Oil and gas physical property parameter label synthesis method and device and prediction model training method

Abstract

The invention provides an oil and gas physical property parameter label synthesis method and device and a prediction model training method, and the method comprises the steps: importing oil and gas physical property parameters obtained through well logging of a target research region into an original physical property parameter distribution characteristic function, and obtaining a physical property parameter distribution characteristic function corresponding to the target research region; performing random sampling in the physical property parameter distribution characteristic function to obtain a plurality of random composite physical property parameters; performing combination according to the plurality of random composite physical parameters to obtain a physical parameter composite curve, and obtaining a reflection coefficient curve; convolution is carried out on the reflection coefficient curve and the statistical seismic wavelets to obtain a plurality of synthetic pre-stack seismic angle gathers; and determining a plurality of oil-gas physical property parameter labels according to the plurality of synthetic pre-stack seismic angle gathers and the random synthetic physical property parameters corresponding to each synthetic pre-stack seismic angle gather, and due to the fact that the physical property parameter distribution curve is used for selecting the oil-gas physical property parameters, the difference between the oil-gas physical property parameter labels and actual labels is small.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geophysical exploration, and in particular to an oil and gas physical property parameter label synthesis method, device and prediction model training method. Background Art

[0002] In recent years, the focus of oil and gas exploration has gradually shifted to the exploration of lithologic oil and gas reservoirs. Different from tectonic oil and gas reservoirs, these new reservoirs are affected by structural and reservoir heterogeneity, with complex reservoir conditions, difficult identification, difficult quantitative prediction, and high investment risks. In the field of oil and gas geophysical exploration, oil and gas physical parameters, such as porosity, oil and gas saturation, and permeability, can quantitatively describe the physical properties of underground rocks and are important parameters for the prediction of lithologic oil and gas reservoirs. Due to the complex reservoir conditions, there is a highly nonlinear relationship between the seismic response characteristics and the oil and gas physical parameters. Conventional techniques for predicting oil and gas physical parameters using seismic data have large errors and cannot meet the requirements of quantitative exploration. The emergence of artificial intelligence technology makes it possible to quantitatively predict the physical parameters of such complex oil and gas reservoirs.

[0003] In the field of oil and gas geophysical exploration, the intelligent geophysical exploration technology formed by combining artificial intelligence with existing conventional seismic data processing and interpretation technology can greatly improve the efficiency of seismic data processing and interpretation. Label data (including pre-stack seismic angle gathers and corresponding oil and gas physical property parameters) is the basis of artificial intelligence supervised learning network, and its quantity and quality directly determine the quality of prediction results. Compared with the fields of images and sounds, it is more difficult to obtain oil and gas physical property parameters in the field of oil and gas geophysical exploration, and pre-stack seismic angle gathers are relatively simple. Therefore, there is a lack of paired label data of pre-stack seismic angle gathers and oil and gas physical property parameters, which seriously restricts the development of intelligent prediction technology for oil and gas physical property parameters. Existing oil and gas physical property parameter labeling technology generally adopts label synthesis methods in the field of image recognition. Based on geological relationships, label synthesis can synthesize the value range of oil and gas physical parameters that are not observed in well logging but may actually exist. Then, within the value range, several oil and gas physical parameters are randomly selected, and the calculation formula is used to calculate the prestack seismic angle gathers corresponding to each oil and gas physical parameter, so as to obtain paired prestack seismic angle gathers and oil and gas physical parameters, expand the number of special parameter labels in the special parameter label set, and enrich the label information to a certain extent. However, because the random generation of oil and gas physical parameters is too theoretical, the synthesized labels are seriously distorted, which is quite different from the actual labels, and the accuracy of the trained prediction model is low. Therefore, it is particularly urgent to develop a set of practical and generalizable oil and gas physical parameter label synthesis solutions. Summary of the invention

[0004] In view of the above-mentioned problems in the prior art, the purpose of this specification is to provide a method and device for synthesizing oil and gas physical property parameter labels and a prediction model training method to solve the problems in the prior art that the synthesized labels are severely distorted, differ greatly from the actual labels, and the accuracy of the trained prediction models is low.

[0005] In order to solve the above technical problems, the specific technical solutions of this specification are as follows:

[0006] On the one hand, this specification provides a method for synthesizing oil and gas physical property parameter labels, comprising:

[0007] Importing a sample set of oil and gas physical property parameters obtained by logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area;

[0008] Random sampling is performed in the physical property parameter distribution characteristic function to obtain a physical property parameter synthetic sample set that meets the physical property parameter distribution characteristics of the study area;

[0009] According to the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, the samples in the physical property parameter synthesis sample set are randomly combined to obtain a physical property parameter synthesis curve, and a reflection coefficient curve is obtained according to the physical property parameter synthesis curve;

[0010] Convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers;

[0011] According to each synthetic pre-stack seismic angle gather and the corresponding physical parameter synthetic curve, the oil and gas physical parameter label data set is determined.

[0012] As an embodiment of the present specification, the sample set of oil and gas physical property parameters obtained by logging in the target study area is imported into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area, further comprising:

[0013] The statistical characteristics of the actual oil and gas physical property parameter sample set are obtained by statistics, including the total number of samples, the total number of lithofacies types, the prior probability of each lithofacies type, and the mean and variance of the physical property parameters belonging to each lithofacies;

[0014] The sample set of oil and gas physical property parameters obtained by logging in the target study area is imported into the following original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area;

[0015]

[0016] Wherein, x represents the value of oil and gas physical property parameter, M represents the total number of lithofacies types in the actual sample set of oil and gas physical property parameter, the total number of lithofacies types is obtained by comprehensive interpretation of well logging, and the total number of lithofacies types is dimensionless; k represents the kth lithofacies type, dimensionless; α k represents the prior probability of the k-th lithofacies type, which is calculated by counting the proportion of the number of samples belonging to the k-th lithofacies to the total number of samples; μ k represents the mean value of the oil and gas physical parameter samples belonging to the kth lithofacies type in the actual sample set of oil and gas physical parameter, λ k represents the variance of the oil and gas physical property parameter samples of the kth lithofacies type in the actual oil and gas physical property parameter sample set; π is the pi, N represents the total number of samples in the actual sample set of physical property parameters, and xi represents the i-th sample in the actual sample set of physical property parameters.

[0017] As an embodiment of the present specification, obtaining a reflection coefficient curve according to a synthetic curve of physical property parameters further includes:

[0018] Converting the physical property parameter composite curve into an elastic parameter composite curve according to a physical property elasticity conversion equation;

[0019] The elastic parameter synthesis curve is converted into a reflection coefficient curve according to the plane wave reflection equation.

[0020] As an embodiment of the present specification, converting the physical property parameter composite curve into the elastic parameter composite curve according to the physical property elasticity conversion equation further includes:

[0021] The physical property parameter synthesis curve is introduced into the following physical property elastic conversion equation to obtain the corresponding elastic parameter curve, wherein the conversion equation is determined based on the rock physics correlation between the elastic parameters and the physical property parameters under the influence of the two factors of lithofacies and pore structure;

[0022]

[0023] Where x represents the oil and gas physical property parameter value; m represents the elastic parameter value; γ j represents the pore structure parameters corresponding to the jth rock in the actual study area, obtained from the rock physics experiment in the study area; j (x,γ j ) represents the deterministic multi-porous structure rock physics model corresponding to the j-th rock; ε j It represents the error between the deterministic multi-porous structure rock physics model corresponding to the j-th rock and the actual observation data, j = 1, 2, ... M; L(j) is an M × 1 lithofacies constraint vector, in which all elements except the j-th value are 0.

[0024] As an embodiment of the present specification, after convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a plurality of synthetic pre-stack seismic angle gathers, the method further includes:

[0025] The frequency band information of some frequency bands in the synthetic pre-stack seismic angle gather is replaced with the frequency band data of the corresponding frequency bands of the measured pre-stack seismic angle gather to obtain the first synthetic pre-stack angle gather data;

[0026] The phase information of some phase segments in the synthetic pre-stack seismic angle gather is replaced with the phase data of the corresponding phase segments in the measured pre-stack seismic angle gather to obtain the second synthetic pre-stack angle gather data;

[0027] The first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data are synthesized to obtain a synthetic pre-stack seismic angle gather corresponding to the target study area;

[0028] The physical property parameter composite curve and the corresponding synthetic generalized pre-stack seismic angle gather constitute the oil and gas physical property parameter label data set.

[0029] As an embodiment of the present specification, the step of replacing the frequency band information of some frequency bands in the synthetic pre-stack seismic angle gather with the frequency band data of the corresponding frequency bands in the measured pre-stack seismic angle gather to obtain the first synthetic pre-stack angle gather data further includes:

[0030] According to the actual plane distribution of the observation system in the target study area, prestack angle gathers of multiple observation points are selected and spectral decomposition is performed to obtain frequency band data of three types of frequency bands of each observation point to form a spectrum pool;

[0031] Perform spectrum decomposition on synthetic pre-stack seismic angle gathers to obtain frequency band data of three frequency bands corresponding to each synthetic pre-stack seismic angle gather;

[0032] The frequency band data of the first frequency band and the frequency band data of the third frequency band of the spectrum data of each synthetic pre-stack angle gather are replaced with the frequency band data of the first frequency band and the frequency band data of the third frequency band randomly selected from the spectrum pool to obtain a plurality of first synthetic pre-stack angle gather data in frequency domains corresponding to each random synthetic physical property parameter curve;

[0033] Performing spectrum inverse transformation on the frequency domain data of the first synthetic pre-stack angle gather in the frequency domain to obtain the first synthetic pre-stack angle gather data.

[0034] As an embodiment of the present specification, the step of replacing the phase information of some phase segments in the synthetic pre-stack seismic angle gather with the phase data of the corresponding phase segments of the measured pre-stack seismic angle gather to obtain the second synthetic pre-stack angle gather data further includes:

[0035] According to the actual plane distribution of the observation system in the target study area, the prestack angle gathers of multiple observation points are selected and phase decomposition is performed to obtain the phase data of three types of phase segments of each observation point to form a phase pool;

[0036] Perform phase decomposition on synthetic pre-stack seismic angle gathers to obtain phase data of three types of phase segments corresponding to each synthetic pre-stack seismic angle gather;

[0037] The phase data of the first type of phase segment and the phase data of the third type of phase segment of each phase segment data of the synthetic prestack angle gather are replaced with the phase data of the first type of phase segment and the phase data of the third type of phase segment randomly selected from the phase pool to obtain the second synthetic prestack angle gather data of several phase domains corresponding to each random synthetic physical property parameter curve;

[0038] Performing a phase inverse transformation on the second synthetic pre-stack angle gather phase data in the phase domain to obtain the second synthetic pre-stack angle gather data.

[0039] As an embodiment of the present specification, the synthesizing the first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data to obtain a synthetic pre-stack seismic angle gather corresponding to the target study area further includes:

[0040] The first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data are introduced into the following equation to synthesize the synthetic pre-stack seismic angle gather F corresponding to the target study area. a :

[0041] F a =F f +βF p

[0042] Among them, F f is the first synthetic pre-stack angle gather data; F p is the second synthetic pre-stack angle gather data; β is the weight coefficient, which is used to adjust the amount of frequency and phase information adopted.

[0043] On the other hand, this article also provides an oil and gas physical property parameter label synthesis device, comprising:

[0044] A distribution characteristic calculation unit is used to import the sample set of oil and gas physical property parameters obtained by well logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area;

[0045] A sampling unit is used to perform random sampling in the physical property parameter distribution characteristic function to obtain a physical property parameter synthetic sample set that meets the physical property parameter distribution characteristics of the study area;

[0046] A curve synthesis unit, for randomly combining samples in a physical property parameter synthesis sample set to obtain a physical property parameter synthesis curve according to the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, and obtaining a reflection coefficient curve according to the physical property parameter synthesis curve;

[0047] A seismic trace synthesis unit, used for convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers;

[0048] The label determination unit is used to determine the oil and gas physical property parameter label data set according to each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthetic curve.

[0049] On the other hand, this paper also provides a method for training an oil and gas physical property parameter prediction model, including:

[0050] Using the method described above, a data set of oil and gas physical property parameter labels is obtained;

[0051] Use the oil and gas physical property parameter label data set to train the oil and gas physical property parameter prediction model;

[0052] The input of the oil and gas physical property parameter prediction model is the pre-stack seismic angle gather of the exploration area, and the output is the predicted oil and gas physical property parameters of the exploration area.

[0053] By adopting the above technical scheme, the oil and gas physical property parameters obtained by logging in the target study area are imported into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area, so as to determine the physical property parameter distribution curve formed by the oil and gas physical property parameters of each hole position in the target study area, and the physical property parameter distribution curve characterizes the overall distribution of oil and gas physical property parameters in the target study area; by randomly sampling in the physical property parameter distribution curve to obtain a number of physical property parameter synthetic sample sets, it is possible to obtain randomly synthesized physical property parameters that meet the oil and gas physical property distribution characteristics of the target study area, and compared with the current randomly generated physical property parameters, the oil and gas physical property parameters that are more in line with the characteristics of the target study area can be obtained; by using the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, the physical property parameter distribution curve can be obtained. The samples in the physical property parameter synthesis sample set are randomly combined to obtain a physical property parameter synthesis curve, and a reflection coefficient curve is obtained according to the physical property parameter synthesis curve, so that the reflection coefficient curve can be obtained using the random synthetic physical property parameters; by convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area, a number of synthetic pre-stack seismic angle gathers are obtained, so that the corresponding pre-stack seismic angle gathers can be obtained using the current random synthetic physical property parameters; by determining the oil and gas physical property parameter label data set according to each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthesis curve, a number of synthetic oil and gas physical property parameter labels can be obtained, and because the physical property parameter distribution curve is used to select the oil and gas physical property parameters, the oil and gas physical property parameter label is less different from the actual label, and the trained prediction model has a higher accuracy rate.

[0054] In order to make the above and other purposes, features and advantages of the present specification more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 An overall system diagram of a method for synthesizing oil and gas physical property parameter labels according to an embodiment of this specification is shown;

[0057] Figure 2 A schematic diagram showing the steps of a method for synthesizing oil and gas physical property parameter labels according to an embodiment of this specification is shown;

[0058] Figure 3A schematic diagram of a porosity characterization function according to an embodiment of this specification is shown;

[0059] Figure 4 A schematic diagram of a synthetic curve of physical property parameters of mud content in an embodiment of this specification is shown;

[0060] Figure 5 A schematic diagram of a synthetic curve of physical property parameters of porosity of an embodiment of this specification is shown;

[0061] Figure 6 A schematic diagram of a synthetic curve of physical property parameters of gas saturation in an embodiment of this specification is shown;

[0062] Figure 7 A schematic diagram of a reflection coefficient curve obtained by synthesizing a physical property parameter curve of an embodiment of this specification is shown;

[0063] Figure 8 A schematic diagram of adjusting the synthetic pre-stack seismic angle gathers in an embodiment of this specification is shown;

[0064] Fig. 9 A schematic diagram of a device for synthesizing oil and gas physical property parameter labels according to an embodiment of this specification is shown;

[0065] Fig.10 A schematic diagram of a computer device according to an embodiment of this specification is shown;

[0066] Figure 11(a)-Figure 11(b) A comparison diagram of the synthetic pre-stack seismic angle gathers and the actual pre-stack seismic angle gathers according to the embodiment of this specification is shown.

[0067] Description of the accompanying symbols:

[0068] 101. parameter memory;

[0069] 102. Arithmetic unit;

[0070] 103. Trainer;

[0071] 104. Prediction terminal;

[0072] 901, distribution feature calculation unit;

[0073] 902. Sampling unit;

[0074] 903, curve synthesis unit;

[0075] 904, seismic trace synthesis unit;

[0076] 905, label determination unit;

[0077] 1002. Computer equipment;

[0078] 1004. Processor;

[0079] 1006. Memory;

[0080] 1008. Driving mechanism;

[0081] 1010, input / output module;

[0082] 1012. Input device;

[0083] 1014. Output device;

[0084] 1016. Presentation equipment;

[0085] 1018. Graphical user interface;

[0086] 1020. Network interface;

[0087] 1022. Communication link;

[0088] 1024. Communication bus. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0090] It should be noted that the terms "first", "second", etc. in this specification and claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0092] like Figure 1The overall system diagram of a method for synthesizing oil and gas physical property parameter labels shown includes: a parameter memory 101, an operator 102, a trainer 103 and a prediction terminal 104.

[0093] The parameter memory 101 is used to store the oil and gas physical property parameters obtained by well logging in the target study area. The oil and gas physical property parameters obtained by well logging are obtained by downhole exploration using exploration tools, that is, the real oil and gas physical property parameters of the target study area.

[0094] The operator 102 is used to fit the physical property parameter distribution curve in the target study area using the oil and gas physical property parameters obtained by logging, and obtain the corresponding reflection coefficient curve, and then use the reflection coefficient curve to convolve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers. Finally, according to the several synthetic pre-stack seismic angle gathers and the random synthetic physical property parameters corresponding to each synthetic pre-stack seismic angle gather, a number of oil and gas physical property parameter labels are determined.

[0095] A trainer 103, used for training an oil and gas physical property parameter prediction model using a plurality of oil and gas physical property parameter labels;

[0096] The input of the oil and gas physical property parameter prediction model is the pre-stack seismic angle gather of the exploration area, and the output is the oil and gas physical property parameters of the exploration area.

[0097] The prediction terminal 104 is used to obtain the pre-stack seismic angle gathers of the target exploration area (target study area) and output the corresponding oil and gas physical property parameters of the target exploration area (target study area).

[0098] Since the current synthesis method for randomly generating oil and gas physical property parameters is too theoretical, the synthesized labels are seriously distorted and differ greatly from the actual labels, and the accuracy of the trained prediction model is low.

[0099] In order to solve the above problems, the embodiments of this specification provide a method for synthesizing oil and gas physical property parameter labels, which can solve the problem that the synthesized labels are quite different from the actual labels. Figure 2 This is a schematic diagram of the steps of a method for synthesizing oil and gas physical property parameter labels provided in an embodiment of this specification. This specification provides method operation steps as described in the embodiment or flow chart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or device product is executed, it can be executed in the order of the method shown in the embodiment or the drawings or in parallel. Specifically, Figure 2 As shown, the method may include:

[0100] Step 201, importing a sample set of oil and gas physical property parameters obtained by well logging in a target study area into an original physical property parameter distribution characteristic function to obtain a physical property parameter distribution characteristic function corresponding to the target study area;

[0101] Step 202, performing random sampling in the physical property parameter distribution characteristic function to obtain a physical property parameter synthetic sample set that meets the physical property parameter distribution characteristics of the study area;

[0102] Step 203: randomly combining samples in the physical property parameter synthesis sample set to obtain a physical property parameter synthesis curve according to the maximum thickness of the target study area, the seismic sampling rate, and the range of lithofacies thickness variation, and obtaining a reflection coefficient curve according to the physical property parameter synthesis curve;

[0103] Step 204, convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers;

[0104] Step 205: Determine an oil and gas physical property parameter label data set based on each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthetic curve.

[0105] By adopting the above technical scheme, the oil and gas physical property parameters obtained by logging in the target study area are imported into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area, so as to determine the physical property parameter distribution curve formed by the oil and gas physical property parameters of each hole position in the target study area, and the physical property parameter distribution curve characterizes the overall distribution of oil and gas physical property parameters in the target study area; by randomly sampling in the physical property parameter distribution curve to obtain a number of physical property parameter synthetic sample sets, it is possible to obtain randomly synthesized physical property parameters that meet the oil and gas physical property distribution characteristics of the target study area, and compared with the current randomly generated physical property parameters, the oil and gas physical property parameters that are more in line with the characteristics of the target study area can be obtained; by using the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, the physical property parameter distribution curve can be obtained. The samples in the physical property parameter synthesis sample set are randomly combined to obtain a physical property parameter synthesis curve, and a reflection coefficient curve is obtained according to the physical property parameter synthesis curve, so that the reflection coefficient curve can be obtained using the random synthetic physical property parameters; by convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area, a number of synthetic pre-stack seismic angle gathers are obtained, so that the corresponding pre-stack seismic angle gathers can be obtained using the current random synthetic physical property parameters; by determining the oil and gas physical property parameter label data set according to each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthesis curve, a number of synthetic oil and gas physical property parameter labels can be obtained, and because the physical property parameter distribution curve is used to select the oil and gas physical property parameters, the oil and gas physical property parameter label is less different from the actual label, and the trained prediction model has a higher accuracy rate.

[0106] As an embodiment of the present specification, step 201, importing a sample set of oil and gas physical property parameters obtained by logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area, further includes:

[0107] Oil and gas physical property parameters can be parameters such as porosity, oil and gas saturation, and permeability that can quantitatively describe the physical properties of underground rocks.

[0108] The statistical characteristics of the actual oil and gas physical property parameter sample set are obtained by statistics, including the total number of samples, the total number of lithofacies types, the prior probability of each lithofacies type, and the mean and variance of the physical property parameters belonging to each lithofacies;

[0109] The sample set of oil and gas physical property parameters obtained by logging in the target study area is imported into the following original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area;

[0110]

[0111] Wherein, x represents the value of oil and gas physical property parameter, M represents the total number of lithofacies types in the actual sample set of oil and gas physical property parameter, the total number of lithofacies types is obtained by comprehensive interpretation of well logging, and the total number of lithofacies types is dimensionless; k represents the kth lithofacies type, dimensionless; α k represents the prior probability of the k-th lithofacies type, which is calculated by counting the proportion of the number of samples belonging to the k-th lithofacies to the total number of samples; μ k represents the mean value of the oil and gas physical parameter samples belonging to the kth lithofacies type in the actual sample set of oil and gas physical parameter, λ k represents the variance of the oil and gas physical property parameter samples of the kth lithofacies type in the actual oil and gas physical property parameter sample set; π is the pi, N represents the total number of samples in the actual sample set of physical property parameters, and xi represents the i-th sample in the actual sample set of physical property parameters.

[0112] like Figure 3The schematic diagram of the porosity characterization function shown in the figure, the vertical direction of the figure represents the number of sampling points with the same porosity in the target study area, and the horizontal direction represents the porosity. For example, one hundred sets of oil and gas physical property parameters are obtained by using exploration equipment to explore the target study area. These one hundred sets of data are respectively brought into the physical property parameter distribution characteristic function to obtain one hundred points on the distribution characteristic plane (the plane characterizing the distribution characteristics of the target study area), and then the one hundred points are used to draw the physical property parameter distribution curve of the target study area. In the distribution curve, it can be seen that the dotted line is the physical property parameter distribution curve, and the columnar data is the oil and gas physical property parameters (porosity) obtained by actual logging observation in the target study area. It can be seen that the physical property parameter distribution characteristic function of this specification has a good effect in characterizing the target study area, and the trend of the obtained physical property parameter distribution characteristic function is closer to the actual distribution of oil and gas physical property parameters in the target study area.

[0113] Compared with the prior art which uses one hundred groups of oil and gas physical property parameters to be obtained by exploration in the target study area and directly generates oil and gas physical property parameters randomly, this specification uses physical property parameter distribution curves to characterize the overall distribution of physical property parameters at different locations in the target study area, and performs random sampling in the obtained physical property parameter distribution curves to obtain a number of randomly synthesized physical property parameters. Therefore, the randomly synthesized physical property parameters finally obtained in this specification are more in line with the actual situation of the target study area, and thus the finally obtained oil and gas physical property parameter labels can be more consistent with the actual oil and gas physical property parameter labels, so that the trained prediction model can have a higher accuracy rate.

[0114] As an embodiment of the present specification, step 202, random sampling is performed in the physical property parameter distribution characteristic function to obtain a synthetic sample set of physical property parameters that conforms to the physical property parameter distribution characteristics of the study area.

[0115] In this step, the Markov chain Monte Carlo random sampling method is used to perform random sampling in the fitted physical property parameter distribution characteristic function to obtain a large number of random synthetic physical property parameters that conform to the physical property parameter distribution characteristics, forming a synthetic sample set of physical property parameters that conform to the physical property parameter distribution characteristics of the study area.

[0116] As an embodiment of the present specification, according to the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, the samples in the physical property parameter synthesis sample set are randomly combined to obtain a physical property parameter synthesis curve, including:

[0117] According to the maximum time thickness of the overall target layer in the target study area, the seismic sampling rate in the target study area and the thickness variation range of each lithofacies, the physical parameters are randomly combined from shallow to deep to obtain a large number of physical parameter synthetic curves with the same length.

[0118] like Figure 4 The schematic diagram of the composite curve of physical property parameters of mud content is shown in Figure 5 The schematic diagram of the synthetic curve of the physical property parameters of porosity is shown in Figure 6 The schematic diagram of the synthetic curve of physical property parameters of gas saturation is shown. It can be seen that the synthetic physical property parameter curves have different shapes and can better simulate the values ​​of different physical property parameters.

[0119] like Figure 7 The schematic diagram of obtaining a reflectance curve by synthesizing a physical property parameter is shown. As an embodiment of the present specification, obtaining a reflectance curve by synthesizing a physical property parameter further includes:

[0120] Step 701, converting the physical property parameter synthesis curve into the elastic parameter synthesis curve according to the physical property elasticity conversion equation;

[0121] According to the correlation between lithofacies and pore structure, the physical property elasticity conversion equation is established for the study area of ​​n types of lithofacies.

[0122] The physical property parameter synthesis curve is introduced into the following physical property elastic conversion equation to obtain the corresponding elastic parameter curve, wherein the conversion equation is determined based on the rock physics correlation between the elastic parameters and the physical property parameters under the influence of the two factors of lithofacies and pore structure;

[0123]

[0124] Where x represents the oil and gas physical property parameter value; m represents the elastic parameter value; γ j represents the pore structure parameters corresponding to the jth rock in the actual study area, obtained from the rock physics experiment in the study area; j (x,γ j ) represents the deterministic multi-porous structure rock physics model corresponding to the j-th rock; ε j represents the error between the deterministic porous structure rock physics model corresponding to the j-th rock and the actual observed data, j = 1, 2, ... M; L(j) is an M × 1 lithofacies constraint vector, in which all elements except the j-th value are 1 and the others are 0

[0125] A composite curve of elastic parameters of the target study area is drawn according to several elastic parameter values.

[0126] Step 702: Convert the elastic parameter synthesis curve into a reflection coefficient curve according to a plane wave reflection equation.

[0127] The elastic parameter synthetic curve is converted into a reflection coefficient curve using the Zoeppritz plane wave reflection equation.

[0128] As an embodiment of the present specification, the reflection coefficient curve is convolved with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers, specifically including:

[0129] Based on the seismic wave convolution theory, the incident angle range of the pre-stack angle gather is set, and the reflection coefficient curve is convolved with the statistical seismic wavelets of the near, medium and far angle partial stack gathers in the target study area to obtain a large number of corresponding synthetic pre-stack seismic angle gathers.

[0130] Then, according to several synthetic prestack seismic angle gathers and the random synthetic physical property parameters corresponding to each synthetic prestack seismic angle gather, several oil and gas physical property parameter labels are determined. At this time, the oil and gas physical property parameter labels can be obtained, wherein the oil and gas physical property parameters included in the oil and gas physical property parameter labels are randomly generated according to the physical property parameter distribution curve, and the corresponding synthetic prestack seismic angle gathers are generated according to the formula, so compared with the prior art, the oil and gas physical property parameters are directly randomly generated, and the synthetic prestack seismic angle gathers are generated using the randomly generated oil and gas physical property parameters. The oil and gas physical property parameter labels generated in this specification are more in line with the actual situation of the target study area.

[0131] It should be noted that, in order to simplify the description, the pre-stack angle gather in this specification refers to the pre-stack seismic angle gather.

[0132] like Figure 8 The schematic diagram of adjusting the synthetic pre-stack seismic angle gathers shown in the figure is an embodiment of the present specification, wherein the reflection coefficient curve is convolved with the statistical seismic wavelet corresponding to the target study area to obtain a plurality of synthetic pre-stack seismic angle gathers, and then the following steps are included:

[0133] Step 801, replacing the frequency band information of some frequency bands in the synthetic pre-stack seismic angle gather with the frequency band data of the corresponding frequency bands of the measured pre-stack seismic angle gather to obtain the first synthetic pre-stack angle gather data;

[0134] First, the prestack angle gathers in the time domain are converted to the frequency domain.

[0135] According to the actual plane distribution of the observation system in the target study area, pre-stack angle gathers of multiple observation points are selected and spectral decomposition is performed to obtain frequency band data of three types of frequency bands for each observation point to form a spectrum pool; in this specification, the frequency bands can be divided into three categories according to the length of the frequency bands. For example, if the total length of the frequency band of each observation point is 50 Hz, then 1-15 Hz can be set as the first type of frequency band data, 16-35 Hz can be set as the second type of frequency band data, and 36-50 Hz can be set as the third type of frequency band data.

[0136] First, the synthetic pre-stack seismic angle gathers in the time domain are converted to the frequency domain.

[0137] Perform spectral decomposition on the synthetic pre-stack seismic angle gathers to obtain frequency band data of three types of frequency bands corresponding to each synthetic pre-stack seismic angle gather; since this specification convolves the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain several synthetic pre-stack seismic angle gathers, the total frequency band length of the synthetic pre-stack seismic angle gathers is consistent with the total frequency band length of the pre-stack seismic angle gathers of each observation point in the actual observation system. Therefore, the synthetic pre-stack seismic angle gathers can also be divided into three categories at equal intervals according to the length of the frequency bands. For example, if the total frequency band length of each synthetic pre-stack seismic angle gather is 50Hz, then 1-15Hz can be set as the first type of frequency band data, 16-35Hz can be set as the second type of frequency band data, and 36-50Hz can be set as the third type of frequency band data.

[0138] The frequency band data of the first frequency band and the frequency band data of the third frequency band of the spectrum data of each synthetic pre-stack angle gather are replaced with the frequency band data of the first frequency band and the frequency band data of the third frequency band randomly selected from the spectrum pool to obtain a plurality of first synthetic pre-stack angle gather data in frequency domains corresponding to each random synthetic physical property parameter curve;

[0139] In this specification, since there are several first-category frequency band data and third-category frequency band data obtained from actual observations of the same target study area in the spectrum pool, such as first-category frequency band data A, first-category frequency band data B, first-category frequency band data C; third-category frequency band data E, third-category frequency band data F, third-category frequency band data G; if the spectrum data of a synthetic pre-stack angle gather includes first-category frequency band data 1, second-category frequency band data 2, and third-category frequency band data 3. Since low-frequency band data in a frequency band data usually corresponds to background data in the data, and high-frequency band data usually corresponds to noise data in the data, it is easier to reflect the actual characteristics. This specification uses frequency band data obtained from different actual observations to replace the spectral data of the synthetic pre-stack angle gather. For example, the first frequency band data A and the third frequency band data E are used to replace the first frequency band data 1 and the third frequency band data 3, and the spectral data of the synthetic pre-stack angle gather finally obtained includes the first frequency band data A, the second frequency band data 2, and the third frequency band data E. Similarly, the first frequency band data A and the third frequency band data F can also be used to replace the first frequency band data 1 and the third frequency band data 3, and the spectral data of the synthetic pre-stack angle gather finally obtained includes the first frequency band data A, the second frequency band data 2, and the third frequency band data F. And so on, a synthetic pre-stack angle gather in which multiple first frequency band data and third frequency band data are replaced by the actually observed frequency band data can be obtained.

[0140] Performing spectrum inverse transformation on the frequency domain data of the first synthetic pre-stack angle gather in the frequency domain to obtain the first synthetic pre-stack angle gather data.

[0141] Step 802, replacing the phase information of some phase segments in the synthetic pre-stack seismic angle gather with the phase data of the corresponding phase segments of the measured pre-stack seismic angle gather to obtain second synthetic pre-stack angle gather data;

[0142] First, the prestack angle gathers in the time domain are converted to the phase domain.

[0143] According to the actual plane distribution of the observation system in the target study area, the prestack angle gathers of multiple observation points are selected and phase decomposition is performed to obtain the phase data of three types of phase segments of each observation point to form a phase pool;

[0144] In this specification, the phase segments can be divided into three categories according to the length of the phase. For example, if the total length of the phase segment of each observation point is 60°, then 1-20° can be set as the first category of phase segment data, 21-40° can be set as the second category of phase segment data, and 41-60° can be set as the third category of phase segment data.

[0145] First, the synthetic prestack seismic angle gathers in the time domain are converted to the phase domain.

[0146] Perform phase decomposition on synthetic pre-stack seismic angle gathers to obtain phase data of three types of phase segments corresponding to each synthetic pre-stack seismic angle gather;

[0147] Since the present specification convolves the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain several synthetic pre-stack seismic angle gathers, the total length of the phase segments of the synthetic pre-stack seismic angle gathers is consistent with the total length of the phase segments of the pre-stack seismic angle gathers of each observation point in the actual observation system. Therefore, the synthetic pre-stack seismic angle gathers can also be divided into three categories according to the length of the phase segments. For example, if the total length of the phase segments of each synthetic pre-stack seismic angle gather is 60°, then 1-20° can be set as the first type of phase segment data, 21-40° can be set as the second type of phase segment data, and 41-60° can be set as the third type of phase segment data.

[0148] The phase data of the first type of phase segment and the phase data of the third type of phase segment of each phase segment data of the synthetic prestack angle gather are replaced with the phase data of the first type of phase segment and the phase data of the third type of phase segment randomly selected from the phase pool to obtain the second synthetic prestack angle gather data of several phase domains corresponding to each random synthetic physical property parameter curve;

[0149] In this specification, since there are several first-type phase segment phase data and third-type phase segment phase data obtained from actual observations of the same target study area in the phase pool, such as first-type phase segment data A, first-type phase segment data B, first-type phase segment data C; third-type phase segment data E, third-type phase segment data F, third-type phase segment data G; if the phase data of a synthetic pre-stack angle gather includes first-type phase segment data 1, second-type phase segment data 2, and third-type phase segment data 3. Since low-phase segment data in a phase segment data usually corresponds to background data in the data, and high-phase segment data usually corresponds to noise data in the data, it is easier to reflect the characteristics in practice. This specification uses different phase data obtained from actual observations to replace the phase data of the synthetic pre-stack angle gather. For example, the first phase segment data A and the third phase segment data E are used to replace the first phase segment data 1 and the third phase segment data 3, and the phase data of the synthetic pre-stack angle gather finally obtained includes the first phase segment data A, the second phase segment data 2, and the third phase segment data E; similarly, the first phase segment data A and the third phase segment data F can also be used to replace the first phase segment data 1 and the third phase segment data 3, and the phase data of the synthetic pre-stack angle gather finally obtained includes the first phase segment data A, the second phase segment data 2, and the third phase segment data F, and so on, to obtain a synthetic pre-stack angle gather in which multiple first phase segment data and third phase segment data are replaced by the actually observed phase segment data.

[0150] Performing a phase inverse transformation on the second synthetic pre-stack angle gather phase data in the phase domain to obtain the second synthetic pre-stack angle gather data.

[0151] Step 803, synthesizing the first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data to obtain a synthetic pre-stack seismic angle gather corresponding to the target study area;

[0152] The first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data are introduced into the following equation to synthesize the synthetic pre-stack seismic angle gather F corresponding to the target study area. a :

[0153] F a =F f +βF p

[0154] Among them, F f is the first synthetic pre-stack angle gather data; F p is the second synthetic pre-stack angle gather data; β is the weight coefficient, which is used to adjust the amount of frequency and phase information adopted.

[0155] like Figure 11(a)-Figure 11(b)The comparison diagram of the synthetic pre-stack seismic angle gather and the actual pre-stack seismic angle gather shown shows that the synthetic pre-stack seismic angle gather synthesized in this specification has a high similarity with the actual seismic angle gather. Therefore, the oil and gas physical property parameter labels in this specification are more consistent with the target study area.

[0156] Step 804: The physical property parameter synthetic curve and the corresponding synthetic generalized pre-stack seismic angle gather constitute an oil and gas physical property parameter label data set.

[0157] The physical property parameter synthetic curve and the corresponding synthetic generalized prestack seismic angle gather constitute the oil and gas physical property parameter label data set; specifically, using a pair of calculated oil and gas physical property parameters and synthetic prestack seismic angle gathers, multiple types of generalized synthetic prestack seismic angle gathers can be generated, that is, one-to-many data are generated through one-to-one data, which increases the number of oil and gas physical property parameter labels. Moreover, since the generalized synthetic prestack seismic angle gathers replace the prestack seismic angle gathers obtained by actual observation, the final oil and gas physical property parameter labels are closer to the actual situation of the target study area.

[0158] For different study areas, the lithofacies type, mean and variance in the characteristic characterization function, seismic wavelet, spectrum and phase pool of the actual pre-stack seismic angle gather are updated to obtain generalized label data suitable for the study area.

[0159] like Fig. 9 The schematic diagram of a device for synthesizing oil and gas physical property parameter labels is shown, comprising:

[0160] The distribution characteristic calculation unit 901 is used to import the sample set of oil and gas physical property parameters obtained by well logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area;

[0161] A sampling unit 902 is used to perform random sampling in the physical property parameter distribution characteristic function to obtain a physical property parameter synthetic sample set that meets the physical property parameter distribution characteristics of the study area;

[0162] The curve synthesis unit 903 is used to randomly combine the samples in the physical property parameter synthesis sample set to obtain a physical property parameter synthesis curve according to the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness, and obtain a reflection coefficient curve according to the physical property parameter synthesis curve;

[0163] A seismic trace synthesis unit 904 is used to convolve the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers;

[0164] The label determination unit 905 is used to determine the oil and gas physical property parameter label data set according to each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthetic curve.

[0165] By adopting the above technical scheme, the oil and gas physical property parameters obtained by logging in the target study area are imported into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area, so as to determine the physical property parameter distribution curve formed by the oil and gas physical property parameters of each hole position in the target study area, and the physical property parameter distribution curve characterizes the overall distribution of oil and gas physical property parameters in the target study area; by randomly sampling in the physical property parameter distribution curve to obtain a number of physical property parameter synthetic sample sets, it is possible to obtain randomly synthesized physical property parameters that meet the oil and gas physical property distribution characteristics of the target study area, and compared with the current randomly generated physical property parameters, the oil and gas physical property parameters that are more in line with the characteristics of the target study area can be obtained; by using the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, the physical property parameter distribution curve can be obtained. The samples in the physical property parameter synthesis sample set are randomly combined to obtain a physical property parameter synthesis curve, and a reflection coefficient curve is obtained according to the physical property parameter synthesis curve, so that the reflection coefficient curve can be obtained using the random synthetic physical property parameters; by convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area, a number of synthetic pre-stack seismic angle gathers are obtained, so that the corresponding pre-stack seismic angle gathers can be obtained using the current random synthetic physical property parameters; by determining the oil and gas physical property parameter label data set according to each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthesis curve, a number of synthetic oil and gas physical property parameter labels can be obtained, and because the physical property parameter distribution curve is used to select the oil and gas physical property parameters, the oil and gas physical property parameter label is less different from the actual label, and the trained prediction model has a higher accuracy rate.

[0166] This specification also provides a method for training an oil and gas physical property parameter prediction model, including:

[0167] Using the method described above, a data set of oil and gas physical property parameter labels is obtained;

[0168] Use the oil and gas physical property parameter label data set to train the oil and gas physical property parameter prediction model;

[0169] The input of the oil and gas physical property parameter prediction model is the pre-stack seismic angle gather of the exploration area, and the output is the predicted oil and gas physical property parameters of the exploration area.

[0170] like Fig.10As shown, a computer device provided by an embodiment of this specification, the computer device 1002 may include one or more processors 1004, such as one or more central processing units (CPUs), each processing unit may implement one or more hardware threads. The computer device 1002 may also include any memory 1006, which is used to store any kind of information such as code, settings, data, etc. Non-restrictive, for example, the memory 1006 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 1002. In one case, when the processor 1004 executes an associated instruction stored in any memory or a combination of memories, the computer device 1002 can perform any operation of the associated instruction. The computer device 1002 also includes one or more drive mechanisms 1008 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0171] The computer device 1002 may also include an input / output module 1010 (I / O) for receiving various inputs (via input devices 1012) and for providing various outputs (via output devices 1014). A specific output mechanism may include a presentation device 1016 and an associated graphical user interface (GUI) 1018. In other embodiments, the input / output module 1010 (I / O), the input device 1012, and the output device 1014 may not be included, and the computer device 1002 may be used as a computer device in a network. The computer device 1002 may also include one or more network interfaces 1020 for exchanging data with other devices via one or more communication links 1022. One or more communication buses 1024 couple the components described above together.

[0172] The communication link 1022 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1022 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0173] Corresponds to Figure 2-Figure 8 The method in the embodiment of the present specification also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0174] The embodiment of the present specification also provides a computer-readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the following Figure 2-Figure 8 The method shown.

[0175] It should be understood that in the various embodiments of this specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0176] It should also be understood that in the embodiments of this specification, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this specification generally indicates that the associated objects before and after are in an "or" relationship.

[0177] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0178] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0179] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0180] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0181] In addition, each functional unit in each embodiment of this specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0182] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0183] Specific embodiments are used in this specification to illustrate the principles and implementation methods of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of this specification. At the same time, for those skilled in the art, according to the ideas of this specification, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this specification.

Claims

1. A method for synthesizing oil and gas physical property parameter labels, characterized in that: include: Importing a sample set of oil and gas physical property parameters obtained by logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area; Random sampling is performed in the physical property parameter distribution characteristic function to obtain a physical property parameter synthetic sample set that meets the physical property parameter distribution characteristics of the study area; According to the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, the samples in the physical property parameter synthesis sample set are randomly combined to obtain a physical property parameter synthesis curve, and a reflection coefficient curve is obtained according to the physical property parameter synthesis curve; Convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers; According to each synthetic pre-stack seismic angle gather and the corresponding physical parameter synthetic curve, the oil and gas physical parameter label data set is determined.

2. The method for synthesizing oil and gas physical property parameter labels according to claim 1, characterized in that: The step of importing the sample set of oil and gas physical property parameters obtained by logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area further includes: The statistical characteristics of the actual oil and gas physical property parameter sample set are obtained by statistics, including the total number of samples, the total number of lithofacies types, the prior probability of each lithofacies type, and the mean and variance of the physical property parameters belonging to each lithofacies; The sample set of oil and gas physical property parameters obtained by logging in the target study area is imported into the following original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area; Wherein, x represents the value of oil and gas physical property parameter, M represents the total number of lithofacies types in the actual sample set of oil and gas physical property parameter, the total number of lithofacies types is obtained by comprehensive interpretation of well logging, and the total number of lithofacies types is dimensionless; k represents the kth lithofacies type, dimensionless; α k represents the prior probability of the k-th lithofacies type, which is calculated by counting the proportion of the number of samples belonging to the k-th lithofacies to the total number of samples; μ k represents the mean value of the oil and gas physical parameter samples belonging to the kth lithofacies type in the actual sample set of oil and gas physical parameter, λ k represents the variance of the oil and gas physical property parameter samples of the kth lithofacies type in the actual oil and gas physical property parameter sample set; π is the pi, N represents the total number of samples in the actual sample set of physical property parameters, and xi represents the i-th sample in the actual sample set of physical property parameters.

3. The method for synthesizing oil and gas physical property parameter labels according to claim 1, characterized in that: The step of obtaining a reflection coefficient curve according to the physical property parameter synthesis curve further comprises: Converting the physical property parameter composite curve into an elastic parameter composite curve according to a physical property elasticity conversion equation; The elastic parameter synthesis curve is converted into a reflection coefficient curve according to the plane wave reflection equation.

4. The method for synthesizing oil and gas physical property parameter labels according to claim 3, characterized in that: The step of converting the physical property parameter composite curve into the elastic parameter composite curve according to the physical property elasticity conversion equation further comprises: The physical property parameter synthesis curve is introduced into the following physical property elastic conversion equation to obtain the corresponding elastic parameter curve, wherein the conversion equation is determined based on the rock physics correlation between the elastic parameters and the physical property parameters under the influence of the two factors of lithofacies and pore structure; Where x represents the oil and gas physical property parameter value; m represents the elastic parameter value; γ j represents the pore structure parameters corresponding to the jth rock in the actual study area, obtained from the rock physics experiment in the study area; j (x,γ j ) represents the deterministic multi-porous structure rock physics model corresponding to the j-th rock; ε j It represents the error between the deterministic multi-porous structure rock physics model corresponding to the j-th rock and the actual observation data, j = 1, 2, ... M; L(j) is an M × 1 lithofacies constraint vector, in which all elements except the j-th value are 0.

5. The method for synthesizing oil and gas physical property parameter labels according to claim 1, characterized in that: After convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a plurality of synthetic pre-stack seismic angle gathers, the method further includes: The frequency band information of some frequency bands in the synthetic pre-stack seismic angle gather is replaced with the frequency band data of the corresponding frequency bands of the measured pre-stack seismic angle gather to obtain the first synthetic pre-stack angle gather data; The phase information of some phase segments in the synthetic pre-stack seismic angle gather is replaced with the phase data of the corresponding phase segments in the measured pre-stack seismic angle gather to obtain the second synthetic pre-stack angle gather data; The first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data are synthesized to obtain a synthetic pre-stack seismic angle gather corresponding to the target study area; The physical property parameter composite curve and the corresponding synthetic generalized pre-stack seismic angle gather constitute the oil and gas physical property parameter label data set.

6. The method for synthesizing oil and gas physical property parameter labels according to claim 5, characterized in that: The step of replacing the frequency band information of some frequency bands in the synthetic pre-stack seismic angle gather with the frequency band data of the corresponding frequency bands of the measured pre-stack seismic angle gather to obtain the first synthetic pre-stack angle gather data further comprises: According to the actual plane distribution of the observation system in the target study area, prestack angle gathers of multiple observation points are selected and spectral decomposition is performed to obtain frequency band data of three types of frequency bands of each observation point to form a spectrum pool; Perform spectrum decomposition on synthetic pre-stack seismic angle gathers to obtain frequency band data of three frequency bands corresponding to each synthetic pre-stack seismic angle gather; The frequency band data of the first frequency band and the frequency band data of the third frequency band of the spectrum data of each synthetic pre-stack angle gather are replaced with the frequency band data of the first frequency band and the frequency band data of the third frequency band randomly selected from the spectrum pool to obtain a plurality of first synthetic pre-stack angle gather data in frequency domains corresponding to each random synthetic physical property parameter curve; Performing spectrum inverse transformation on the frequency domain data of the first synthetic pre-stack angle gather in the frequency domain to obtain the first synthetic pre-stack angle gather data.

7. The method for synthesizing oil and gas physical property parameter labels according to claim 5, characterized in that: The step of replacing the phase information of some phase segments in the synthetic pre-stack seismic angle gather with the phase data of the corresponding phase segments of the measured pre-stack seismic angle gather to obtain the second synthetic pre-stack angle gather data further comprises: According to the actual plane distribution of the observation system in the target study area, the prestack angle gathers of multiple observation points are selected and phase decomposition is performed to obtain the phase data of three types of phase segments of each observation point to form a phase pool; Perform phase decomposition on synthetic pre-stack seismic angle gathers to obtain phase data of three types of phase segments corresponding to each synthetic pre-stack seismic angle gather; The phase data of the first type of phase segment and the phase data of the third type of phase segment of each phase segment data of the synthetic prestack angle gather are replaced with the phase data of the first type of phase segment and the phase data of the third type of phase segment randomly selected from the phase pool to obtain the second synthetic prestack angle gather data of several phase domains corresponding to each random synthetic physical property parameter curve; Performing a phase inverse transformation on the second synthetic pre-stack angle gather phase data in the phase domain to obtain the second synthetic pre-stack angle gather data.

8. The method for synthesizing oil and gas physical property parameter labels according to claim 5, characterized in that: The synthesizing the first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data to obtain a synthetic pre-stack seismic angle gather corresponding to the target study area further comprises: The first synthetic pre-stack angle gather data and the second synthetic pre-stack angle gather data are introduced into the following equation to synthesize the synthetic pre-stack seismic angle gather F corresponding to the target study area. a : F a =F f +βF p Among them, F f is the first synthetic pre-stack angle gather data; F p is the second synthetic pre-stack angle gather data; β is the weight coefficient, which is used to adjust the amount of frequency and phase information adopted.

9. An oil and gas physical property parameter label synthesis device, characterized in that: include: A distribution characteristic calculation unit is used to import the sample set of oil and gas physical property parameters obtained by well logging in the target study area into the original physical property parameter distribution characteristic function to obtain the physical property parameter distribution characteristic function corresponding to the target study area; A sampling unit is used to perform random sampling in the physical property parameter distribution characteristic function to obtain a physical property parameter synthetic sample set that meets the physical property parameter distribution characteristics of the study area; A curve synthesis unit, for randomly combining samples in a physical property parameter synthesis sample set to obtain a physical property parameter synthesis curve according to the maximum thickness of the target study area, the seismic sampling rate and the range of lithofacies thickness variation, and obtaining a reflection coefficient curve according to the physical property parameter synthesis curve; A seismic trace synthesis unit, used for convolving the reflection coefficient curve with the statistical seismic wavelet corresponding to the target study area to obtain a number of synthetic pre-stack seismic angle gathers; The label determination unit is used to determine the oil and gas physical property parameter label data set according to each synthetic pre-stack seismic angle gather and the corresponding physical property parameter synthetic curve.

10. A method for training a prediction model for oil and gas physical property parameters, characterized in that: include: Using the method described in any one of claims 1 to 8, obtaining a data set of oil and gas physical property parameter labels; Use the oil and gas physical property parameter label data set to train the oil and gas physical property parameter prediction model; The input of the oil and gas physical property parameter prediction model is the pre-stack seismic angle gather of the exploration area, and the output is the predicted oil and gas physical property parameters of the exploration area.

Images