Logging data generation method and device, equipment and storage medium

Abstract

The invention provides a logging data generation method and device, equipment and a storage medium, and belongs to the technical field of oil-gas exploration. The method comprises the steps that based on actual logging data of any well, a porosity curve and a shale curve corresponding to all rock stratums related to the well are determined; for any rock stratum, based on the porosity curve and the shale curve corresponding to the rock stratum, determining a first experiment variation function and a second experiment variation function corresponding to the rock stratum; based on the first experimental variation function and the second experimental variation function, determining a first variation function model and a second variation function model; and based on the first experiment variation model and the second experiment variation model, generating logging data corresponding to the rock stratum. According to the technical scheme, the database of the logging data can be expanded.

Description

Technical Field

[0001] The present application relates to the field of petroleum exploration technology, and in particular to a method, device, equipment and storage medium for generating well logging data. Background Art

[0002] Well logging data is the basis of well logging interpretation and reservoir prediction. At the same time, high-quality well logging data is a valuable data source in machine learning, but in many practical engineering problems, the collection of well logging data is very expensive and time-consuming. For deep learning, the total amount of well logging data is even more important, because the number of parameters in deep learning models is larger, often up to tens of thousands of parameters, and the demand for training data is huge. However, due to factors such as economic cost, exploration degree, and technical conditions, the number of wells drilled in actual project applications is limited, and it is difficult to obtain a large number of training samples for machine learning. For example, the database of the Bakken Shale in North Dakota, the United States, contains a total of 28,920 wells, but there are only 39 wells with complete acoustic logging data, accounting for 0.13% of the total number of wells. Insufficient actual logging data leads to the prediction accuracy of machine learning usually failing to meet the demand, or the generalization of the training model has serious problems. In order to ensure the model training and application effect of machine learning (deep learning), generating pseudo logging data has become the focus of research in this field. Summary of the invention

[0003] The embodiment of the present application provides a method, device, equipment and storage medium for generating well logging data, which can expand the database of well logging data. The technical solution is as follows:

[0004] In one aspect, a method for generating well logging data is provided, the method comprising:

[0005] Based on the actual logging data of any well, determine the porosity curve and shale curve corresponding to each rock layer involved in the well, the porosity curve is used to represent the distribution of porosity in the corresponding rock layer, and the shale curve is used to represent the shale content in the corresponding rock layer;

[0006] For any rock formation, based on the porosity curve and the shale curve corresponding to the rock formation, determine the first experimental variogram and the second experimental variogram corresponding to the rock formation, wherein the first experimental variogram is used to represent the change of porosity in the rock formation with depth, and the second experimental variogram is used to represent the change of shale content in the rock formation with depth;

[0007] Determining a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram, wherein the first variogram model is used to represent the spatial distribution of porosity in the rock formation, and the second variogram model is used to represent the spatial distribution of mud content in the rock formation;

[0008] Based on the first experimental variation model and the second experimental variation model, well logging data corresponding to the rock formation is generated.

[0009] In some embodiments, determining the porosity curve and the shale curve corresponding to each rock formation involved in any well based on actual well logging data of the well includes:

[0010] Preprocess the actual logging data of any well;

[0011] In the case where the actual logging data includes pore data and shale data, generating a porosity curve and a shale curve corresponding to each rock formation based on the preprocessed pore data and the shale data;

[0012] In the case that the actual well logging data does not include porosity data and shale data, well logging interpretation is performed on the pre-processed actual well logging data to obtain porosity curves and shale curves corresponding to each rock formation.

[0013] In some embodiments, for any rock formation, determining a first experimental variogram and a second experimental variogram corresponding to the rock formation based on a porosity curve and a shale curve corresponding to the rock formation includes:

[0014] For any curve of the porosity curve and the shale curve corresponding to any rock formation, based on the curve, determine the covariance matrix of the curve, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the rock formation;

[0015] determining the first experimental variogram based on a covariance matrix of the porosity curve and the porosity curve;

[0016] The second experimental variogram is determined based on the covariance matrix of the mud curve and the mud curve.

[0017] In some embodiments, determining a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram includes:

[0018] Selecting a theoretical variogram model based on actual well logging data corresponding to the rock formation;

[0019] Fitting the theoretical variogram model to the first experimental variogram to obtain the first variogram model;

[0020] The theoretical variogram model is fitted to the second experimental variogram to obtain the second variogram model.

[0021] In some embodiments, fitting the theoretical variogram model with the first experimental variogram to obtain the first variogram model includes:

[0022] Using the least square method, the theoretical variogram model is fitted with the first experimental variogram to obtain the range and base value of the variogram model corresponding to the rock formation;

[0023] Based on the variation range and the base value, corresponding parameters in the theoretical variogram model are updated to obtain the first variogram model.

[0024] In some embodiments, generating the well logging data corresponding to the rock formation based on the first experimental variation model and the second experimental variation model includes:

[0025] For any one of the first experimental variation model and the second experimental variation model, based on the model, determining a covariance function of the model;

[0026] Determine a correlation coefficient matrix based on the covariance function of the model and the covariance matrix of the corresponding curve of the model, wherein the covariance function of the model is used to represent the distribution difference of the corresponding data in the simulated rock formation, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the real rock formation, and the relationship coefficient matrix is ​​used to represent the correlation between the simulated well logging data and the real actual well logging data;

[0027] Based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the shale, the well logging data corresponding to the rock formation is generated.

[0028] In some embodiments, generating the logging data corresponding to the rock formation based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the shale includes:

[0029] For any correlation coefficient matrix among the correlation coefficient matrix corresponding to porosity and the correlation coefficient matrix corresponding to shale, the correlation coefficient matrix is ​​decomposed by using the square root method to obtain a lower triangular matrix;

[0030] Through Gaussian simulation, a plurality of random error matrices are obtained, and the plurality of random error matrices all conform to Gaussian distribution;

[0031] For any random error matrix, multiply the lower triangular matrix by the random error matrix to obtain an error matrix, wherein multiple values ​​in the error matrix are used to represent errors at various positions on the corresponding curve;

[0032] A plurality of values ​​in the error matrix are added to the values ​​on the corresponding curve to obtain a target curve, and the target curve is used to represent the simulated logging data.

[0033] In some embodiments, the method further comprises:

[0034] For any rock formation, a rock physics model is selected based on actual well logging data corresponding to the rock formation;

[0035] Inverting the rock physics model to obtain a target model corresponding to the rock formation;

[0036] Based on the target model, the target curve corresponding to the porosity and the target curve corresponding to the shale, rock physical simulation is performed to obtain at least one elastic parameter corresponding to the rock formation, and the at least one elastic parameter belongs to the simulated logging data.

[0037] In another aspect, a device for generating well logging data is provided, the device comprising:

[0038] A first determination module is used to determine the porosity curve and the shale curve corresponding to each rock layer involved in any well based on the actual well logging data of the well, wherein the porosity curve is used to represent the distribution of porosity in the corresponding rock layer, and the shale curve is used to represent the shale content in the corresponding rock layer;

[0039] A second determination module is used to determine, for any rock formation, a first experimental variogram and a second experimental variogram corresponding to the rock formation based on a porosity curve and a shale curve corresponding to the rock formation, wherein the first experimental variogram is used to represent the variation of porosity in the rock formation with depth, and the second experimental variogram is used to represent the variation of shale content in the rock formation with depth;

[0040] a third determination module, configured to determine a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram, wherein the first variogram model is used to represent the spatial distribution of porosity in the rock formation, and the second variogram model is used to represent the spatial distribution of mud content in the rock formation;

[0041] A generating module is used to generate well logging data corresponding to the rock formation based on the first experimental variation model and the second experimental variation model.

[0042] In some embodiments, the first determination module is used to preprocess the actual logging data of any well; when the actual logging data includes pore data and mud data, the porosity curve and mud curve corresponding to each rock formation are generated based on the preprocessed pore data and mud data; when the actual logging data does not include pore data and mud data, the preprocessed actual logging data is interpreted to obtain the porosity curve and mud curve corresponding to each rock formation.

[0043] In some embodiments, the second determination module is used to determine the covariance matrix of any curve among the porosity curve and the mud curve corresponding to any rock formation based on the curve, and the covariance matrix is ​​used to represent the distribution difference of corresponding data in the rock formation; determine the first experimental variation function based on the covariance matrix of the porosity curve and the porosity curve; determine the second experimental variation function based on the covariance matrix of the mud curve and the mud curve.

[0044] In some embodiments, the third determination module is used to select a theoretical variogram model based on actual logging data corresponding to the rock formation; fit the theoretical variogram model with the first experimental variogram to obtain the first variogram model; fit the theoretical variogram model with the second experimental variogram to obtain the second variogram model.

[0045] In some embodiments, the third determination module is used to fit the theoretical variogram model with the first experimental variogram using the least squares method to obtain the range and base value of the variogram model corresponding to the rock formation; based on the range and the base value, the corresponding parameters in the theoretical variogram model are updated to obtain the first variogram model.

[0046] In some embodiments, the generating module comprises:

[0047] A first determining unit, configured to determine, for any one of the first experimental variation model and the second experimental variation model, a covariance function of the model based on the model;

[0048] A second determination unit is used to determine a correlation coefficient matrix based on a covariance function of the model and a covariance matrix of a curve corresponding to the model, wherein the covariance function of the model is used to represent the distribution difference of the corresponding data in the simulated rock formation, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the real rock formation, and the relationship coefficient matrix is ​​used to represent the correlation between the simulated well logging data and the real actual well logging data;

[0049] The generating unit is used to generate the well logging data corresponding to the rock formation based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the shale.

[0050] In some embodiments, the generating unit is used to decompose any correlation coefficient matrix in the correlation coefficient matrix corresponding to porosity and the correlation coefficient matrix corresponding to mud by the square root method to obtain a lower triangular matrix; obtain multiple random error matrices through Gaussian simulation, and the multiple random error matrices all conform to Gaussian distribution; for any random error matrix, multiply the lower triangular matrix by the random error matrix to obtain an error matrix, and the multiple values ​​in the error matrix are used to represent the errors at each position on the corresponding curve; add the multiple values ​​in the error matrix to the values ​​on the corresponding curve to obtain a target curve, and the target curve is used to represent the simulated logging data.

[0051] In some embodiments, the apparatus further comprises:

[0052] A processing module is used to select a rock physics model for any rock formation based on actual logging data corresponding to the rock formation; invert the rock physics model to obtain a target model corresponding to the rock formation; perform rock physics simulation based on the target model, a target curve corresponding to porosity, and a target curve corresponding to mud to obtain at least one elastic parameter corresponding to the rock formation, wherein the at least one elastic parameter belongs to the simulated logging data.

[0053] On the other hand, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the method for generating logging data in an embodiment of the present application.

[0054] On the other hand, a computer-readable storage medium is provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor to implement the method for generating logging data in the embodiment of the present application.

[0055] On the other hand, a computer program is provided, including a computer program, which, when executed by a processor, implements the method for generating well logging data as described in the embodiments of the present application.

[0056] The embodiment of the present application provides a method for generating logging data, which uses known actual logging data to determine the porosity curve and mud curve corresponding to each rock layer (reservoir), so that the porosity and mud content and other properties of each rock layer can be obtained through logging interpretation. Then, by calculating the experimental variograms corresponding to the porosity and mud content, the corresponding variogram model is determined, so that the spatial distribution of porosity and mud content in the rock layer can be analyzed. That is, by analyzing the probability distribution of properties such as porosity and mud content, the spatial variability of the logging data corresponding to the rock layer is determined, so that more realistic logging data can be simulated, which not only enriches the logging data, but also ensures the quality of the logging data, which is conducive to the subsequent training of more accurate deep learning models through the generated logging data. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 is a flow chart of a method for generating well logging data provided according to an embodiment of the present application;

[0059] Figure 2 is a target curve corresponding to a mud content provided in an embodiment of the present application;

[0060] Figure 3 is a target curve corresponding to a porosity provided in an embodiment of the present application;

[0061] Figure 4 is a rock physics simulation result provided according to an embodiment of the present application;

[0062] Figure 5 is a flow chart of another method for generating well logging data provided in an embodiment of the present application;

[0063] Figure 6 It is a structural schematic diagram of a device for generating well logging data provided according to an embodiment of the present application;

[0064] Figure 7 is a schematic structural diagram of another device for generating well logging data provided in an embodiment of the present application;

[0065] Figure 8 is a structural block diagram of a terminal provided according to an embodiment of the present application;

[0066] Fig. 9It is a structural block diagram of a server provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0067] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0068] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with basically the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on quantity and execution order.

[0069] In the present application, the term "at least one" means one or more, and the term "plurality" means two or more.

[0070] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. For example, the actual logging data involved in this application was obtained with full authorization.

[0071] Figure 1 is a flow chart of a method for generating well logging data according to an embodiment of the present application, see Figure 1 In the present application embodiment, the method for generating logging data is described by taking the execution by a terminal as an example. The method for generating logging data comprises the following steps:

[0072] 101. Based on the actual logging data of any well, the terminal determines the porosity curve and shale curve corresponding to each rock layer involved in the well. The porosity curve is used to represent the distribution of porosity in the corresponding rock layer, and the shale curve is used to represent the shale content in the corresponding rock layer.

[0073] In an embodiment of the present application, the terminal can collect and organize the actual logging data of any well in the study area. The actual logging data may include various types of data such as gamma, neutron, density, porosity, water saturation and longitudinal wave velocity, which are not limited in this embodiment of the present application. The terminal can also obtain data such as regional empirical relationships, geological parameters, core test conclusions, etc. corresponding to the well, which are not limited in this embodiment of the present application. For any well, the terminal can determine the porosity curve and mud curve corresponding to each rock formation involved in the well based on the actual logging data of the well. Different rock formations have different lithologies, and the corresponding porosity curves and mud curves are also different.

[0074] In some embodiments, the process of the terminal determining the porosity curve and shale curve corresponding to each rock formation involved in the well includes: the terminal preprocesses the actual logging data of any well. Then, in the case where the actual logging data includes pore data and shale data, the terminal generates the porosity curve and shale curve corresponding to each rock formation based on the preprocessed pore data and shale data. In the case where the actual logging data does not include pore data and shale data, the preprocessed actual logging data is subjected to logging interpretation to obtain the porosity curve and shale curve corresponding to each rock formation. Specifically, the terminal can preprocess the measured logging curves in the actual logging data. The preprocessing process includes operations such as environmental correction, anomaly detection, missing interpolation, and multi-well standardization. The embodiment of the present application does not limit the preprocessing process. This method can ensure the correctness and effectiveness of the measured data.

[0075] In some embodiments, for any rock formation, if any of the porosity curve and the shale curve corresponding to the rock formation does not conform to the normal distribution, the terminal can perform a normal transformation on the curve. Optionally, the terminal can perform a Yeo-Johnson normal transformation on the curve to make the curve conform to the normal distribution. The specific method of the normal transformation is not limited in the embodiment of the present application.

[0076] The terminal can use the following formula 1 to perform normal distribution.

[0077] Formula 1:

[0078]

[0079] Among them, Y is used to represent the data (curve) after normalization transformation; X is used to represent the original data (original curve); γ is used to represent the transformation coefficient, which can be determined by maximum likelihood estimation.

[0080] In some embodiments, the terminal can splice the porosity curves of each rock layer according to the depth corresponding to each rock layer to obtain the porosity curve of the well. The terminal splices the shale curves of each rock layer according to the corresponding depth of each rock layer to obtain the shale curve Vsh of the well. norm .

[0081] In some embodiments, the terminal can perform a sliding average filter on at least one of the porosity curve and the shale curve of the well. The filtered curve is smoother and can reflect the distribution of the porosity or shale content corresponding to the curve with depth. The filtered curve can be regarded as the trend of the porosity or shale content with depth, which can also be called the background trend. Accordingly, the terminal can perform a sliding average filter on the porosity curve of the well to obtain the background trend corresponding to the porosity. The terminal can perform sliding average filtering on the well's mud curve to obtain the background trend Vsh corresponding to the mud content. normtrend .

[0082] 102. For any rock formation, the terminal determines the first experimental variogram and the second experimental variogram corresponding to the rock formation based on the porosity curve and mud curve corresponding to the rock formation. The first experimental variogram is used to represent the change of porosity in the rock formation with depth, and the second experimental variogram is used to represent the change of mud content in the rock formation with depth.

[0083] In an embodiment of the present application, for any curve in the porosity curve and the shale curve corresponding to any rock formation, the terminal determines the covariance matrix cov of the curve based on the curve. The covariance matrix is ​​used to represent the distribution difference of the corresponding data in the rock formation. The terminal determines the first experimental variogram based on the covariance matrix of the porosity curve and the porosity curve. The terminal determines the second experimental variogram based on the covariance matrix of the shale curve and the shale curve. The above-mentioned experimental variogram can be called a vertical experimental variogram.

[0084] The terminal may use the following formula 2 to calculate any of the above experimental variation functions.

[0085] Formula 2:

[0086] γ(h)=AVG[(dn(x)-dn(x+h)) 2 ]=AVG[(d(h)) 2 ]

[0087] Among them, γ(h) is used to represent the experimental variation function; AVG[·] is used to represent the sum of squares of the differences of all data pairs at two locations with a distance of h; dn(x) is used to represent the actual logging data at a depth of x; and dn(x+h) is used to represent the actual logging data at a depth of x+h.

[0088] If the well logging data to be simulated is greater than 1 and is correlated with each other, then d(h) is equal to the Euclidean distance in the multidimensional space. See the following formula 3.

[0089] Formula 3:

[0090]

[0091] Among them, dn j It is used to represent the j-th type of data in the actual logging data at depth x, expressed in the form of a vector; dn j (x+h) is used to represent the j-th type of data in the actual logging data at the depth of x+h; P is used to represent the number of data in the actual logging data, that is, the number of vectors.

[0092] Correspondingly, through the above formula 3, the terminal can transform the above formula 2. Please refer to the following formula 4.

[0093] Formula 4:

[0094] γ(h)=AVG[(DN(x)-DN(x+h))M(DN(x)-DN(x+h)) T ]

[0095] where γ(h) is used to represent the experimental variation function; AVG[·] is used to represent the sum of squares of the differences of all data pairs at two locations with a distance of h; DN(x) = [dn1(x), dn2(x), …, dn p (x)] is used to represent the actual logging data at a depth of x; DN(x+h) is used to represent the actual logging data at a depth of x+h; M is used to represent the inverse matrix of the covariance matrix cov; and T is used to represent the transpose of the matrix.

[0096] 103. The terminal determines a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram, wherein the first variogram model is used to represent the spatial distribution of porosity in the rock formation, and the second variogram model is used to represent the spatial distribution of mud content in the rock formation.

[0097] In an embodiment of the present application, the terminal can select a theoretical variogram model based on actual logging data corresponding to the rock formation. Then, the terminal fits the theoretical variogram model with the first experimental variogram to obtain a first variogram model. The terminal fits the theoretical variogram model with the second experimental variogram to obtain a second variogram model. The embodiment of the present application does not limit the selection of the theoretical variogram model.

[0098] Optionally, the theoretical variogram model may be a spherical variogram model. The terminal may express the spherical variogram model using the following formula 5.

[0099] Formula 5:

[0100]

[0101] Among them, γ(h)′ is used to represent the theoretical variogram model, which can be determined based on regional empirical relationships and geological parameters; a is used to represent the range, which refers to the range of spatial correlation of regional variables (porosity or shale content). Within the range, the data are correlated, while outside the range, the data are uncorrelated, that is, the observed values ​​outside the range do not affect the estimation results; C0 is used to represent the nugget value; C is used to represent the arch height, that is, the magnitude of the variability that can be observed on the effective data scale; C ′=C0+C is used to represent the base value; h is used to represent the distance.

[0102] In some embodiments, the terminal fits the theoretical variogram model with the first experimental variogram, and the process of obtaining the first variogram model includes: the terminal uses the least squares method to fit the theoretical variogram model with the first experimental variogram to obtain the range and sill value of the variogram model corresponding to the rock formation. Then, the terminal updates the corresponding parameters in the theoretical variogram model based on the range and sill value to obtain the first variogram model. That is, the terminal brings the range and sill value into the above formula five to obtain the first variogram model. The principle of calculating the second variogram model in the embodiment of the present application is similar to the principle of calculating the first variogram model, which will not be repeated here.

[0103] 104. The terminal generates logging data corresponding to the rock formation based on the first experimental variation model and the second experimental variation model.

[0104] In an embodiment of the present application, for any model in the first experimental variation model and the second experimental variation model, the terminal can determine the covariance function of the model based on the model. Then, the terminal determines the correlation coefficient matrix based on the covariance function of the model and the covariance matrix of the corresponding curve of the model. The covariance function of the model is used to represent the distribution difference of the corresponding data in the simulated rock formation, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the real rock formation, and the relationship number matrix is ​​used to represent the correlation between the simulated logging data and the real actual logging data. Then, the terminal generates the logging data corresponding to the rock formation based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the mud.

[0105] The terminal can calculate the covariance function corresponding to the rock formation by using the following formula 6.

[0106] Formula 6:

[0107] C(h)=C′-γ(h)

[0108] Among them, C(h) is used to represent the covariance function corresponding to the rock layer; C′ is used to represent the base value corresponding to the porosity (or mud content); γ(h) is used to represent the variogram model corresponding to the porosity (or mud content).

[0109] Then, the terminal uses formula 6 to obtain the covariance function C(h) and the covariance matrix cov obtained in step 102 to generate the correlation coefficient matrix R e .

[0110] In some embodiments, the process of generating logging data corresponding to the rock formation based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the mud quality by the terminal includes: for any correlation coefficient matrix in the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the mud quality, the terminal can use the square root method to decompose the correlation coefficient matrix to obtain a lower triangular matrix. Then, the terminal obtains multiple random error matrices through Gaussian simulation, and the multiple random error matrices all conform to the Gaussian distribution. Then, for any random error matrix, the terminal multiplies the lower triangular matrix with the random error matrix to obtain an error matrix. Multiple values ​​in the error matrix are used to represent the errors at each position on the corresponding curve. Then, the terminal adds multiple values ​​in the error matrix to the values ​​on the corresponding curve to obtain a target curve. The target curve is used to represent the simulated logging data.

[0111] Specifically, the terminal can convert the correlation coefficient matrix R e Perform Cholesky decomposition to obtain the lower triangular L matrix. Then, the terminal can obtain multiple sets of random error matrices R that conform to the Gaussian distribution through Gaussian simulation. The random error matrix R can be a two-dimensional array with 2 columns, which is not limited in the embodiment of the present application. Then, the terminal multiplies the vector of the Gaussian random error R by the L matrix to obtain the error matrix E of the curve corresponding to each position. Then, the terminal can compare the error matrix E with the trend of the measured logging curve. With Vsh normtrend By adding them up respectively, multiple sets of simulated porosity and mud content are obtained.

[0112] The terminal can also perform a normal inverse transformation on the target curve corresponding to the generated porosity and mud content to obtain multiple sets of original-scale mud content and porosity under the rock layer (single lithology).

[0113] The terminal may repeatedly execute step 102 and step 104 until all the logging data corresponding to all the rock formations involved in the well are simulated.

[0114] In some embodiments, for any rock formation, the terminal can also select a rock physics model based on the actual logging data corresponding to the rock formation. Then, the terminal inverts the rock physics model to obtain a target model corresponding to the rock formation. Then, the terminal performs rock physics simulation based on the target model, the target curve corresponding to the porosity, and the target curve corresponding to the mud to obtain at least one elastic parameter corresponding to the rock formation. At least one elastic parameter belongs to the simulated logging data. At least one elastic parameter may include parameters such as P-wave velocity and density. The embodiments of the present application are not limited to this.

[0115] Specifically, the terminal can combine the generated target curves into separate shale content and porosity according to the rock formation, thereby obtaining multiple sets of simulated porosity and shale content. Figure 2 and Figure 3 . Figure 2 It is a target curve corresponding to mud content provided according to an embodiment of the present application. Figure 2 Contains 5 sets of simulated mud contents. Figure 3 It is a target curve corresponding to a porosity provided according to an embodiment of the present application. Figure 3 There are 5 sets of simulated porosity. Then, the terminal performs rock physics simulation based on the rock physics model corresponding to the rock formation and the simulated curves to generate multiple sets of P-wave and S-wave velocities and densities. For details, see Figure 4 . Figure 4 It is a rock physics simulation result provided according to an embodiment of the present application. Figure 4 The sample simulation results of shale content, porosity, P-wave and S-wave velocity and density are shown under the condition of constant lithology and saturation. Comparing the prediction results, it can be seen that the overall trend of the simulated multiple sets of data is consistent with the lithology, but the details are different, which can be considered as an effective expansion of the current logging database.

[0116] In order to more clearly describe the method for generating well logging data provided in the embodiment of the present application, it is further described below with reference to the accompanying drawings. Figure 5 is a flow chart of another method for generating well logging data according to an embodiment of the present application. Figure 5 , the terminal first collects and organizes the actual logging curves, geological parameters, core test conclusions and other data of any well in the study area. Then, the terminal can preprocess the above data. Then, the terminal performs logging interpretation based on the preprocessed data. Then, the terminal can perform normal transformation on the actual logging data of different rock formations. Then, the terminal can process the actual logging data of different rock formations through sliding average filtering to obtain the background trend of the actual logging data of different rock formations. Then, the terminal can merge the background trends of the actual logging data of different rock formations according to the position of the rock formation. For any rock formation, the terminal can calculate the covariance matrix cov of the curve to be simulated (porosity curve and shale curve) of the rock formation. Then, the terminal calculates the experimental variogram of the curve to be simulated of the rock formation. Then, the terminal determines the variogram model of the curve to be simulated of the rock formation. Then, the terminal generates corresponding logging data (samples) based on the variogram model. Then, the terminal can merge the logging data of each rock formation. Then, the terminal can perform inverse normal transformation on the logging data of each rock formation. In addition, the terminal can also select a rock physics model based on actual logging data corresponding to the rock formation. Then, the terminal inverts the rock physics model to obtain a target model corresponding to the rock formation. Then, the terminal performs rock physics simulation based on the target model, the target curve corresponding to the porosity, and the target curve corresponding to the shale to obtain at least one elastic parameter corresponding to the rock formation.

[0117] The embodiment of the present application provides a method for generating logging data, using known actual logging data to determine the porosity curve and shale curve corresponding to each rock layer (reservoir), so that the porosity and shale content and other properties of each rock layer can be obtained through logging interpretation, and then, by calculating the experimental variogram corresponding to the porosity and shale content, the corresponding variogram model is determined, so that the spatial distribution of porosity and shale content in the rock layer can be analyzed, that is, by analyzing the probability distribution of properties such as porosity and shale content, the spatial variability of the logging data corresponding to the rock layer is determined, so that more realistic logging data can be simulated, which not only enriches the logging data, but also ensures the quality of the logging data, which is conducive to the subsequent training of more accurate deep learning models through the generated logging data; in addition, the rock physics model corresponding to the rock layer is inverted by the known elastic parameters, and the reservoir parameters are converted into elastic parameters such as longitudinal and transverse wave velocities that conform to the rock physics law using the model corresponding to the rock layer. Through this technology, reservoir parameters and elastic parameter curves under different geological conditions can be obtained, enriching the logging and rock physics labels of machine learning.

[0118] Figure 6 Schematic diagram of a device for generating logging data according to an embodiment of the present application. The device is used to execute the steps of the above-mentioned method for generating logging data. Figure 6 The device for generating logging data includes: a first determination module 601 , a second determination module 602 , a third determination module 603 and a generation module 604 .

[0119] The first determination module 601 is used to determine the porosity curve and shale curve corresponding to each rock layer involved in the well based on the actual well logging data of any well, the porosity curve is used to represent the distribution of porosity in the corresponding rock layer, and the shale curve is used to represent the shale content in the corresponding rock layer;

[0120] The second determination module 602 is used to determine, for any rock formation, a first experimental variogram and a second experimental variogram corresponding to the rock formation based on a porosity curve and a shale curve corresponding to the rock formation, wherein the first experimental variogram is used to represent the variation of porosity in the rock formation with depth, and the second experimental variogram is used to represent the variation of shale content in the rock formation with depth;

[0121] A third determination module 603 is used to determine a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram, the first variogram model is used to represent the spatial distribution of porosity in the rock formation, and the second variogram model is used to represent the spatial distribution of mud content in the rock formation;

[0122] The generation module 604 is used to generate well logging data corresponding to the rock formation based on the first experimental variation model and the second experimental variation model.

[0123] In some embodiments, Figure 7 is a schematic diagram of the structure of another device for generating well logging data according to an embodiment of the present application. Figure 7 The first determination module 601 is used to preprocess the actual logging data of any well; when the actual logging data includes pore data and mud data, the porosity curve and mud curve corresponding to each rock layer are generated based on the preprocessed pore data and mud data; when the actual logging data does not include pore data and mud data, the preprocessed actual logging data is interpreted to obtain the porosity curve and mud curve corresponding to each rock layer.

[0124] In some embodiments, see Figure 7 The second determination module 602 is used to determine the covariance matrix of any curve in the porosity curve and the mud curve corresponding to any rock formation based on the curve, and the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the rock formation; determine the first experimental variation function based on the covariance matrix of the porosity curve and the porosity curve; determine the second experimental variation function based on the covariance matrix of the mud curve and the mud curve.

[0125] In some embodiments, see Figure 7 The third determination module 603 is used to select a theoretical variogram model based on actual logging data corresponding to the rock formation; fit the theoretical variogram model with the first experimental variogram to obtain a first variogram model; and fit the theoretical variogram model with the second experimental variogram to obtain a second variogram model.

[0126] In some embodiments, see Figure 7 The third determination module 603 is used to fit the theoretical variogram model with the first experimental variogram using the least squares method to obtain the range and base value of the variogram model corresponding to the rock formation; based on the range and base value, the corresponding parameters in the theoretical variogram model are updated to obtain the first variogram model.

[0127] In some embodiments, see Figure 7 , generating module 604, including:

[0128] A first determining unit 6041 is used to determine the covariance function of any one of the first experimental variation model and the second experimental variation model based on the model;

[0129] The second determining unit 6042 is used to determine the correlation coefficient matrix based on the covariance function of the model and the covariance matrix of the corresponding curve of the model, the covariance function of the model is used to represent the distribution difference of the corresponding data in the simulated rock formation, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the real rock formation, and the relationship coefficient matrix is ​​used to represent the correlation between the simulated well logging data and the real actual well logging data;

[0130] The generating unit 6043 is used to generate the well logging data corresponding to the rock formation based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the shale.

[0131] In some embodiments, the generating unit 6043 is used to decompose the correlation coefficient matrix corresponding to porosity and the correlation coefficient matrix corresponding to mud by using the square root method to obtain a lower triangular matrix; obtain multiple random error matrices through Gaussian simulation, and the multiple random error matrices all conform to the Gaussian distribution; for any random error matrix, multiply the lower triangular matrix by the random error matrix to obtain an error matrix, and multiple values ​​in the error matrix are used to represent the errors at each position on the corresponding curve; add multiple values ​​in the error matrix to the values ​​on the corresponding curve to obtain a target curve, and the target curve is used to represent the simulated logging data.

[0132] In some embodiments, see Figure 7 , the device further comprises:

[0133] Processing module 605 is used to select a rock physics model for any rock formation based on actual logging data corresponding to the rock formation; invert the rock physics model to obtain a target model corresponding to the rock formation; perform rock physics simulation based on the target model, a target curve corresponding to the porosity, and a target curve corresponding to the mud to obtain at least one elastic parameter corresponding to the rock formation, and the at least one elastic parameter belongs to the simulated logging data.

[0134] The embodiment of the present application provides a device for generating logging data, which uses known actual logging data to determine the porosity curve and mud curve corresponding to each rock layer (reservoir), so that the porosity and mud content and other properties of each rock layer can be obtained through logging interpretation. Then, by calculating the experimental variograms corresponding to the porosity and mud content, the corresponding variogram model is determined, so that the spatial distribution of porosity and mud content in the rock layer can be analyzed, that is, by analyzing the probability distribution of properties such as porosity and mud content, the spatial variability of the logging data corresponding to the rock layer is determined, so that more realistic logging data can be simulated, which not only enriches the logging data, but also ensures the quality of the logging data, which is conducive to the subsequent training of more accurate deep learning models through the generated logging data.

[0135] It should be noted that the device for generating logging data provided in the above embodiment is only illustrated by the division of the above functional modules when running the application program. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device for generating logging data provided in the above embodiment and the method for generating logging data belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0136] In the embodiments of the present application, the computer device can be configured as a terminal or a server. When the computer device is configured as a terminal, the terminal can be used as the execution subject to implement the technical solution provided in the embodiments of the present application. When the computer device is configured as a server, the server can be used as the execution subject to implement the technical solution provided in the embodiments of the present application. The technical solution provided in the present application can also be implemented through interaction between the terminal and the server. The embodiments of the present application are not limited to this.

[0137] Figure 8 800 is a block diagram of a terminal 800 according to an embodiment of the present application. The terminal 800 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The terminal 800 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0138] Typically, the terminal 800 includes a processor 801 and a memory 802 .

[0139] The processor 801 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 801 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 801 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 801 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 801 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0140] The memory 802 may include one or more computer-readable storage media, which may be non-transitory. The memory 802 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 802 is used to store at least one computer program, which is used to be executed by the processor 801 to implement the method for generating logging data provided in the method embodiment of the present application.

[0141] In some embodiments, the terminal 800 may further optionally include: a peripheral device interface 803 and at least one peripheral device. The processor 801, the memory 802 and the peripheral device interface 803 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 803 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 804, a display screen 805, a camera assembly 806, an audio circuit 807 and a power supply 808.

[0142] The peripheral device interface 803 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 801 and the memory 802. In some embodiments, the processor 801, the memory 802, and the peripheral device interface 803 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 801, the memory 802, and the peripheral device interface 803 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0143] The radio frequency circuit 804 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 804 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 804 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. In some embodiments, the radio frequency circuit 804 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 804 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 804 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0144] The display screen 805 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 805 is a touch display screen, the display screen 805 also has the ability to collect touch signals on the surface or above the surface of the display screen 805. The touch signal can be input to the processor 801 as a control signal for processing. At this time, the display screen 805 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 805 can be one, set on the front panel of the terminal 800; in other embodiments, the display screen 805 can be at least two, respectively set on different surfaces of the terminal 800 or in a folding design; in other embodiments, the display screen 805 can be a flexible display screen, set on the curved surface or folding surface of the terminal 800. Even, the display screen 805 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 805 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0145] The camera assembly 806 is used to capture images or videos. In some embodiments, the camera assembly 806 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal, and the rear camera is disposed on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 806 may also include a flash. The flash may be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0146] The audio circuit 807 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 801 for processing, or input them into the radio frequency circuit 804 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 800. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 801 or the radio frequency circuit 804 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 807 may also include a headphone jack.

[0147] The power supply 808 is used to power various components in the terminal 800. The power supply 808 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 808 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0148] In some embodiments, the terminal 800 further includes one or more sensors 809 , including but not limited to: an acceleration sensor 810 , a gyroscope sensor 811 , a pressure sensor 812 , an optical sensor 813 , and a proximity sensor 814 .

[0149] The acceleration sensor 810 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 800. For example, the acceleration sensor 810 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 801 can control the display screen 805 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 810. The acceleration sensor 810 can also be used to collect game or user motion data.

[0150] The gyro sensor 811 can detect the body direction and rotation angle of the terminal 800, and the gyro sensor 811 can cooperate with the acceleration sensor 810 to collect the user's 3D actions on the terminal 800. The processor 801 can implement the following functions based on the data collected by the gyro sensor 811: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0151] The pressure sensor 812 can be set on the side frame of the terminal 800 and / or the lower layer of the display screen 805. When the pressure sensor 812 is set on the side frame of the terminal 800, it can detect the user's holding signal of the terminal 800, and the processor 801 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 812. When the pressure sensor 812 is set on the lower layer of the display screen 805, the processor 801 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 805. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0152] The optical sensor 813 is used to collect the ambient light intensity. In one embodiment, the processor 801 can control the display brightness of the display screen 805 according to the ambient light intensity collected by the optical sensor 813. Specifically, when the ambient light intensity is high, the display brightness of the display screen 805 is increased; when the ambient light intensity is low, the display brightness of the display screen 805 is reduced. In another embodiment, the processor 801 can also dynamically adjust the shooting parameters of the camera component 806 according to the ambient light intensity collected by the optical sensor 813.

[0153] The proximity sensor 814, also called a distance sensor, is usually disposed on the front panel of the terminal 800. The proximity sensor 814 is used to collect the distance between the user and the front of the terminal 800. In one embodiment, when the proximity sensor 814 detects that the distance between the user and the front of the terminal 800 is gradually decreasing, the processor 801 controls the display screen 805 to switch from the screen-on state to the screen-off state; when the proximity sensor 814 detects that the distance between the user and the front of the terminal 800 is gradually increasing, the processor 801 controls the display screen 805 to switch from the screen-off state to the screen-on state.

[0154] Those skilled in the art will understand that Figure 8 The structure shown in the figure does not constitute a limitation on the terminal 800, and the terminal 800 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0155] Fig. 9It is a structural block diagram of a server provided according to an embodiment of the present application. The server 900 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 901 and one or more memories 902, wherein the memory 902 stores at least one computer program, and the at least one computer program is loaded and executed by the processor 901 to implement the method for generating logging data provided by the above-mentioned various method embodiments. Of course, the server 900 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The server 900 may also include other components for realizing device functions, which will not be described in detail here.

[0156] The embodiment of the present application also provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is loaded and executed by a processor of a computer device to implement the operation performed by the computer device in the method for generating well logging data in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0157] The embodiment of the present application also provides a computer program product, including a computer program, which is stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the method for generating logging data provided in the above various optional implementations.

[0158] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0159] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for generating well logging data, characterized in that: The method comprises: Based on the actual logging data of any well, determine the porosity curve and shale curve corresponding to each rock layer involved in the well, the porosity curve is used to represent the distribution of porosity in the corresponding rock layer, and the shale curve is used to represent the shale content in the corresponding rock layer; For any rock formation, based on the porosity curve and the shale curve corresponding to the rock formation, determine the first experimental variogram and the second experimental variogram corresponding to the rock formation, wherein the first experimental variogram is used to represent the change of porosity in the rock formation with depth, and the second experimental variogram is used to represent the change of shale content in the rock formation with depth; Determining a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram, wherein the first variogram model is used to represent the spatial distribution of porosity in the rock formation, and the second variogram model is used to represent the spatial distribution of mud content in the rock formation; Based on the first experimental variation model and the second experimental variation model, well logging data corresponding to the rock formation is generated.

2. The method according to claim 1, characterized in that The method of determining the porosity curve and the shale curve corresponding to each rock formation involved in any well based on the actual well logging data of the well includes: Preprocess the actual logging data of any well; In the case where the actual logging data includes pore data and shale data, generating a porosity curve and a shale curve corresponding to each rock formation based on the preprocessed pore data and the shale data; In the case that the actual well logging data does not include porosity data and shale data, well logging interpretation is performed on the pre-processed actual well logging data to obtain porosity curves and shale curves corresponding to each rock formation.

3. The method according to claim 1, characterized in that For any rock formation, determining a first experimental variogram and a second experimental variogram corresponding to the rock formation based on a porosity curve and a shale curve corresponding to the rock formation comprises: For any curve of the porosity curve and the shale curve corresponding to any rock formation, based on the curve, determine the covariance matrix of the curve, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the rock formation; determining the first experimental variogram based on a covariance matrix of the porosity curve and the porosity curve; The second experimental variogram is determined based on the covariance matrix of the mud curve and the mud curve.

4. The method according to claim 1, characterized in that The determining of a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram includes: Selecting a theoretical variogram model based on actual well logging data corresponding to the rock formation; Fitting the theoretical variogram model to the first experimental variogram to obtain the first variogram model; The theoretical variogram model is fitted to the second experimental variogram to obtain the second variogram model.

5. The method according to claim 4, characterized in that The step of fitting the theoretical variogram model with the first experimental variogram to obtain the first variogram model includes: Using the least square method, the theoretical variogram model is fitted with the first experimental variogram to obtain the range and base value of the variogram model corresponding to the rock formation; Based on the variation range and the base value, corresponding parameters in the theoretical variogram model are updated to obtain the first variogram model.

6. The method according to claim 1, characterized in that The generating the well logging data corresponding to the rock formation based on the first experimental variation model and the second experimental variation model comprises: For any one of the first experimental variation model and the second experimental variation model, based on the model, determining a covariance function of the model; Determine a correlation coefficient matrix based on the covariance function of the model and the covariance matrix of the corresponding curve of the model, wherein the covariance function of the model is used to represent the distribution difference of the corresponding data in the simulated rock formation, the covariance matrix is ​​used to represent the distribution difference of the corresponding data in the real rock formation, and the relationship coefficient matrix is ​​used to represent the correlation between the simulated well logging data and the real actual well logging data; Based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the shale, the well logging data corresponding to the rock formation is generated.

7. The method according to claim 6, characterized in that The generating of the well logging data corresponding to the rock formation based on the correlation coefficient matrix corresponding to the porosity and the correlation coefficient matrix corresponding to the shale comprises: For any correlation coefficient matrix among the correlation coefficient matrix corresponding to porosity and the correlation coefficient matrix corresponding to shale, the correlation coefficient matrix is ​​decomposed by using the square root method to obtain a lower triangular matrix; Through Gaussian simulation, a plurality of random error matrices are obtained, and the plurality of random error matrices all conform to Gaussian distribution; For any random error matrix, multiply the lower triangular matrix by the random error matrix to obtain an error matrix, wherein multiple values ​​in the error matrix are used to represent errors at various positions on the corresponding curve; A plurality of values ​​in the error matrix are added to the values ​​on the corresponding curve to obtain a target curve, and the target curve is used to represent the simulated logging data.

8. The method according to claim 7, characterized in that The method further comprises: For any rock formation, a rock physics model is selected based on actual well logging data corresponding to the rock formation; Inverting the rock physics model to obtain a target model corresponding to the rock formation; Based on the target model, the target curve corresponding to the porosity and the target curve corresponding to the shale, rock physical simulation is performed to obtain at least one elastic parameter corresponding to the rock formation, and the at least one elastic parameter belongs to the simulated logging data.

9. A device for generating well logging data, characterized in that: The device comprises: A first determination module is used to determine the porosity curve and the shale curve corresponding to each rock layer involved in any well based on the actual well logging data of the well, wherein the porosity curve is used to represent the distribution of porosity in the corresponding rock layer, and the shale curve is used to represent the shale content in the corresponding rock layer; A second determination module is used to determine, for any rock formation, a first experimental variogram and a second experimental variogram corresponding to the rock formation based on a porosity curve and a shale curve corresponding to the rock formation, wherein the first experimental variogram is used to represent the variation of porosity in the rock formation with depth, and the second experimental variogram is used to represent the variation of shale content in the rock formation with depth; a third determination module, configured to determine a first variogram model and a second variogram model based on the first experimental variogram and the second experimental variogram, wherein the first variogram model is used to represent the spatial distribution of porosity in the rock formation, and the second variogram model is used to represent the spatial distribution of mud content in the rock formation; A generating module is used to generate well logging data corresponding to the rock formation based on the first experimental variation model and the second experimental variation model.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the method for generating well logging data according to any one of claims 1 to 8.

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