Natural gas hydrate identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a natural gas hydrate identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring seismic data of a target area; the seismic data are imported into a target program, intercept data are obtained through operation according to the intercept attribute, and density reflection coefficient data are obtained through operation according to the density reflection coefficient principle; obtaining intercept density reflection coefficient data according to the ratio of the intercept data to the density reflection coefficient data; and screening in the target area based on the intercept density reflection coefficient data to obtain a natural gas hydrate area. According to the method, the intercept data and the density reflection coefficient data of the comprehensive seismic data are creatively adopted, the intercept data and the density reflection coefficient data are subjected to ratio processing for natural gas hydrate recognition, and compared with single utilization of a certain kind of data, the problem of multiplicity of solutions in the judgment process can be effectively solved by utilizing the intercept data and the density reflection coefficient data, and the accuracy of natural gas hydrate recognition is improved. And the identification accuracy of the natural gas hydrate reservoir is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a natural gas hydrate identification method, device, electronic equipment and storage medium. Background Art

[0002] The current common research method for natural gas hydrate identification is to use the inverted longitudinal wave impedance and shear wave impedance to identify natural gas hydrates. When the inverted longitudinal wave impedance and shear wave impedance show high values, it is determined whether the formation contains natural gas hydrates. However, the related technology has the following problems: Disadvantage 1: Hydrates exhibit high P-wave and S-wave velocities. However, since formation velocities are affected not only by hydrates but also by formation lithology, the increased P-wave impedance can be difficult to determine solely whether the increased P-wave impedance is due to the presence of hydrates or the influence of formation lithology.

[0003] Disadvantage 2: When lithologic factors increase formation velocity, density data often exhibits abnormally high values. However, when hydrates increase formation velocity, formation density remains virtually unchanged. However, due to limitations in inversion methods and data quality, it is often impossible to accurately obtain density data, making it difficult to accurately identify natural gas hydrates. Summary of the Invention

[0004] The present invention aims to at least partially address the limitations of related technologies. To this end, the present invention provides a natural gas hydrate identification method, device, electronic device, and storage medium that can accurately identify natural gas hydrates.

[0005] In one aspect, an embodiment of the present invention provides a method for identifying natural gas hydrates, comprising the following steps: Acquire seismic data of the target area; Import the seismic data into the target program, calculate the intercept data according to the intercept attribute, and calculate the density reflection coefficient data according to the density reflection coefficient principle; According to the ratio of the intercept data to the density reflection coefficient data, the intercept density reflection coefficient data is obtained; The natural gas hydrate area is screened in the target area based on the intercept density reflectance data.

[0006] Optionally, the method further comprises the following steps: Construct a function script based on the intercept attribute and the calculation logic corresponding to the density reflection coefficient principle; The target program is obtained based on function script integration.

[0007] Optionally, obtaining intercept data according to intercept attribute calculation includes the following steps: Obtain velocity and density data of the target area based on seismic data analysis; The velocity data includes the velocity difference and the average velocity of the strata above and below the interface in the target area, and the density data includes the density difference and the average density of the strata above and below the interface in the target area; According to the velocity data and density data, the intercept data is obtained by using the preset intercept expression calculation; The intercept expression is:

[0008] Where, K represents intercept data; Indicates the speed difference; Indicates the average speed; Indicates the density difference; represents the average density.

[0009] Optionally, obtaining density reflection coefficient data according to the density reflection coefficient principle includes the following steps: Based on the target equation, the effective signal term and noise term of the seismic data are separated, and then the density reflection coefficient data is calculated; The target equation is based on the Zoplitz equation and is derived by introducing noise factors and singular value decomposition. The expression of the target equation is:

[0010] Where, R Represents the reflection coefficient matrix, which includes the longitudinal wave reflection coefficient, the shear wave reflection coefficient, and the density reflection coefficient. The density reflection coefficient data is obtained based on the calculation result of the density reflection coefficient; B represents the coefficient matrix; T represents the transpose symbol; R pp Represents the reflection coefficient corresponding to different incident angles; N represents the noise factor; Q 、 D 、 P is the decomposition matrix of the coefficient matrix.

[0011] Optionally, based on the Zoplitz equation, a target equation is derived by introducing a noise factor and performing singular value decomposition, including the following steps: The noise factor is introduced on the basis of the simplified formula of the Zoplitz equation to obtain the first equation; Among them, the expression of the first equation is: ; Solve the first equation by singular value decomposition and obtain the decomposition matrix of the coefficient matrix as the second equation; The expression of the second equation is:B=PDQ Where, P and Q are orthogonal matrices of different dimensions, D is a diagonal matrix; Based on the second equation, the generalized inverse matrix of the coefficient matrix is solved according to the singular value decomposition method, and then substituted into the second equation to obtain the target equation.

[0012] Optionally, before the step of obtaining intercept density reflection coefficient data according to the ratio of the intercept data to the density reflection coefficient data, the method further comprises the following steps: The intercept data and density reflection coefficient data were normalized.

[0013] Optionally, screening a natural gas hydrate region in a target region based on intercept density reflectance data comprises the following steps: Comparing the intercept density reflection coefficient data of each area in the target area with a preset threshold value, and determining that the corresponding area is a natural gas hydrate area when the intercept density reflection coefficient data is greater than the preset threshold value; The intercept density reflection coefficient data of each area in the target area is obtained based on the ratio of the intercept data to the density reflection coefficient data of the corresponding area.

[0014] In another aspect, an embodiment of the present invention provides a natural gas hydrate identification device, comprising: The first module is used to obtain seismic data of the target area; The second module is used to import seismic data into the target program, obtain intercept data according to intercept attribute calculation, and obtain density reflection coefficient data according to density reflection coefficient principle calculation; The third module is used to obtain intercept density reflection coefficient data according to the ratio of the intercept data to the density reflection coefficient data; The fourth module is used to screen out natural gas hydrate areas in the target area based on intercept density reflection coefficient data.

[0015] Optionally, the apparatus further includes a fifth module, configured to perform the following operations: Construct a function script based on the intercept attribute and the calculation logic corresponding to the density reflection coefficient principle; The target program is obtained based on function script integration.

[0016] Optionally, the device further comprises: The sixth module is used to normalize the intercept data and the density reflection coefficient data.

[0017] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned natural gas hydrate identification method.

[0018] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. The program executable by the processor is used to implement the above-mentioned natural gas hydrate identification method when executed by the processor.

[0019] The embodiment of the present invention obtains seismic data of a target area; imports the seismic data into a target program, obtains intercept data based on intercept attribute calculations, and obtains density reflection coefficient data based on the density reflection coefficient principle; obtains intercept density reflection coefficient data based on the ratio of the intercept data to the density reflection coefficient data; and screens the target area for natural gas hydrate areas based on the intercept density reflection coefficient data. The present invention innovatively uses intercept data and density reflection coefficient data from integrated seismic data, performs ratio processing on the two, and identifies natural gas hydrates. Compared with using only one type of data, the use of intercept data and density reflection coefficient data can effectively eliminate the multi-solution problem in the judgment process and effectively improve the accuracy of natural gas hydrate reservoir identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0021] Figure 1 This is a schematic diagram of an implementation environment for a natural gas hydrate identification method provided by an embodiment of the present invention; Figure 2 1 is a flow chart of a method for identifying natural gas hydrates provided by an embodiment of the present invention; Figure 3 A schematic diagram of the expansion process of computing intercept data provided by an embodiment of the present invention; Figure 4 A schematic diagram of an expansion process for deriving a target equation according to an embodiment of the present invention; Figure 5 A schematic diagram of an expanded process of the natural gas hydrate identification method provided in an embodiment of the present invention; Figure 6 A schematic diagram of another expanded flow chart of the natural gas hydrate identification method provided in an embodiment of the present invention; Figure 7 A schematic diagram of the expanded flow of step S400 provided in an embodiment of the present invention; Figure 8 A schematic diagram of an example model for identifying natural gas hydrates using intercept density reflection coefficient provided in an embodiment of the present invention; Figure 9A schematic structural diagram of a natural gas hydrate identification device provided by an embodiment of the present invention; Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0024] References to "embodiments" in this disclosure mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the disclosure. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] It is understood that the natural gas hydrate identification method provided in the embodiments of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0026] To facilitate understanding of the technical solutions of the present invention, the following are first explained regarding the technical features that may appear in the embodiments of the present invention: like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1, the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to the network in a wireless or wired manner to complete data transmission and exchange.

[0027] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0028] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0029] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0030] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a natural gas hydrate identification method. The following description will be made using the natural gas hydrate identification method applied in the server 101 as an example. It can be understood that the natural gas hydrate identification method can also be applied in the terminal 102.

[0031] Reference Figure 2 , Figure 2 This is a flowchart of a natural gas hydrate identification method applied to a server according to an embodiment of the present invention. The execution subject of the natural gas hydrate identification method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps: S100, acquiring seismic data of a target area; For example, in some specific implementations, a seismic signal receiving and recording system can be used to collect seismic data. Conventionally, the device that senses seismic signals is called a geophone, and the device that collects and records seismic signals is called a seismic exploration instrument (or seismic (recording) instrument). These are key components of a seismic instrument. Specifically, the geophone and seismic exploration instrument work together to achieve complete seismic data collection.

[0032] S200, importing seismic data into the target program, obtaining intercept data according to intercept attribute calculation, and obtaining density reflection coefficient data according to density reflection coefficient principle calculation; It should be noted that, in some embodiments, Figure 3 As shown, obtaining intercept data according to intercept attribute calculation may include the following steps: S211, obtaining velocity data and density data of the target area based on seismic data analysis; wherein the velocity data includes the velocity difference and the average velocity value of the strata above and below the interface in the target area, and the density data includes the density difference and the average density value of the strata above and below the interface in the target area; S212, obtaining intercept data according to the velocity data and density data using a preset intercept expression calculation; wherein the intercept expression is:

[0033] Where, K represents intercept data; Indicates the speed difference; Indicates the average speed; Indicates the density difference; represents the average density.

[0034] For example, in some embodiments, the intercept attribute approximates the reflection coefficient of a P-wave at normal incidence. The intercept profile is effectively the P-wave profile at normal incidence. Compared to conventional stacked profiles, the intercept profile is closer to a zero-offset profile. A large intercept value indicates a large difference in P-wave velocity between the upper and lower layers.

[0035] The intercept expression is:

[0036] in, and are the velocity difference and average value of the upper and lower formations on the interface, and is the density difference and average value of the strata above and below the interface.

[0037] It should be noted that in some embodiments, obtaining density reflection coefficient data according to the density reflection coefficient principle may include the following steps: separating the effective signal term and the noise term of the seismic data based on the target equation, and then calculating the density reflection coefficient data; wherein the target equation is based on the Zoplitz equation and is derived by introducing a noise factor and performing singular value decomposition; the expression of the target equation is:

[0038] Where, R Represents the reflection coefficient matrix, which includes the longitudinal wave reflection coefficient, the shear wave reflection coefficient, and the density reflection coefficient. The density reflection coefficient data is obtained based on the calculation result of the density reflection coefficient;B represents the coefficient matrix; T represents the transpose symbol; R pp Represents the reflection coefficient corresponding to different incident angles; N represents the noise factor; Q 、 D 、 P is the decomposition matrix of the coefficient matrix.

[0039] For example, in some specific embodiments, the density parameter is related to the porosity, pore fluid properties, fluid saturation and skeleton mineral composition of the formation. In general, the density inversion result is closely related to the signal-to-noise ratio of the data and the angle of incidence. Theoretically, in the absence of noise, the density parameter can be reliably estimated using near-offset seismic data. However, density inversion is highly unstable, and the offset range and noise are the most important factors affecting the inversion effect. Once noise is added, for pre-stack AVO inversion, it is necessary to use far-offset seismic data to extract the density parameter. However, if the data signal-to-noise ratio is relatively low, the density parameter cannot be accurately obtained. Therefore, it is unrealistic to increase the offset distance of the seismic data in order to accurately extract density information.

[0040] First, the Gidlow approximation formula is re-derived to eliminate the influence of unnecessary disturbance sources on the density inversion results. At the same time, an effective noise suppression process is introduced to improve the pathological degree of the pre-stack AVO inversion problem. It is proved that the inversion method has good stability and noise resistance, and can achieve satisfactory density inversion results even in the case of relatively high noise and the absence of long-offset seismic data.

[0041] Among them, in some embodiments, such as Figure 4 As shown, based on the Zoplitz equation, the target equation is derived by introducing a noise factor and singular value decomposition, which may include the following steps: S221, introducing a noise factor based on the simplified formula of the Zoplitz equation to obtain a first equation; wherein the expression of the first equation is: ; S222, solve the first equation by singular value decomposition to obtain a decomposition matrix of the coefficient matrix as the second equation; wherein the expression of the second equation is: B=PDQ Where, P and Q are orthogonal matrices of different dimensions, D is a diagonal matrix; S223. Based on the second equation, the generalized inverse matrix of the coefficient matrix is solved according to the singular value decomposition method, and then substituted into the second equation to reversely obtain the target equation.

[0042] For example, in some specific implementations, the basic principle of density reflection coefficient can be implemented as follows: Introducing noise factors based on Zoeppritz's simplified formula N , the formula becomes: ; Where, B is the coefficient matrix, R is the reflection coefficient matrix to be determined , 、 、 are the longitudinal wave reflection coefficient, shear wave reflection coefficient, and density reflection coefficient, respectively. is the reflection coefficient corresponding to different incident angles.

[0043] The above equation is solved by the singular value decomposition method as follows: The matrix B Performing singular value decomposition yields: B=PDQ ; In the formula P It is an orthogonal matrix with M rows and 3 columns; D It is a 3-row, 3-column diagonal matrix, and the diagonal elements are positive or 0; Q It is an orthogonal matrix with 3 rows and 3 columns. Solved by singular value decomposition method B The generalized inverse matrix of , and then solving the above equation can be obtained:

[0044] Through the above formula, the effective signal term and noise term of seismic data can be separated, and finally the density reflection coefficient data volume can be calculated. .

[0045] Among them, in some embodiments, such as Figure 5 As shown, the method may further include the following steps: T100, constructing a function script according to the intercept attribute and the operation logic corresponding to the density reflection coefficient principle; T200, obtaining a target program based on the function script integration.

[0046] For example, in some specific implementations, intercept data can be obtained by importing seismic data into a matlab program and performing calculations, and density reflection coefficient data can be obtained by importing seismic data into a matlab program and performing calculations; in some specific application scenarios, the aforementioned related processing logic for obtaining intercept data based on intercept attribute calculations and obtaining density reflection coefficient data based on the density reflection coefficient principle can be integrated into the matlab program to be implemented as a target program.

[0047] Among them, in some embodiments, such as Figure 6 As shown, before executing step S300, the method may further include the following steps: S500, normalizing the intercept data and the density reflection coefficient data.

[0048] For example, in some embodiments, both the intercept data and the density reflectance data are normalized to [0, 1], especially in areas with weaker energy, so that the energy is consistent. Specifically, the normalization process helps to eliminate the influence of different dimensions or magnitudes on data analysis.

[0049] S300, obtaining intercept density reflection coefficient data according to the ratio of the intercept data to the density reflection coefficient data; For example, in some specific embodiments, the intercept data I and the density reflection coefficient The intercept density reflection coefficient data is obtained by comparison. The ratio is defined as the intercept density reflection coefficient ratio, and the expression is as follows: .

[0050] S400, screening a natural gas hydrate region in the target region based on the intercept density reflection coefficient data; It should be noted that, in some embodiments, Figure 7 As shown, step S400 may include the following steps: S401, comparing the intercept density reflection coefficient data of each area in the target area with a preset threshold; S402, when the intercept density reflection coefficient data is greater than the preset threshold, determining that the corresponding area is a natural gas hydrate area; wherein the intercept density reflection coefficient data of each area in the target area is obtained based on the ratio of the intercept data of the corresponding area to the density reflection coefficient data.

[0051] For example, in some embodiments, the intercept density reflection coefficient ratio can effectively indicate the reaction reservoir properties. When the formation does not contain natural gas hydrate, When the ratio is 0.05, it indicates that the formation contains natural gas hydrates. The larger the ratio, the greater the probability of containing natural gas hydrates. Specifically, in order to more accurately screen the natural gas hydrate area, the preset threshold value can be appropriately increased, such as from 1 to 1.05, 1.1, etc. (it can be adjusted according to actual needs).

[0052] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0053] First, it should be noted that the common method for identifying natural gas hydrates primarily relies on inversion, which can obtain P-wave velocity, S-wave velocity, and density. During hydrate identification, hydrates appear as high P-wave and S-wave velocities. However, since formation velocity is affected not only by hydrates but also by formation lithology, resulting in an increase in P-wave impedance, it is difficult to definitively determine whether the increase in P-wave impedance is due to the presence of hydrates or the influence of formation lithology. When lithology causes an increase in formation velocity, density data often exhibits abnormally high values. However, when hydrates cause an increase in formation velocity, formation density remains virtually unchanged. However, due to limitations in inversion methods and data quality, accurate density data is often impossible to obtain. These two factors make the identification of natural gas hydrates challenging.

[0054] In view of this, an embodiment of the present invention provides a method for identifying natural gas hydrates, the implementation principle of which is as follows: Intercept properties: The intercept property approximates the reflection coefficient of a P-wave at vertical incidence. The intercept profile is actually the P-wave profile at vertical incidence. Compared to conventional stacked profiles, the intercept profile is closer to the zero-offset profile. Large intercept values indicate a large difference in P-wave velocity between the upper and lower layers.

[0055] The intercept expression is:

[0056] in, and are the velocity difference and average value of the upper and lower formations on the interface, and is the density difference and average value of the strata above and below the interface.

[0057] Density reflectance: Density parameters are related to the porosity, pore fluid properties, fluid saturation, and skeletal mineral composition of the formation. Generally speaking, density inversion results are closely related to the data's signal-to-noise ratio and the angle of incidence. Theoretically, in the absence of noise, density parameters can be reliably estimated using close-offset seismic data. However, density inversion is highly unstable, with offset range and noise being the most important factors affecting the inversion effect. Once noise is introduced, prestack AVO inversion requires the use of long-offset seismic data to extract density parameters. However, if the data's signal-to-noise ratio is relatively low, the density parameters cannot be accurately determined. Therefore, increasing the offset of seismic data to accurately extract density information is unrealistic.

[0058] First, the Gidlow approximation formula is re-derived to eliminate the influence of unnecessary disturbance sources on the density inversion results. At the same time, an effective noise suppression process is introduced to improve the pathological degree of the pre-stack AVO inversion problem. It is proved that the inversion method has good stability and noise resistance, and can achieve satisfactory density inversion results even in the case of relatively high noise and the absence of long-offset seismic data.

[0059] The basic principle of density reflection coefficient is as follows: The noise factor N is introduced based on the Zoeppritz simplified formula, and the formula becomes:

[0060] Where, B is the coefficient matrix, R is the reflection coefficient matrix to be determined , 、 、 are the longitudinal wave reflection coefficient, shear wave reflection coefficient, and density reflection coefficient, respectively. is the reflection coefficient corresponding to different incident angles.

[0061] The above equation is solved by the singular value decomposition method as follows: The matrix B Performing singular value decomposition yields: B=PDQ In the formula P It is an orthogonal matrix with M rows and 3 columns; D It is a 3-row, 3-column diagonal matrix, and the diagonal elements are positive or 0; Q It is an orthogonal matrix with 3 rows and 3 columns. Solved by singular value decomposition method B The generalized inverse matrix of , and then solving the above equation can be obtained:

[0062] Through the above formula, the effective signal term and noise term of seismic data can be separated, and finally the density reflection coefficient data volume can be calculated. .

[0063] Data volume operations: The intercept data I and the density reflection coefficient The energy is normalized so that its energy range is between 0 and 1, and then the two are ratioed. The ratio is defined as the intercept density reflection coefficient ratio, and the expression is as follows:

[0064] The intercept density reflection coefficient ratio can effectively indicate the reaction reservoir properties. When the formation does not contain natural gas hydrate, When the ratio is , it indicates that the formation contains natural gas hydrates. The larger the ratio, the greater the probability of containing natural gas hydrates.

[0065] Theoretical signal test: Set theoretical geological model (such as Figure 8 The stratum is divided into three layers. The velocities (VP) of the upper, middle, and lower layers are 2000 m / s, 2400 m / s, and 2000 m / s, respectively. The densities (DEN) are 2 g / cm 3 , 2.16g / cm 3 , 2g / cm 3 The velocity of the sandy reservoir is 3000m / s and the density is 2.2g / cm 3 The velocity of the sandy reservoir is 3000m / s and the density is 2.16g / cm 3 The sandy reservoir and the gas hydrate reservoir have the same velocity but different density. The intercept data calculated by this model can be found in Figure 8 In the layer marked with B, black represents the crest and white represents the trough. In non-sand reservoirs and non-gas hydrate reservoirs, the top boundary of the formation is the crest and the bottom boundary of the formation is the trough. Compared with sandy reservoirs and gas hydrate reservoirs, the energy is weaker. Since the sandy reservoir and gas hydrate reservoir have the same velocity, the reflection coefficient of the sandy reservoir and the gas hydrate reservoir is the same, so the seismic phase axis energy is basically the same. The density reflection coefficient profile obtained by this seismic model is shown in Figure 8 In the layer marked C, it can be seen that the density reflection coefficient energy of the upper and lower interfaces of the sandy reservoir is larger, higher than that of the adjacent strata, while the energy of the gas hydrate reservoir is weaker, comparable to that of the adjacent strata. The reason is that the density of the gas hydrate reservoir is the same as that of the adjacent strata. This is in line with objective laws. Figure 8 In the layer marked D, it can be seen that in the natural gas hydrate reservoir, the ratio is significantly greater than 1, which is a good indicator of natural gas hydrate, while in the sandy reservoir, the intercept density reflection coefficient ratio is not significantly different from the adjacent strata.

[0066] In summary, the intercept density reflectance coefficient can be used to identify natural gas hydrate reservoirs well.

[0067] In some specific application scenarios, the embodiments of the present invention can be implemented through the following process: 1. Preparation of software and hardware conditions: A computer capable of running MATLAB 2018b and MATLAB 2018b software.

[0068] 2. Obtaining intercept data: Import the seismic data into the MATLAB program and perform calculations to obtain the intercept data.

[0069] 3. Calculation of density reflection coefficient: Import the seismic data into the MATLAB program and perform calculations to obtain the density reflection coefficient data.

[0070] 4. Data normalization processing: Normalize both the intercept data and the density reflection coefficient data to [0,1], especially in areas with weaker energy, so that the energy is consistent.

[0071] 5. Obtaining the intercept density reflection coefficient: The intercept data is compared with the density reflection coefficient to obtain the intercept density reflection coefficient data.

[0072] 6. Characterization of natural gas hydrate: In the obtained intercept density reflection coefficient data, the area with a value greater than 1 is screened, and the area can effectively indicate natural gas hydrate.

[0073] In summary, the present invention improves the accuracy of natural gas hydrate identification by integrating intercept data and pressure-noise density gradient data from seismic data and performing ratio processing on the two. Specifically, the present invention utilizes both intercept data and density reflection coefficient data, effectively eliminating the multi-solution problem in the judgment process compared to using only one data type, and effectively improving the accuracy of natural gas hydrate reservoir identification.

[0074] On the other hand, Figure 9 As shown, an embodiment of the present invention provides a natural gas hydrate identification device 900, which may include: The first module 901 is used to obtain seismic data of a target area; The second module 902 is used to import the seismic data into the target program, obtain intercept data according to the intercept attribute calculation, and obtain density reflection coefficient data according to the density reflection coefficient principle; The third module 903 is used to obtain intercept density reflection coefficient data according to the ratio of the intercept data to the density reflection coefficient data; The fourth module 904 is used to screen and obtain natural gas hydrate regions in the target region based on the intercept density reflection coefficient data.

[0075] In some embodiments, the apparatus may further include a fifth module configured to perform the following operations: Construct a function script based on the intercept attribute and the calculation logic corresponding to the density reflection coefficient principle; The target program is obtained based on function script integration.

[0076] In some embodiments, the apparatus may further include: The sixth module is used to normalize the intercept data and the density reflection coefficient data.

[0077] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0078] Another embodiment of the present invention provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor implements the above-described natural gas hydrate identification method when executing the computer program. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0079] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0080] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is shown. The electronic device 1000 includes: The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention. The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention. Input / output interface 1003, used to implement information input and output; Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.); Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 ); The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0081] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0082] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0083] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0084] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0085] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0088] It should be noted that although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0089] Through the above description of the embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored on a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes instructions for causing a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present invention.

[0090] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented by the present invention. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0091] Furthermore, while the present invention has been described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed in the present invention, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0092] If a function 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 the present invention, or the portion that contributes to the prior art, or a portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0093] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus (e.g., a computer-based apparatus, a processor-included apparatus, or other apparatus that can fetch and execute instructions from, an instruction execution apparatus, device, or apparatus). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus.

[0094] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0095] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0096] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0098] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for identifying natural gas hydrates, characterized in that: The following steps are involved: Acquire seismic data of the target area; Importing the seismic data into the target program, obtaining intercept data according to intercept attribute calculation, and obtaining density reflection coefficient data according to density reflection coefficient principle calculation; Obtaining intercept density reflection coefficient data according to a ratio of the intercept data to the density reflection coefficient data; A natural gas hydrate region is screened in the target region based on the intercept density reflection coefficient data.

2. The natural gas hydrate identification method according to claim 1, characterized in that: The method further comprises the following steps: Construct a function script according to the intercept attribute and the operation logic corresponding to the density reflection coefficient principle; The target program is obtained based on the function script integration.

3. The natural gas hydrate identification method according to claim 1, characterized in that: Obtaining intercept data according to the intercept attribute calculation includes the following steps: Obtaining velocity data and density data of the target area based on the seismic data analysis; The velocity data includes the velocity difference and the average velocity of the strata above and below the interface in the target area, and the density data includes the density difference and the average density of the strata above and below the interface in the target area; Obtaining the intercept data by using a preset intercept expression according to the velocity data and the density data; Wherein, the intercept expression is: Where, K represents intercept data; Indicates the speed difference; Indicates the average speed; Indicates the density difference; represents the average density.

4. The natural gas hydrate identification method according to claim 1, characterized in that: The method of obtaining density reflection coefficient data by calculating the density reflection coefficient principle includes the following steps: Separating the effective signal term and the noise term of the seismic data based on the target equation, and then calculating the density reflection coefficient data; The target equation is derived based on the Zoplitz equation by introducing noise factors and singular value decomposition; the expression of the target equation is: Where, R Represents the reflection coefficient matrix, which includes the longitudinal wave reflection coefficient, the shear wave reflection coefficient, and the density reflection coefficient. The density reflection coefficient data is obtained based on the calculation result of the density reflection coefficient; B represents the coefficient matrix; T represents the transpose symbol; R pp Represents the reflection coefficient corresponding to different incident angles; N represents the noise factor; Q 、 D 、 P is the decomposition matrix of the coefficient matrix.

5. The natural gas hydrate identification method according to claim 4, characterized in that: The target equation is derived based on the Zoplitz equation by introducing a noise factor and performing singular value decomposition, including the following steps: The noise factor is introduced into the simplified formula of the Zoplitz equation to obtain the first equation; The expression of the first equation is: ; Solving the first equation by singular value decomposition to obtain the decomposition matrix of the coefficient matrix as the second equation; Wherein, the expression of the second equation is: B=PDQ Where, P and Q are orthogonal matrices of different dimensions, D is a diagonal matrix; Based on the second equation, the generalized inverse matrix of the coefficient matrix is solved according to the singular value decomposition method, and then substituted into the second equation to reversely obtain the target equation.

6. The natural gas hydrate identification method according to claim 1, characterized in that: Before the step of obtaining the intercept density reflection coefficient data according to the ratio of the intercept data to the density reflection coefficient data, the method further comprises the following steps: The intercept data and the density reflection coefficient data are normalized.

7. The natural gas hydrate identification method according to claim 1, characterized in that: The method of screening the target area to obtain the natural gas hydrate area based on the intercept density reflection coefficient data comprises the following steps: Comparing the intercept density reflection coefficient data of each area in the target area with a preset threshold value, and determining that the corresponding area is the natural gas hydrate area when the intercept density reflection coefficient data is greater than the preset threshold value; The intercept density reflection coefficient data of each area in the target area is obtained based on the ratio of the intercept data to the density reflection coefficient data of the corresponding area.

8. A natural gas hydrate identification device, characterized in that: include: The first module is used to obtain seismic data of the target area; The second module is used to import the seismic data into the target program, obtain intercept data according to intercept attribute calculation, and obtain density reflection coefficient data according to density reflection coefficient principle calculation; A third module is configured to obtain intercept density reflection coefficient data according to a ratio of the intercept data to the density reflection coefficient data; The fourth module is used to screen and obtain a natural gas hydrate area in the target area based on the intercept density reflection coefficient data.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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