisotropic reflection coefficient equation;

[0024] A calculation module for calculating anisotropic parameters based on the isotropic inversion formula through a step-by-step inversion algorithm;

[0025] A prediction module for predicting sweet spots according to the result of the pre-stack seismic inversion and the anisotropic parameters.

[0026] Preferably, the vertical transverse isotropy (VTI) medium longitudinal wave anisotropic reflection coefficient equation is as follows:

[0027]

[0028] Wherein, represents the anisotropic reflection coefficient, θ represents the longitudinal wave incident angle, V P0 represents the longitudinal wave vertical phase velocity, V S0 represents the shear wave vertical phase velocity, term A represents the square of the longitudinal wave impedance, term B represents the anisotropic shear wave modulus term, and term C represents the square term of the anisotropic longitudinal wave velocity;

[0029] Wherein, the pre-stack seismic inversion result is expressed as:

[0030]

[0031] Wherein, Z p represents the longitudinal wave impedance, σ is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, μ represents the shear wave modulus, and δ and ε represent the Thomsen weak anisotropic parameters.

[0032] Preferably, the isotropic inversion formula is as follows:

[0033]

[0034] Wherein, represents the isotropic reflection coefficient, θ represents the longitudinal wave incident angle, V P0 represents the longitudinal wave vertical phase velocity, V S0 represents the shear wave vertical phase velocity, term A' represents the square of the longitudinal wave impedance, term B' represents the isotropic shear wave modulus term, and term C' represents the square term of the isotropic longitudinal wave velocity;

[0035] Wherein,

[0036] Preferably, the anisotropic parameter is as follows:

[0037]

[0038] On the other hand, the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the dessert prediction method described above is implemented.

[0039] On yet another aspect, the present invention provides an electronic device, and the electronic device includes:

[0040] a memory storing executable instructions;

[0041] A processor that runs the executable instructions in the memory to implement the dessert prediction method described above.

[0042] The beneficial effects of the dessert prediction method of the present invention are as follows: Aiming at the characteristics of VTI media, based on the VTI media P-wave anisotropic reflection coefficient equation and anisotropic parameters, dessert prediction is carried out, solving the accuracy problem brought by the inversion of the isotropic reflection coefficient equation, and being able to improve the accuracy of dessert prediction, providing a new basis for shale oil and gas reservoir description and dessert prediction.

[0043] The method and device of the present invention have other characteristics and advantages, which will be obvious in the accompanying drawings and subsequent specific embodiments incorporated herein, or will be described in detail in the accompanying drawings and subsequent specific embodiments incorporated herein. These accompanying drawings and specific embodiments are jointly used to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0045] Figure 1 Shows a flowchart of a dessert prediction method according to an embodiment of the present invention.

[0046] Figure 2 Shows three seismic inversion results obtained by the dessert prediction method according to an exemplary embodiment of the present invention.

[0047] Figure 3 Shows Figure 2 The comparison results of the three seismic inversion results with the well-side trace.

[0048] Figure 4 Shows the anisotropic parameters ε and σ obtained by the dessert prediction method according to an exemplary embodiment of the present invention.

[0049] Figure 5 Shows Figure 4 The comparison results of the anisotropic parameters ε and σ with the well-side trace.

[0050] Figure 6 Shows the brittleness result diagram of the dessert prediction method according to an exemplary embodiment of the present invention.

[0051] Figure 7 Shows the organic matter content diagram of the dessert prediction method according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention will be more thorough and complete, and will fully convey the scope of the present invention to those skilled in the art.

[0053] The present invention provides a sweet spot prediction method, including the following steps:

[0054] Step 1: Perform pre-stack seismic inversion according to the vertical transverse isotropic (VTI) medium P-wave anisotropic reflection coefficient equation;

[0055] Step 2: Calculate anisotropic parameters based on the isotropic inversion formula through a step-by-step inversion algorithm;

[0056] Step 3: Perform sweet spot prediction based on the results of pre-stack seismic inversion and anisotropic parameters.

[0057] The sweet spot prediction method of the present invention solves the accuracy problem caused by inversion of the isotropic reflection coefficient equation, can improve the accuracy of sweet spot prediction, and provides a new basis for shale oil and gas reservoir description and sweet spot prediction.

[0058] Example 1

[0059] Figure 1 The flowchart of the sweet spot prediction method according to an embodiment of the present invention is shown. As shown in the figure, the method includes steps 1 to 3.

[0060] Step 1: Perform pre-stack seismic inversion according to the VTI medium P-wave anisotropic reflection coefficient equation.

[0061] In this embodiment, the VTI medium P-wave anisotropic reflection coefficient formula given by Rüger in 1998 is improved to obtain the following VTI medium P-wave anisotropic reflection coefficient equation:

[0062]

[0063] Wherein, represents the anisotropic reflection coefficient, θ represents the P-wave incident angle, V P0 represents the vertical P-wave phase velocity, V S0 represents the vertical S-wave phase velocity, the A term represents the square of the P-wave impedance, the B term represents the anisotropic S-wave shear modulus term, and the C term represents the square of the anisotropic P-wave velocity term;

[0064] Wherein, the pre-stack seismic inversion result is expressed as:

[0065]

[0066] Among them, Z p represents the longitudinal wave impedance, σ is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, μ represents the shear modulus of the transverse wave, and δ and ε represent the Thomsen weak anisotropic parameters.

[0067] Step 2: Calculate the anisotropic parameters based on the isotropic inversion formula through the step-by-step inversion algorithm.

[0068] The isotropic inversion formula is:

[0069]

[0070] Among them, represents the isotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, V P0 represents the vertical phase velocity of the longitudinal wave, V S0 represents the vertical phase velocity of the transverse wave, the A' term represents the square of the longitudinal wave impedance, the B' term represents the isotropic shear modulus term of the transverse wave, and the C' term represents the square term of the isotropic longitudinal wave velocity.

[0071] Among them,

[0072] Therefore, the anisotropic parameters are:

[0073]

[0074] The step-by-step inversion algorithm is an iterative algorithm used to solve linear inverse problems. It gradually updates the model parameters by approaching the target solution step by step until a result that meets the accuracy requirements is achieved.

[0075] The basic steps of the step-by-step inversion algorithm are as follows:

[0076] Initialization: Select an initial model or set some parameters of the initial model.

[0077] Forward calculation: Perform forward calculation based on the initial model to obtain the forward result.

[0078] Inverse calculation: Perform inverse calculation based on the forward result and the mathematical model of the inverse problem to obtain the updated model parameters.

[0079] Model update: Apply the updated model parameters to the initial model to obtain the updated model.

[0080] Judge whether the stopping condition is met: If the difference between the updated model and the target solution is within an acceptable range, or the preset number of iterations is reached, stop the iteration; otherwise, return to the second step and continue with the forward calculation and inverse calculation.

[0081] The advantage of the step-by-step inversion algorithm is that it can gradually approach the target solution, avoiding the computational complexity and memory consumption problems brought about by directly solving large-scale linear equations. At the same time, the step-by-step inversion algorithm can terminate the iterative process in a timely manner through appropriate stopping conditions, preventing the continuous update of model parameters in the wrong direction, which may lead to the result deviating further and further from the true solution.

[0082] Step 3: Conduct sweet spot prediction based on the results of pre-stack seismic inversion and anisotropic parameters.

[0083] By performing pre-stack seismic inversion according to the VTI medium P-wave anisotropic reflection coefficient equation, three pre-stack seismic inversion results, namely A, B, and C, can be obtained. Through the step-by-step inversion algorithm, based on the isotropic inversion formula, in the case of small-angle stacked seismic data input, the shear modulus term of the S-wave and the square term of the P-wave velocity can be calculated, and the anisotropic parameters ε and σ can be calculated. Based on these calculation results, combined with statistical rock physics and the multiple support vector regression algorithm, sweet spot prediction can be achieved.

[0084] Both statistical rock physics and the multiple support vector regression (MSVR) algorithm are effective tools for geological exploration and rock property prediction. Combining them can build a powerful model to achieve sweet spot prediction. The general steps are as follows:

[0085] Data collection and preprocessing: First, we need to collect various physical property data of rocks, such as density, porosity, permeability, etc., as well as data on formation depth, formation thickness, formation age, etc. These data can be obtained through geological survey institutions, oilfield services, etc. Then, we need to preprocess these data, including data cleaning, missing value filling, outlier handling, etc.

[0086] Feature engineering: After collecting and preprocessing the data, we need to perform feature engineering. The goal of feature engineering is to extract meaningful features from the original data for model construction. For example, we can transform the original data into a form that is easier for the model to understand, such as logarithmic transformation or standardization. We can also extract meaningful statistics from the data, such as mean, variance, covariance, etc.

[0087] Construct a statistical rock physics model: Using the preprocessed data and the features extracted by feature engineering, we can construct a statistical rock physics model. This model can describe the relationship between rock properties and formation properties. We can implement this model through algorithms such as least squares method, random forest, neural network, etc.

[0088] Multivariate Support Vector Regression Model: After constructing the statistical rock physics model, we can use the multivariate support vector regression model to predict the properties of sweet spots. This model can predict the location and quantity of sweet spots given a set of rock and formation characteristics. We can optimize the model's parameters through techniques such as cross-validation and grid search.

[0089] Model Evaluation and Optimization: Finally, we need to evaluate the performance of the model and optimize it. We can evaluate the accuracy of the model by comparing the differences between the predicted results and the actual results. If the performance of the model is poor, we need to go back to the feature engineering step and try to extract new features or optimize the extraction methods of existing features. We can also try to adjust the model's parameters to optimize its performance.

[0090] Example 2

[0091] Embodiment 2 provides a sweet spot prediction method, including the following steps:

[0092] Step 1: Data collection and analysis.

[0093] The experimental work area is located in a marine shale gas field in southern China. The structure is relatively simple. Four parts of stacked seismic data were prepared for inversion. The target layer for research is the Longmaxi Formation of the Silurian System. The seismic time window range of the target layer is approximately 1700 - 1900 ms; it includes the data of one well, namely the longitudinal and transverse wave velocities, density, and anisotropy parameters.

[0094] Step 2: Perform prestack seismic inversion according to the VTI medium longitudinal wave anisotropic reflection coefficient equation. Through the step-by-step inversion algorithm, calculate the anisotropy parameters based on the isotropic inversion formula.

[0095] In this embodiment, the VTI medium longitudinal wave anisotropic reflection coefficient equation:

[0096]

[0097] Among them, represents the anisotropic reflection coefficient, θ represents the longitudinal wave incident angle, V P0 represents the longitudinal wave vertical phase velocity, V S0 represents the transverse wave vertical phase velocity, the A term represents the square of the longitudinal wave impedance, the B term represents the anisotropic transverse wave shear modulus term, and the C term represents the square term of the anisotropic longitudinal wave velocity;

[0098] Among them, the prestack seismic inversion result is expressed as:

[0099]

[0100] Among them, Z p\(Z\) represents the longitudinal wave impedance, \(\sigma\) is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, \(\mu\) represents the shear modulus of the transverse wave, and \(\delta\) and \(\varepsilon\) represent the Thomsen weak anisotropic parameters.

[0101] In this embodiment, the isotropic inversion formula is:

[0102]

[0103] where \(R_i\) represents the isotropic reflection coefficient, \(\theta\) represents the longitudinal wave incident angle, \(V_p\) P0 represents the vertical phase velocity of the longitudinal wave, \(V_s\) S0 represents the vertical phase velocity of the transverse wave, the A' term represents the square of the longitudinal wave impedance, the B' term represents the isotropic shear modulus term of the transverse wave, and the C' term represents the square term of the isotropic longitudinal wave velocity.

[0104] where

[0105] Therefore, the anisotropic parameters are:

[0106]

[0107] Based on the seismic data, according to the improved VTI reflection coefficient equation, the square term A of the longitudinal wave impedance, the shear modulus term B of the transverse wave, the square term C of the longitudinal wave velocity, and the anisotropic parameters \(\varepsilon\) and \(\sigma\) are calculated step by step using the generalized linear inversion method. Figure 2 Display the three seismic inversion results obtained in this step, Figure 3 Display the comparison results of the three seismic inversion results with the well-side trace, Figure 4 Display the seismic inversion results of the anisotropic parameters \(\varepsilon\) and \(\sigma\), Figure 5 Display the comparison results of the seismic inversion results of the anisotropic parameters \(\varepsilon\) and \(\sigma\) with the well-side trace.

[0108] Step 3: Perform sweet spot prediction based on the results of pre-stack seismic inversion and anisotropic parameters.

[0109] The organic matter content can describe the volume of hydrocarbons underground, and the lithofacies can describe the lithology and fluid characteristics. Through the inversion results, combined with the A-C lithofacies classification, the brittleness of the reservoir is predicted, and the prediction results are as Figure 6 shown. Using the anisotropic parameter results, the multi-support vector regression algorithm is adopted to realize the prediction of the organic matter content, and the prediction results are as Figure 7 shown.

[0110] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0111] Example 3

[0112] This embodiment provides a sweet spot prediction device, including:

[0113] A pre-stack seismic inversion module for performing pre-stack seismic inversion according to the VTI medium P-wave anisotropic reflection coefficient equation;

[0114] A calculation module for calculating anisotropic parameters based on the isotropic inversion formula through a step-by-step inversion algorithm;

[0115] A prediction module for performing sweet spot prediction according to the result of the pre-stack seismic inversion and the anisotropic parameters.

[0116] In this embodiment, the VTI medium P-wave anisotropic reflection coefficient equation is:

[0117]

[0118] Wherein, represents the anisotropic reflection coefficient, θ represents the P-wave incident angle, V P0 represents the P-wave vertical phase velocity, V S0 represents the S-wave vertical phase velocity, the A term represents the square of the P-wave impedance, the B term represents the anisotropic S-wave shear modulus term, and the C term represents the square of the anisotropic P-wave velocity term;

[0119] Wherein, the pre-stack seismic inversion result is expressed as:

[0120]

[0121] Wherein, Z p represents the P-wave impedance, σ is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, μ represents the S-wave shear modulus, and δ and ε represent the Thomsen weak anisotropic parameters.

[0122] In this embodiment, the isotropic inversion formula is:

[0123]

[0124] Wherein, represents the isotropic reflection coefficient, θ represents the P-wave incident angle, V P0 represents the P-wave vertical phase velocity, V S0 represents the S-wave vertical phase velocity, the A' term represents the square of the P-wave impedance, the B' term represents the isotropic S-wave shear modulus term, and the C' term represents the square of the isotropic P-wave velocity term;

[0125] Wherein,

[0126] In this embodiment, the anisotropic parameters are:

[0127]

[0128] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be repeated herein.

[0129] Example 4

[0130] This embodiment provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the foregoing dessert prediction method.

[0131] A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in a groove storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through an optical fiber cable), or electrical signals transmitted through wires.

[0132] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be repeated herein.

[0133] Example 5

[0134] This embodiment provides an electronic device, comprising:

[0135] a memory storing executable instructions;

[0136] a processor that runs the executable instructions in the memory to implement the foregoing dessert prediction method.

[0137] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0138] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0139] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0140] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when the instructions are executed by the processor of the computer or other programmable data processing apparatus, an apparatus is created that implements the functions / acts specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, a programmable data processing apparatus, and / or other devices to operate in a particular manner, so that the computer-readable medium storing the instructions comprises a manufacture, which includes instructions that implement various aspects of the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0141] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device, such that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0142] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0143] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skill in the technical field to understand the embodiments disclosed herein.

Claims

1. A dessert prediction method, characterized in that, the method comprises: Step 1: Perform prestack seismic inversion according to the VTI medium P-wave anisotropic reflection coefficient equation; Step 2: Calculate anisotropic parameters based on the isotropic inversion formula through a step-by-step inversion algorithm; Step 3: Perform dessert prediction according to the results of the prestack seismic inversion and the anisotropic parameters.

2. The method according to claim 1, characterized in that, the VTI medium P-wave anisotropic reflection coefficient equation is: Among them, represents the anisotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, V P0 represents the vertical phase velocity of the longitudinal wave, V S0 represents the vertical phase velocity of the shear wave, term A represents the square of the longitudinal wave impedance, term B represents the anisotropic shear modulus term of the shear wave, and term C represents the square term of the anisotropic longitudinal wave velocity; wherein, the prestack seismic inversion result is expressed as: where Z p represents the longitudinal wave impedance, σ is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, μ represents the shear modulus of the transverse wave, and δ and ε represent the Thomsen weak anisotropic parameters.

3. The method according to claim 2, characterized in that, the isotropic inversion formula is: Among them, represents the isotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, V P0 represents the vertical phase velocity of the longitudinal wave, V S0 represents the vertical phase velocity of the shear wave, the A' term represents the square of the longitudinal wave impedance, the B' term represents the isotropic shear modulus term of the shear wave, and the C' term represents the square term of the isotropic longitudinal wave velocity; Among them, B′ = μ, 4. The method according to claim 3, characterized in that, the anisotropic parameters are:

5. A dessert prediction device, characterized in that, comprising: A prestack seismic inversion module for performing prestack seismic inversion according to the VTI medium P-wave anisotropic reflection coefficient equation; A calculation module for calculating anisotropic parameters based on the isotropic inversion formula through a step-by-step inversion algorithm; A prediction module for performing dessert prediction according to the results of the prestack seismic inversion and the anisotropic parameters.

6. The device according to claim 5, characterized in that, the VTI medium P-wave anisotropic reflection coefficient equation is: Among them, represents the anisotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, V P0 represents the vertical phase velocity of the longitudinal wave, V S0 represents the vertical phase velocity of the shear wave, term A represents the square of the longitudinal wave impedance, term B represents the anisotropic shear modulus term of the shear wave, and term C represents the square term of the anisotropic longitudinal wave velocity; wherein, the prestack seismic inversion result is expressed as: Among them, Z p represents the longitudinal wave impedance, σ is the equivalent anisotropic parameter defined by Tsvankin and Thomsen in 1994, μ represents the shear modulus of the transverse wave, and δ and ε represent the Thomsen weak anisotropic parameters.

7. The device according to claim 6, characterized in that, the isotropic inversion formula is: Among them, represents the isotropic reflection coefficient, θ represents the incident angle of the longitudinal wave, V P0 represents the vertical phase velocity of the longitudinal wave, V S0 represents the vertical phase velocity of the shear wave, the A' term represents the square of the longitudinal wave impedance, the B' term represents the isotropic shear modulus term of the shear wave, and the C' term represents the square term of the isotropic longitudinal wave velocity; Among them, 8. The device according to claim 7, characterized in that, the anisotropic parameters are:

9. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the dessert prediction method according to any one of claims 1-4.

10. An electronic device, characterized in that, the electronic device comprises: A memory storing executable instructions; A processor, and the processor runs the executable instructions in the memory to implement the dessert prediction method according to any one of claims 1-4.

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