Method for determining probability of oil and gas reservoir distribution based on seismic frequency-variable fluid factor

Abstract

This application discloses a method, apparatus, storage medium, and product for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors, relating to the field of oil and gas exploration technology. The method includes: acquiring pre-stack data of the exploration area and performing pre-stack AVO inversion to obtain initial frequency-varying velocity vectors; constructing a posterior probability distribution of the objective function based on the convolution model and propagation matrix model in seismic wave propagation theory and Bayesian theory; obtaining the posterior probability of the objective function using the Markov chain Monte Carlo method; and determining the frequency-varying velocity vector at the maximum of the posterior probability density function; thereby determining the dispersion gradient of new fluid factors at different frequencies at the dominant seismic wave frequency to determine the distribution probability of oil and gas reservoirs; the dispersion gradient of the new fluid factors is positively correlated with the probability of the existence of oil and gas reservoirs; the parameters constituting the new fluid factors include: Poisson's ratio, P-wave velocity impedance and S-wave velocity impedance, Lamé coefficient, and density. This method can significantly improve the accuracy of oil and gas reservoir identification.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and specifically to a method, apparatus, storage medium, and product for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors. Background Technology

[0002] With the deepening of oil exploration and the increasing complexity of oil and gas reservoirs, the requirements for reservoir prediction accuracy are becoming increasingly stringent. Fluid identification can be performed from seismic observation data using reflection coefficients or inversion impedance data, and the fluid factor defined by seismic inversion parameters plays a crucial role in reservoir fluid identification. In recent years, in-depth research has been conducted on reservoir fluid identification based on seismic P-wave and S-wave data. Finding a fluid factor that is more sensitive to fluid dynamics has become a key factor for more accurate oil and gas reservoir prediction. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, storage medium, and product for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors, in order to solve the technical problem of how to more accurately predict oil and gas reservoirs in the prior art.

[0004] To achieve the above objectives, the first aspect of this application provides a method for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors, comprising:

[0005] Acquire pre-stack data of the exploration area and obtain the frequency-varying initial velocity vector determined by pre-stack AVO inversion of the exploration area. The frequency-varying initial velocity vector includes the initial velocity values ​​of reflected waves at different frequencies, and the initial velocity values ​​of reflected waves at different frequencies are the same.

[0006] Based on the propagation matrix equation and the initial value vector of the frequency-varying velocity in the exploration area, determine the frequency-varying longitudinal wave reflection coefficient vector of the exploration area.

[0007] Based on the convolution model in seismic wave propagation theory, the synthetic seismic forward modeling record is determined according to the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data.

[0008] Based on synthetic seismic forward modeling records and pre-stack data, a posterior probability density function of the frequency-varying velocity function is constructed based on Bayesian theory. Based on the pre-stack data, synthetic seismic forward modeling records, and initial value vector of the frequency-varying velocity, the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data is determined using the Markov chain Monte Carlo method.

[0009] The dispersion curves of the preset fluid factor of reflected waves at different frequencies are determined by the frequency-varying velocity vector at the maximum value of the posterior probability density function, and the dispersion gradient at the dominant frequency of the seismic wave is obtained. Based on the dispersion gradient of the dominant frequency of the seismic wave, the distribution probability of oil and gas reservoirs in the exploration area is determined.

[0010] Among them, the dispersion gradient and the probability of the existence of oil and gas reservoirs are positively correlated. The parameters composed of the preset fluid factors include: Poisson's ratio, P-wave velocity impedance and S-wave velocity impedance, Lamé coefficient, and rock density.

[0011] In this embodiment, determining the dispersion curves of the preset fluid factor for reflected waves of different frequencies based on the frequency-varying velocity vector with the largest posterior probability density and calculating the dispersion gradient at the dominant frequency of the seismic wave includes: determining the Poisson's ratio, P-wave velocity impedance, S-wave velocity impedance, Lamé coefficient, and rock density corresponding to reflected waves of different frequencies based on the frequency-varying velocity vector; determining the preset fluid factor for reflected waves of different frequencies based on the Poisson's ratio, P-wave impedance, S-wave impedance, Lamé coefficient, and rock density; and determining the dispersion gradient of the preset fluid factor at the dominant frequency of the seismic wave based on the preset fluid factor for reflected waves of different frequencies.

[0012] In this embodiment of the application, the preset fluid factor includes:

[0013] ;

[0014] in, For longitudinal wave velocity impedance, For transverse wave velocity impedance, The Lamé coefficient is given. The density of the rock, Poisson's ratio, This is the adjustment coefficient.

[0015] In this application embodiment, the Markov chain Monte Carlo method includes the Metropolis-Hastington algorithm.

[0016] In this embodiment, based on synthetic seismic forward modeling records and pre-stack data, a posterior probability density function of the frequency-varying velocity function is constructed using Bayesian theory. Based on the pre-stack data, synthetic seismic forward modeling records, and initial value vectors of the frequency-varying velocity, the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data is determined using the Markov chain Monte Carlo method. This includes: determining the initial value vector of the frequency-varying velocity as the initial iteration point of the Metropolis-Hastins algorithm; iterating to a preset number of iterations using the Metropolis-Hastins algorithm based on the initial iteration point, synthetic seismic forward modeling records, and pre-stack data to obtain the frequency-varying velocity vector for each iteration; determining the velocity interval of the frequency-varying velocity vector for each iteration based on the frequency-varying velocity vector for each iteration, and determining the posterior probability density distribution of the frequency-varying velocity vector for each iteration within the velocity interval; and determining the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data based on the posterior probability density distribution.

[0017] In this embodiment of the application, the frequency-varying P-wave reflection coefficient vector of the exploration area is determined based on the propagation matrix equation and the initial value vector of the frequency-varying velocity of the exploration area, including: determining the upper spatial propagation matrix, the lower spatial propagation matrix and the intermediate interlayer propagation matrix of the exploration area based on the initial value vector of the frequency-varying velocity; and determining the frequency-varying P-wave reflection coefficient vector of the exploration area based on the upper spatial propagation matrix, the lower spatial propagation matrix and the intermediate interlayer propagation matrix.

[0018] In this embodiment of the application, based on the convolution model of seismic waves, the synthetic seismic forward modeling record is determined according to the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data. This includes: obtaining the synthetic seismic forward modeling record by performing a Fourier transform from the frequency domain to the time domain based on the product of the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data.

[0019] A second aspect of this application provides an apparatus for determining the probability distribution of oil and gas reservoirs based on seismically variable fluid factors, comprising: a memory configured to store instructions; and a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the method for determining the probability distribution of oil and gas reservoirs based on seismically variable fluid factors according to any of the above embodiments.

[0020] A third aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to execute a method for determining the probability of oil and gas reservoir distribution based on seismic frequency-varying fluid factors according to any of the preceding embodiments.

[0021] The fourth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the method for determining the probability of oil and gas reservoir distribution based on seismic frequency-varying fluid factors according to any of the above embodiments.

[0022] The above technical solution, after acquiring pre-stack data of the exploration area and the initial value vector of the frequency-varying velocity of the reflected waves determined by pre-stack AVO (Amplitude Versus Offset) inversion, further determines the synthetic seismic forward modeling record of the corresponding exploration area based on the initial value vector and pre-stack data. Then, based on Bayesian theory and Markov chain Monte Carlo method, iterates each component of the initial value vector based on the synthetic seismic forward modeling record and the initial value vector, thereby determining the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data. This allows the determination of the dispersion gradient of the preset fluid factors at the dominant seismic wave frequency, and the probability distribution of the presence of oil and gas reservoirs in the exploration area can be obtained based on this dispersion gradient. The preset fluid factors include Poisson's ratio, P-wave velocity impedance, S-wave velocity impedance, Lamé coefficient, and rock density, which can distinguish between reservoir fluid and rock fluid types, and improve the accuracy of reservoir fluid prediction and oil and gas identification.

[0023] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0025] Figure 1 The illustration shows a flowchart of a method for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors according to an embodiment of this application.

[0026] Figure 2 The illustration shows a flowchart of a method for determining the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to pre-stack data according to an embodiment of this application.

[0027] Figure 3 This schematic diagram illustrates the sensitivity of a preset fluid factor to changes in porosity compared to a conventional fluid factor and various elastic moduli according to an embodiment of this application.

[0028] Figure 4 This illustration schematically shows the variation of a preset fluid factor with frequency compared to a conventional fluid factor and the variation of various elastic moduli according to an embodiment of this application.

[0029] Figure 5 The diagram illustrates the dispersion gradient of a normalized value of a preset fluid factor and a normalized value of a conventional fluid factor according to an embodiment of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0031] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0032] If the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0033] With the increasing demands of oil and gas reservoir exploration and development, accurate identification of pore fluids in subsurface rocks has become a crucial objective in seismic exploration. Currently, the technological bottleneck in fluid identification methods lies in achieving accurate identification of subsurface reservoir fluids and classifying fluid types. While many methods exist for identifying subsurface fluid information, methods capable of accurately classifying rock fluid types are rare. This research aims to analyze the mechanistic properties of fluid-bearing rocks based on rock physics theory, thereby developing a fluid indicator factor and using this factor to predict the distribution of subsurface reservoir fluids. Analysis shows that current fluid factor research focuses on the equivalent sensitivity of rock fluids, lacking studies on the sensitivity to fluid types. Developing a fluid factor that is sensitive to both reservoir fluids and rock fluid types is essential for improving the accuracy of reservoir fluid prediction and enhancing the precision of oil and gas identification. Based on the above analysis, this application provides a method for determining the probability of oil and gas reservoir distribution based on a newly proposed fluid factor and a seismically variable fluid factor. The newly proposed fluid factor has the highest sensitivity to water saturation and rock porosity, and the method has dual functions of fluid prediction and fluid type identification. It can be used to predict the spatial distribution of underground fluids in the fields of geoscience and engineering, and therefore can be used to predict the distribution of underground oil and gas reservoirs.

[0034] Figure 1 The illustration schematically shows a flowchart of a method for determining the distribution probability of oil and gas reservoirs based on seismically variable fluid factors according to an embodiment of this application. Figure 1 As shown in the embodiments of this application, a method for determining the probability of oil and gas reservoir distribution based on seismically frequency-varying fluid factors is provided. This method may include the following steps:

[0035] S102. Obtain pre-stack data of the exploration area and obtain the frequency-varying initial velocity vector determined by pre-stack AVO inversion of the exploration area, wherein the frequency-varying initial velocity vector includes the initial velocity values ​​of reflected waves of different frequencies, and the initial velocity values ​​of reflected waves of different frequencies are the same.

[0036] S104. Determine the frequency-varying longitudinal wave reflection coefficient vector of the exploration area based on the propagation matrix equation and the initial value vector of the frequency-varying velocity.

[0037] S106. Based on the convolution model in seismic wave propagation theory, determine the synthetic seismic forward modeling record according to the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data.

[0038] S108. Based on the synthetic seismic forward modeling record and pre-stack data, construct the posterior probability density function of the frequency-varying velocity function based on Bayesian theory. Based on the pre-stack data, synthetic seismic forward modeling record, and initial value vector of the frequency-varying velocity, determine the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data using the Markov chain Monte Carlo method.

[0039] S110. Based on the frequency-varying velocity vector with the largest posterior probability density, determine the dispersion curves of the preset fluid factors for reflected waves at different frequencies and calculate the dispersion gradient at the dominant frequency of the seismic wave. Based on the dispersion gradient of the dominant frequency of the seismic wave, determine the probability of oil and gas reservoir distribution in the exploration area. Among them, the dispersion gradient and the probability of the existence of oil and gas reservoirs are positively correlated. The parameters of the preset fluid factors include: Poisson's ratio, P-wave velocity impedance and S-wave velocity impedance, Lamé coefficient, and rock density.

[0040] The method for determining the probability of hydrocarbon reservoir distribution based on seismic frequency-varying fluid factors provided in this application, after acquiring pre-stack data of the exploration area and the initial value vector of frequency-varying velocity of reflected waves determined by pre-stack AVO inversion, further determines the synthetic seismic forward modeling record of the corresponding exploration area based on the initial value vector of frequency-varying velocity and the pre-stack data. Then, based on Bayesian theory and Markov chain Monte Carlo method, iterates each component in the initial value vector of frequency-varying velocity according to the synthetic seismic forward modeling record, pre-stack data, and initial value vector of frequency-varying velocity, thereby determining the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data. This allows the determination of the dispersion gradient of the preset fluid factors at the dominant seismic wave frequency, and the probability distribution of hydrocarbon reservoirs in the exploration area can be obtained based on the dispersion gradient. The preset fluid factors include Poisson's ratio, P-wave velocity impedance, S-wave velocity impedance, Lamé coefficient, and rock density, which can distinguish between reservoir fluid and rock fluid types, and improve the accuracy of reservoir fluid prediction and hydrocarbon identification.

[0041] Understandably, in this embodiment of the application, the pre-stack data of the exploration area in step S102 may include actual observed seismic data. Pre-stack AVO inversion can determine the initial velocity values ​​of reflected waves at different frequencies by analyzing the aforementioned pre-stack data, and thereby obtain the frequency-varying velocity initial value vector of the reflected waves.

[0042] In some embodiments of this application, the preset fluid factor includes:

[0043] ;

[0044] in, For longitudinal wave velocity impedance, For transverse wave velocity impedance, The Lamé coefficient is given. The density of the rock, Poisson's ratio, The adjustment coefficient can be obtained through experimentation or empirical fitting, representing the proportional relationship between longitudinal and transverse waves.

[0045] To determine the aforementioned preset fluid factor and obtain its dispersion gradient at the dominant frequency of the seismic wave, in some embodiments of this application, step S108 may include:

[0046] Based on the frequency-varying velocity vector, determine the Poisson's ratio, longitudinal wave velocity impedance, transverse wave velocity impedance, Lamé coefficient, and rock density corresponding to reflected waves of different frequencies;

[0047] Based on Poisson's ratio, longitudinal wave impedance, transverse wave impedance, Lamé coefficient, and rock density, the preset fluid factor for reflected waves of different frequencies is determined.

[0048] The dispersion gradient of the preset fluid factor at the dominant frequency of the seismic wave is determined based on the preset fluid factor of the reflected waves at different frequencies.

[0049] In some embodiments of this application, step S104 may include:

[0050] Determine the upper spatial propagation matrix, lower spatial propagation matrix, and intermediate interlayer propagation matrix of the exploration area based on the initial value vector of the frequency-varying velocity.

[0051] The frequency-varying P-wave reflection coefficient vector of the exploration area is determined based on the propagation matrix of the upper space, the propagation matrix of the lower space, and the propagation matrix of the intermediate layer.

[0052] Understandably, we assume the exploration area is a horizontally multi-layered model, comprising three parts: an upper semi-infinite elastic space, a lower semi-infinite elastic space, and an intermediate dispersive interlayer (which may contain N horizontal sub-layers). Substituting the initial velocity vector using the propagation matrix method can simulate the seismic AVO response characteristics of the dispersive layer, thus providing a bridge between formation dispersion velocity and seismic response characteristics. For an incident angle of... When a longitudinal wave is incident on a horizontally layered medium, its reflection coefficient can be expressed as: ,in Let represent the reflection coefficient and transmission coefficient of longitudinal and transverse waves, respectively. Their calculation formulas can be expressed as follows:

[0053] ;

[0054] in, and These represent the propagation matrix of the upper space and the propagation matrix of the lower space, respectively. Represents the propagation matrix of the intermediate layer. Indicates the thickness of each layer. It is the P-wave incident vector. According to the above formula, the frequency-varying longitudinal wave reflection coefficient, which is related to the elastic properties of the medium, the thin-layer structure and thickness, the incident wave frequency, and the incident angle, can be obtained.

[0055] In some embodiments of this application, step S106 may include: obtaining a synthetic seismic forward modeling record by performing a Fourier transform from the frequency domain to the time domain based on the product of the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data.

[0056] Understandably, based on the seismic convolution model, the synthetic seismic forward modeling record can be obtained by performing an inverse Fourier transform on the frequency domain seismic record:

[0057] ;

[0058] Wherein represents the incident angle of the seismic wavelet corresponding to the pre-stack data, represents the reflection coefficient, R is the frequency-varying P-wave reflection coefficient at different incident angles, represents the seismic wavelet corresponding to the pre-stack data in the time domain, and represents the seismic wavelet corresponding to the pre-stack data in the frequency domain.

[0059] After obtaining the synthetic seismic forward modeling record, step S108 is executed to determine the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data. The target posterior probability density function based on Bayesian theory in step S108 can be, for example:

[0060] ;

[0061] in, Let be the likelihood function, representing the probability that, given a frequency-varying velocity vector, corresponding actual observed seismic data exists. Let be the probability distribution function of the frequency-varying velocity. Let be the probability distribution function of the actual observed seismic data. The likelihood function can be determined based on the actual observed seismic data, synthetic seismic forward modeling records, and frequency-varying velocity functions.

[0062] In some embodiments of this application, the Markov chain Monte Carlo method in step S108 includes the Metropolis-Hastington algorithm.

[0063] Further, see Figure 2 In some embodiments of this application, step S108 includes:

[0064] S202. Determine the initial value vector of the frequency-varying velocity as the initial iteration point of the Metropolis-Hastington algorithm;

[0065] S204. Based on the initial iteration point, the synthetic seismic forward modeling record, and the pre-stack data, the Metropolis-Hastington algorithm is used to iterate to the preset number of iterations to obtain the frequency-varying velocity vector for each iteration.

[0066] S206. Based on the frequency-varying velocity vector of each iteration, determine the velocity range of the frequency-varying velocity vector of each iteration, and determine the posterior probability density distribution of the frequency-varying velocity vector of each iteration within the velocity range.

[0067] S208. Based on the posterior probability density distribution, determine the frequency-varying velocity vector that has the largest posterior probability density relative to the actual observed seismic data.

[0068] The velocity range of the frequency-varying velocity vector obtained in each iteration of step S206 can be fitted to obtain the posterior probability density of the frequency-varying velocity vector in each iteration of the velocity range, and used to represent the target posterior probability density function based on Bayesian theory mentioned above. Then, the frequency-varying velocity vector with the largest posterior probability density relative to the actual observed seismic data can be determined accordingly.

[0069] After determining the frequency-varying velocity vector with the highest posterior probability density relative to the actual observed seismic data, step S110 can be executed to determine the dispersion curves of the preset fluid factors of reflected waves at different frequencies and to obtain the dispersion gradient at the dominant frequency of the seismic wave, thereby determining the probability of oil and gas reservoir distribution in the exploration area.

[0070] To verify the application effect of the preset fluid factor Figures 3 to 5 The sensitivity of conventional and new technologies to rock porosity and seismic dispersion was compared, demonstrating the advantages of the new technology in fluid indication and fluid type classification. Furthermore, combining the frequency-varying parameter inversion results with the new parameters revealed a stronger frequency dependence. This is crucial for reservoir fluid identification.

[0071] Figure 3 To demonstrate the advantages of the preset fluid factor in fluid identification, the preset fluid factor exhibits higher sensitivity to changes in porosity compared to conventional fluid factors and various elastic moduli. This implies a higher correlation between the porosity of underground rocks and the preset fluid factor, highlighting its unique advantage in fluid identification. Porosity sensitivity analysis verifies the feasibility of this method in fluid type classification.

[0072] Figure 4This study demonstrates the advantages of pre-defined fluid factors in seismic dispersion identification. Within a seismic frequency band, the pre-defined fluid factors, based on the oil and gas reservoir distribution probability provided in this application, exhibit a higher degree of variation with frequency compared to conventional fluid factors and various elastic moduli. This implies a higher correlation between subsurface rock fluid dispersion and seismic frequency, highlighting the unique advantage of pre-defined fluid factors in seismic dispersion identification. Fluid factor frequency dependence analysis verifies the feasibility of this method in fluid identification.

[0073] Figure 5 The dispersion gradients of the normalized values ​​of the preset fluid factor and the conventional fluid factor are shown. The dispersion gradient of the preset fluid factor exhibits higher sensitivity than that of the conventional fluid factor and the dispersion gradients of various elastic moduli. This means that using the dispersion characteristics of the preset fluid factor to identify fluids has higher accuracy, demonstrating the unique advantages of using the dispersion gradient of this preset fluid factor in fluid identification. Dispersion gradient feature analysis also verifies the feasibility of this method in fluid identification.

[0074] In summary, the method for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors and its preset fluid factors provided in this application have the characteristics of being highly sensitive to rock porosity and seismic dispersion. The high frequency dependence has great potential value for reservoir fluid identification and reservoir characterization.

[0075] This application also provides an apparatus for determining the probability of oil and gas reservoir distribution based on seismically variable fluid factors, comprising: a memory and a processor. The memory is configured to store instructions. The processor is configured to retrieve instructions from the memory and, when executing the instructions, to implement the method for determining the probability of oil and gas reservoir distribution based on seismically variable fluid factors provided in any of the above embodiments.

[0076] This application also provides a machine-readable storage medium storing instructions that cause a machine to execute the above-described method for determining the probability distribution of oil and gas reservoirs based on seismic frequency-varying fluid factors.

[0077] This application also provides a computer program product, including a computer program. When executed by a processor, the computer program implements a method for determining the probability of oil and gas reservoir distribution based on seismically variable fluid factors, as provided in any of the above embodiments.

[0078] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will 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 program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0082] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0083] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0084] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0086] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining the probability of oil and gas reservoir distribution based on seismically variable fluid factors, characterized in that, include: Acquire pre-stack data of the exploration area and obtain the frequency-varying initial velocity vector determined by performing pre-stack AVO inversion of the exploration area, wherein the frequency-varying initial velocity vector includes the initial velocity values ​​of reflected waves at different frequencies, and the initial velocity values ​​of reflected waves at different frequencies are the same. Based on the propagation matrix equation of the exploration area and the initial value vector of the frequency-varying velocity, determine the frequency-varying longitudinal wave reflection coefficient vector of the exploration area; Based on the convolution model in seismic wave propagation theory, the synthetic seismic forward modeling record is determined according to the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data. Based on the synthetic seismic forward modeling record and the pre-stack data, a posterior probability density function of the frequency-varying velocity function is constructed based on Bayesian theory. Based on the pre-stack data, the synthetic seismic forward modeling record, and the initial value vector of the frequency-varying velocity, the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data is determined using the Markov chain Monte Carlo method. The dispersion curves of the preset fluid factor of the reflected waves at different frequencies are determined based on the frequency-varying velocity vector at the maximum value of the posterior probability density function, and the dispersion gradient at the main frequency of the seismic wave is obtained. Based on the dispersion gradient at the main frequency of the seismic wave, the oil and gas reservoir distribution probability of the exploration area is determined. The dispersion gradient is positively correlated with the probability of the existence of the oil and gas reservoir. The parameters constituted by the preset fluid factor include: Poisson's ratio, P-wave velocity impedance and S-wave velocity impedance, Lamé coefficient, and rock density.

2. The method according to claim 1, characterized in that, The step of determining the dispersion curves of the preset fluid factor for reflected waves of different frequencies based on the frequency-varying velocity vector at the maximum value of the posterior probability density function and calculating the dispersion gradient at the dominant frequency of the seismic wave includes: Based on the frequency-varying velocity vector, determine the Poisson's ratio, longitudinal wave velocity impedance, transverse wave velocity impedance, Lamé coefficient, and rock density corresponding to reflected waves of different frequencies; Based on the Poisson's ratio, the longitudinal wave velocity impedance, the transverse wave velocity impedance, the Lamé coefficient, and the rock density, the preset fluid factor for the reflected waves of different frequencies is determined. The dispersion gradient at the dominant frequency of the seismic wave is determined based on the preset fluid factor of the reflected waves at different frequencies.

3. The method according to claim 1, characterized in that, The preset fluid factor includes: ； in, For longitudinal wave velocity impedance, For transverse wave velocity impedance, The Lamé coefficient is given. The density of the rock, Poisson's ratio, This is the adjustment coefficient.

4. The method according to claim 1, characterized in that, The Markov chain Monte Carlo method includes the Metropolis-Hastington algorithm.

5. The method according to claim 4, characterized in that, The step of constructing a posterior probability density function of the frequency-varying velocity function based on Bayesian theory using the synthetic seismic forward modeling record and the pre-stack data, and determining the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data using the Markov chain Monte Carlo method, includes: The initial value vector of the frequency-varying velocity is determined as the initial iteration point of the Metropolis-Hastington algorithm; Based on the initial iteration point, the synthetic seismic forward modeling record, and the pre-stack data, the Metropolis-Hastington algorithm is used to iterate to a preset number of iterations to obtain the frequency-varying velocity vector for each iteration. Based on the frequency-varying velocity vectors of each iteration, determine the velocity range of the frequency-varying velocity vectors of each iteration, and determine the posterior probability density distribution of the frequency-varying velocity vectors of each iteration within the velocity range. Based on the posterior probability density distribution, determine the frequency-varying velocity vector at the maximum value of the posterior probability density function relative to the pre-stack data.

6. The method according to claim 1, characterized in that, The step of determining the frequency-varying P-wave reflection coefficient vector of the exploration area based on the propagation matrix equation of the exploration area and the initial value vector of the frequency-varying velocity includes: The upper spatial propagation matrix, lower spatial propagation matrix, and intermediate interlayer propagation matrix of the exploration area are determined based on the initial value vector of the frequency-varying velocity. The frequency-varying longitudinal wave reflection coefficient vector of the exploration area is determined based on the upper spatial propagation matrix, the lower spatial propagation matrix, and the intermediate interlayer propagation matrix.

7. The method according to claim 1, characterized in that, The convolution model based on seismic wave propagation theory determines the synthetic seismic forward modeling record based on the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data, including: The synthetic seismic forward modeling record is obtained by performing a Fourier transform from the frequency domain to the time domain on the product of the frequency-varying P-wave reflection coefficient vector and the seismic wavelet corresponding to the pre-stack data.

8. A device for determining the probability of oil and gas reservoir distribution based on seismic frequency-varying fluid factors, characterized in that, include: The memory is configured to store instructions; as well as A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for determining the probability distribution of oil and gas reservoirs based on seismically variable fluid factors according to any one of claims 1 to 7.

9. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the method for determining the probability of oil and gas reservoir distribution based on seismic frequency-varying fluid factors according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the distribution probability of oil and gas reservoirs based on seismic frequency-varying fluid factors according to any one of claims 1 to 7.

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