Coal seam gas content prediction method, device, equipment, storage medium and product

Abstract

The embodiment of the invention provides a coal seam gas content prediction method and device, equipment, a storage medium and a product. Comprising the steps of obtaining a corresponding relation between different-source multi-scale data of a target area and coal seam gas content, and making a plurality of cross plots according to the corresponding relation; determining main control factor data of the coal seam gas content according to the plurality of cross plots; performing data preprocessing on the main control factor data to obtain parameter data with the same scale; inputting the parameter data with the same scale into a pre-trained single-scale coal seam gas content prediction model to obtain a seismic scale coal seam gas content prediction result, a logging scale coal seam gas content prediction result and a geological scale coal seam gas content prediction result; carrying out nonlinear fusion on all prediction results to obtain a coal seam gas content prediction value; and outputting a coal seam gas content plane distribution diagram of the target area according to the coal seam gas content predicted value. The method is used for achieving the effect of improving the coal seam gas content prediction precision.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration technology, and in particular to a method, device, equipment, storage medium and product for predicting the gas content of coal seams. Background Art

[0002] With the widespread application of multi-source and multi-scale data such as seismic, well logging, and geology, the comprehensive use of multi-source and multi-scale data can effectively improve the understanding and evaluation of coalbed methane resources and improve the accuracy of coalbed gas content prediction. In practical applications, there are significant differences in the scale, information characteristics, and noise levels of different data sources. How to effectively integrate these data, eliminate redundant features, and retain key features has become a key issue in coalbed gas content prediction.

[0003] In the existing technology, coal seam gas content is predicted based on a simple data linear weighted fusion strategy. However, this method fails to fully exploit the complementarity between multi-source and multi-scale data, resulting in low coal seam gas content prediction accuracy. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment, storage medium and product for predicting the gas content of a coal seam, so as to achieve the effect of improving the accuracy of predicting the gas content of a coal seam.

[0005] In a first aspect, an embodiment of the present application provides a coal seam gas content prediction method, which is applied to a computer device, comprising: acquiring multi-scale data from different sources in a target area; acquiring a correspondence between the coal seam gas content and the multi-scale data from different sources, and making a plurality of intersection diagrams based on the correspondence; determining the main control factor data of the coal seam gas content based on the plurality of intersection diagrams; performing data preprocessing on the main control factor data to obtain parameter data of the same scale; inputting the parameter data of the same scale into a pre-trained seismic scale coal seam gas content prediction model to obtain a seismic scale coal seam gas content prediction result for the target area; and inputting the parameter data of the same scale into a pre-trained seismic scale coal seam gas content prediction model to obtain a seismic scale coal seam gas content prediction result for the target area. The parameter data is input into a pre-trained logging scale coal seam gas content prediction model to obtain the logging scale coal seam gas content prediction result of the target area; the parameter data with the same scale is input into a pre-trained geological scale coal seam gas content prediction model to obtain the geological scale coal seam gas content prediction result of the target area; the seismic scale coal seam gas content prediction result, the logging scale coal seam gas content prediction result and the geological scale coal seam gas content prediction result are nonlinearly fused by a nonlinear integration method to obtain a coal seam gas content prediction value; according to the coal seam gas content prediction value, a coal seam gas content plane distribution map of the target area is output.

[0006] In one possible implementation, the parameter data includes seismic scale data; accordingly, the training process of the seismic scale coal seam gas content prediction model includes: using different machine learning algorithms to respectively construct initial seismic scale coal seam gas content prediction models for the seismic scale data; for any machine learning algorithm, according to each training result, using the Bayesian hyperparameter optimization method to optimize the parameters of the seismic scale coal seam gas content prediction model, and calculating the seismic scale coal seam gas content prediction error; among the different machine learning algorithms, selecting the machine learning algorithm with the smallest seismic scale coal seam gas content prediction error to establish a trained seismic scale coal seam gas content prediction model.

[0007] In one possible implementation, the machine learning algorithm is a random forest algorithm; accordingly, the training process of the seismic-scale coal seam gas content prediction model includes: randomly extracting samples with replacement to generate multiple different training subsets, wherein each training subset includes parameter data in a sample well; for each data subset, constructing a decision tree, and randomly selecting multiple feature data for subset data modeling; selecting the optimal feature value from the multiple feature data as a split point according to a preset criterion, and dividing the coal seam gas content data; continuing to split the two data areas obtained by the division according to the preset criterion until the preset stop condition is met, thereby completing the establishment of the seismic-scale coal seam gas content prediction model corresponding to the data subset; the seismic-scale coal seam gas content prediction models corresponding to all data subsets constitute the seismic-scale coal seam gas content prediction model.

[0008] In a possible implementation manner, the formula for dividing the coal seam gas content data is:

[0009]

[0010] In the formula, y i The distribution is the gas content value of coal seams in different areas; Represents the region R1(j,s)={x|x j ≤s} the average value of coal seam gas content; The region R2(j,s)={x|x j ≥s} the average gas content of coal seams; x j represents the jth feature in the subset; s represents the split point; N1 represents the number of samples in the R1 region, and N2 represents the number of samples in the R2 region; MSE(j,i) represents traversing the multiple randomly selected feature data and taking the feature value with the smallest MSE as the optimal feature value.

[0011] In a possible implementation, the calculation formula of the prediction result of the seismic scale coal seam gas content prediction model is:

[0012]

[0013]

[0014] Where, T b represents the coal seam gas content prediction model of each subset; x test represents each training subset; represents the coal seam gas content prediction result given by the coal seam gas content prediction model of each subset; B represents the number of subsets; It represents the prediction result of the seismic scale coal seam gas content prediction model.

[0015] In a possible implementation, the parameter data also includes well logging scale data; accordingly, the training process of the well logging scale coal seam gas content prediction model includes: using different machine learning algorithms to respectively construct initial well logging scale coal seam gas content prediction models for the well logging scale data; for any machine learning algorithm, according to each training result, using the Bayesian hyperparameter optimization method to optimize the parameters of the well logging scale coal seam gas content prediction model, and calculating the well logging scale coal seam gas content prediction error; among the different machine learning algorithms, selecting the machine learning algorithm with the smallest well logging scale coal seam gas content prediction error to establish a trained well logging scale coal seam gas content prediction model.

[0016] In one possible implementation, the parameter data also includes geological scale data; accordingly, the training process of the geological scale coal seam gas content prediction model includes: using different machine learning algorithms to respectively construct initial geological scale coal seam gas content prediction models for the geological scale data; for any machine learning algorithm, according to each training result, using the Bayesian hyperparameter optimization method to optimize the parameters of the geological scale coal seam gas content prediction model, and calculating the geological scale coal seam gas content prediction error; among the different machine learning algorithms, selecting the machine learning algorithm with the smallest geological scale coal seam gas content prediction error to establish a trained geological scale coal seam gas content prediction model.

[0017] In a possible implementation, the calculation formula for the predicted value of coal seam gas content is:

[0018]

[0019] In the formula, Represents the predicted value of gas content in the coal seam; RF (1,2,3) W respectively represent the seismic scale coal seam gas content prediction model, the well logging scale coal seam gas content prediction model and the geological scale coal seam gas content prediction model; l (1,2,3) represents the weight of the coal seam gas content prediction model at different scales in the lth layer, bl represents the bias of the lth layer, F l represents the activation function of the lth layer, l = 1, 2, …, L.

[0020] In a possible implementation, the main controlling factor data include seismic data, well logging data and geological data; accordingly, the data preprocessing of the main controlling factor data includes: performing mathematical transformation on the seismic data to obtain seismic attributes related to the gas content of the coal seam; for the well logging data, obtaining a three-dimensional data body of the same scale as the seismic data through a high-resolution inversion method; for the geological data, obtaining geological attributes of the same scale as the seismic data through indirect reflection through the seismic attributes and / or the three-dimensional data body.

[0021] In a possible implementation, the geological data includes coal body structure and fracture data, fault data and discrete factor data; accordingly, the geological data is indirectly reflected through the seismic attributes and / or the three-dimensional data body, including: indirectly reflecting the coal body structure and fracture data through density values, wherein the density values ​​are determined through the logging data; indirectly reflecting the fault data through dip attributes, wherein the dip attributes are determined through the seismic attributes; determining the estimated value of each discrete factor data through the Kriging interpolation method, wherein the determination formula of the estimated value of each discrete factor data is:

[0022]

[0023] In the formula, G(x0,y0) represents the estimated value of the point (x0,y0) in the spatiotemporal domain; G(x i ,y j ) is the discrete factor data; k(i,j) is the weight coefficient of different spatial points.

[0024] In a second aspect, an embodiment of the present application provides a coal seam gas content prediction device, which is applied to a computer device, including:

[0025] The data acquisition module is used to obtain multi-scale data from different sources in the target area.

[0026] The intersection diagram drawing module is used to obtain the corresponding relationship between the coal seam gas content and the multi-scale data from different sources, and to produce multiple intersection diagrams based on the corresponding relationship.

[0027] The main controlling factor data determination module is used to determine the main controlling factor data of the coal seam gas content according to the multiple intersection diagrams.

[0028] The data preprocessing module is used to perform data preprocessing on the main control factor data to obtain parameter data with the same scale.

[0029] The seismic scale coal seam gas content prediction module is used to input the parameter data of the same scale into the pre-trained seismic scale coal seam gas content prediction model to obtain the seismic scale coal seam gas content prediction result of the target area.

[0030] The well logging scale coal seam gas content prediction module is used to input the parameter data of the same scale into the pre-trained well logging scale coal seam gas content prediction model to obtain the well logging scale coal seam gas content prediction result of the target area.

[0031] The geological scale coal seam gas content prediction module is used to input parameter data of the same scale into a pre-trained geological scale coal seam gas content prediction model to obtain the geological scale coal seam gas content prediction result of the target area.

[0032] The nonlinear fusion module is used to nonlinearly fuse the seismic scale coal seam gas content prediction result, the logging scale coal seam gas content prediction result and the geological scale coal seam gas content prediction result using a nonlinear integration method to obtain a coal seam gas content prediction value.

[0033] The output module is used to output a planar distribution map of the coal seam gas content in the target area according to the predicted value of the coal seam gas content.

[0034] In a third aspect, an embodiment of the present application provides a computer device, including: a memory, a processor;

[0035] The memory stores computer-executable instructions;

[0036] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0038] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0039] The coal seam gas content prediction method, device, equipment, storage medium and product provided in the embodiments of the present application use artificial intelligence algorithms to output seismic-scale coal seam gas content prediction results, well logging-scale coal seam gas content prediction results and geological-scale coal seam gas content prediction results for multi-source and multi-scale data such as seismic data, well logging data and geological data, and use nonlinear fusion algorithms to achieve fusion of data from different sources and scales in high-dimensional space, thereby overcoming the limitations of conventional methods in terms of the singleness of data source and insufficient nonlinear processing capabilities, and improving the prediction accuracy of coal seam gas content. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] Figure 1 A schematic diagram of a scenario of a method for predicting coal seam gas content provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of a flow chart of a method for predicting coal seam gas content provided in an embodiment of the present application;

[0043] Figure 2a A schematic diagram of the relationship between the seismic scale parameters and the coal seam gas content provided in the embodiment of the present application;

[0044] Figure 2b A schematic diagram of the relationship between the well logging scale parameters and the coal seam gas content provided in the embodiment of the present application;

[0045] Figure 2c A schematic diagram of the relationship between burial depth and coal seam gas content provided in the embodiment of the present application;

[0046] Figure 2d A schematic diagram of the relationship between coal seam thickness and coal seam gas content provided in the embodiment of the present application;

[0047] Figure 2e A schematic diagram of the relationship between density and coal seam gas content provided in the embodiment of the present application;

[0048] Figure 2f A schematic diagram of the relationship between the thickness of the coal seam roof sandstone and the gas content of the coal seam provided in the embodiment of the present application;

[0049] Figure 2g A schematic diagram of the relationship between the inclination angle and the gas content of the coal seam provided in the embodiment of the present application;

[0050] Figure 3a A schematic diagram of the relative error of the predicted value on a single well provided in an embodiment of the present application;

[0051] Figure 3bA comparison chart of the predicted results and actual results of coal seam gas content in a single well provided in the embodiment of the present application;

[0052] Figure 3c A schematic diagram of prediction results on a single well when different training wells and test wells are randomly selected according to an embodiment of the present application;

[0053] Figure 3d A plane distribution diagram of the predicted coal seam gas content provided in the embodiment of the present application;

[0054] Figure 4 A schematic diagram of the structure of a coal seam gas content prediction device provided in an embodiment of the present application;

[0055] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present application.

[0056] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0058] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail. In recent years, with the continuous growth of global energy demand, oil and gas resources have occupied an important position in the global energy structure. For a long time, conventional oil and gas exploration and development technology has been relatively mature, and underground oil and gas resources have been effectively developed through means such as seismic exploration, drilling technology and logging technology. However, the depletion of conventional oil and gas resources and the increase in global demand for clean energy have gradually made the exploration and development of unconventional oil and gas resources and deep oil and gas resources a hot topic. Unconventional oil and gas resources include shale gas, tight sandstone gas and coalbed methane, which are widely distributed and rich in reserves, and are becoming an important direction for future energy development. Among unconventional oil and gas resources, coalbed methane, as a natural gas mainly distributed in coal seams, has received widespread attention due to its high methane content and rich resources. With the promotion of renewable energy and the gradual reduction of conventional oil and gas resources, coalbed methane, as a clean energy, has become increasingly important. Therefore, accurate assessment of the gas content of coal seams is of great practical significance for drilling deployment, effective development and utilization of this resource, promoting energy transformation, and reducing environmental pollution. Coalbed methane is mainly formed by coal materials during coalification through biogenesis or thermal genesis and stored in coal seams. Its occurrence mechanism is affected by many factors, including the geological structure, burial depth and coal quality characteristics of the coal seams. Due to the complexity and diversity of the formation and storage process of coalbed methane, its occurrence state is often highly nonlinear and uncertain. Specifically, the porosity, thickness, burial depth of coal seams, and the interaction between coal seams and surrounding rocks will affect the flow and accumulation of coalbed methane. Traditional prediction methods often rely on a single or limited data source, and do not make full use of multi-source and multi-scale data. The prediction of coalbed gas content has great uncertainty. Under different geological conditions, the prediction results of traditional methods have large deviations and are difficult to guarantee accuracy, which restricts the development process and economic effects of coalbed methane. With the widespread application of multi-source and multi-scale data such as seismic, well logging, and geology, the comprehensive use of multi-source and multi-scale data can effectively improve the understanding and evaluation of coalbed methane resources and improve the accuracy of coalbed gas content prediction. For example, seismic data can provide the spatial distribution and geometric characteristics of coal seams, well logging data can reflect the characteristics of coal reservoirs, and geological data can reveal the overall distribution law of coalbed methane. However, in practical applications, there are significant differences in the scale, information characteristics, and noise levels of different data sources. How to effectively fuse these data, eliminate redundant features, and retain key features has become a key issue in coalbed gas content prediction. Most research methods are based on simple data linear weighted fusion strategies and fail to fully explore the complementarity between multi-source and multi-scale data. Although some studies have used statistical models (empirical formulas) to predict coalbed gas content and have achieved certain results in applications in characteristic areas, these methods generally lack in-depth mining of the complex relationships between multi-source and multi-scale data and are difficult to adapt to the complex occurrence environment of coalbed methane under different geological conditions.Although machine learning such as artificial neural networks can process large amounts of data and find potential patterns, most studies predict gas content based on different attributes of seismic data or its mathematical transformation. This is a fusion of multiple seismic attributes rather than a fusion of multi-source and multi-scale data, and is limited by the resolution of seismic data. The prediction accuracy of coal seam gas content is often low. Secondly, some studies use a variety of well logging data that are sensitive to coal seam gas content combined with machine learning to predict coal seam gas content. Although the prediction results have high vertical resolution, they are limited to the prediction of gas content at the well location. The accuracy of mathematical interpolation methods to extend to non-well location areas is limited, especially in areas with strong lateral heterogeneity of coal seam gas content. In addition, the prediction of gas content in thin coal seams is affected by the relationship between lithological combinations and has great uncertainty.

[0059] In order to solve the above technical problems, the inventors thought of improving the prediction accuracy of deep coal seam gas content by integrating multi-source and multi-scale data such as seismic data, well logging data and geological data and machine learning algorithms. Specifically, for multi-source and multi-scale data such as seismic data, well logging data and geological data, artificial intelligence is used to output seismic scale coal seam gas content prediction results, well logging scale coal seam gas content prediction results and geological scale coal seam gas content prediction results. The fusion of data from different sources and scales is realized in high-dimensional space through a nonlinear fusion algorithm, which overcomes the limitations of conventional methods in terms of the singleness of data source and insufficient nonlinear processing capabilities, thereby improving the prediction accuracy of coal seam gas content.

[0060] Based on the above creative findings, the inventor proposed the technical solution of the present application.

[0061] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0062] Figure 1 A schematic diagram of a scenario of a method for predicting coal seam gas content provided in an embodiment of the present application, such as Figure 1 As shown, the specific application scenario of the present application includes: a receiving device 101, a processor 102 and a display device 103.

[0063] It is understandable that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the coal seam gas content prediction method. In other feasible implementations of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0064] In a specific implementation process, the receiving device 101 may be an input / output interface or a communication interface, which is used to obtain different source multi-scale data of the target area and the corresponding relationship between the coal seam gas content and the different source multi-scale data.

[0065] Processor 102 can make multiple intersection diagrams according to the corresponding relationship, and determine the main controlling factor data of the coal seam gas content according to the multiple intersection diagrams; perform data preprocessing on the main controlling factor data to obtain parameter data of the same scale; input the parameter data of the same scale into a pre-trained seismic scale coal seam gas content prediction model to obtain the seismic scale coal seam gas content prediction result of the target area; input the parameter data of the same scale into a pre-trained well logging scale coal seam gas content prediction model to obtain the well logging scale coal seam gas content prediction result of the target area; input the parameter data of the same scale into a pre-trained geological scale coal seam gas content prediction model to obtain the geological scale coal seam gas content prediction result of the target area; use a nonlinear integration method to nonlinearly fuse the seismic scale coal seam gas content prediction result, the well logging scale coal seam gas content prediction result and the geological scale coal seam gas content prediction result to obtain the coal seam gas content prediction value; according to the coal seam gas content prediction value, output the coal seam gas content plane distribution map of the target area.

[0066] The display device 103 can be used to display the plan distribution map of the coal seam gas content in the target area.

[0067] The display device may also be a touch display screen, which is used to receive user instructions while displaying the above content to achieve operational interaction with the user.

[0068] It should be understood that the above-mentioned processor can be implemented by the processor reading instructions in the memory and executing the instructions, or it can be implemented by a chip circuit.

[0069] Figure 2 A schematic diagram of a method for predicting coal seam gas content provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method includes:

[0070] S201: Acquire multi-scale data from different sources in the target area.

[0071] Among them, data are prepared from three aspects: seismic, well logging and geological. Seismic refers to data related to coal seam gas content, including original seismic data and its mathematical transformation results, such as instantaneous frequency, seismic amplitude, wave impedance, attenuation coefficient, inclination and coherence. Well logging refers to rock parameters related to coal seam gas content, including density, natural gamma and porosity. Geologically, it refers to the key controlling factors of coal seam gas content, including thermal evolution degree, sedimentary environment and structural characteristics.

[0072] Specifically, seismic data obtained through active source seismic exploration can reflect the fluctuation information of underground stratum structure and properties. Various parameters such as resistivity, density, and acoustic wave time difference in coal seams are measured through drilling. Based on the theory of address reservoir control, the main factors affecting the occurrence of coalbed methane are analyzed, such as the depositional environment, structural characteristics, and thermal evolution degree of coal seams.

[0073] S202: Obtain the corresponding relationship between the coal seam gas content and the multi-scale data from different sources, and produce a plurality of cross-plots according to the corresponding relationship.

[0074] Specifically, the intersection analysis method is used to select sensitive parameters or main controlling factors that have a certain change pattern with the change of coal seam gas content. For example, in seismic terms, in gas-bearing coal seams, the density is relatively low and the sound wave propagation speed is slow. When seismic waves pass through coal seams with high gas content, strong reflection information will be generated, which is manifested as a strong amplitude feature on the seismic profile. In well logging, when the ash content is high, the pores and cracks of the coal are partially blocked, reducing the adsorption space and migration channels of coalbed methane, which may lead to a decrease in gas content. The ash is mainly composed of inorganic minerals in the coal seam, and the higher the ash content, the higher the natural gamma value. Therefore, natural gamma can be used to indirectly reflect the gas content of coal seams. Geologically, the factors affecting the gas content of coal seams can be analyzed from three aspects: "generation, storage, and capping". For example, a high degree of thermal evolution is conducive to the formation of more coalbed methane; when the roof and floor plates are mudstone deposits, the low permeability of the mudstone plays a sealing role. The thicker the mudstone on the roof, the more conducive it is to sealing the underlying coalbed methane, preventing gas escape and retaining more coalbed methane; when the geological structure is complex and faults are developed, the faults can be both coalbed methane migration channels and side blockages to prevent coalbed methane migration; in addition, the stress near the fault development is more complex, which can easily form cracks in the coal seam and increase the coal seam gas storage space.

[0075] Specifically, the data of different sources and scales obtained from the basic analysis are based on the coal seam gas content calculated by well logging or the gas content of coal seam core sampling, and cross plots are made between the different data and the coal seam gas content.

[0076] S203: Determine the main controlling factor data of the coal seam gas content based on multiple intersection diagrams.

[0077] Specifically, the changing trends of different data with the coal seam gas content are analyzed, and data with obvious changing rules and high correlation are selected as the main controlling factor data of coal seam gas content.

[0078] Specifically, Figure 2a The schematic diagram of the relationship between the seismic scale parameters and the coal seam gas content provided in the embodiment of the present application. Figure 2aAs shown in the figure, the left figure is a schematic diagram of the relationship between seismic amplitude and coal seam gas content, and the right figure is a schematic diagram of the relationship between intercept minus gradient and coal seam gas content. In the seismic data, the absolute value of the seismic amplitude and the absolute value of the intercept minus gradient both increase with the increase of coal seam gas content. Figure 2b The following is a schematic diagram of the relationship between the well logging scale parameters and the coal seam gas content provided in the embodiment of the present application. Figure 2b As shown in the figure, the smaller the natural gamma is, the less ash content and impurities in the coal are, which is conducive to gas adsorption on the coal seam surface. In geology, the factors affecting the gas content of coal seams are analyzed from the three perspectives of "generation, storage and capping". Figure 2c The schematic diagram of the relationship between the buried depth and the gas content of the coal seam provided in the embodiment of the present application is as follows: Figure 2c As shown, within the critical depth, the deeper the coal seam is buried, the higher the gas content of the coal seam is. Figure 2d The schematic diagram of the relationship between coal seam thickness and coal seam gas content provided in the embodiment of the present application is as follows: Figure 2d As shown, thick coal seams generally have larger reservoir volumes and more organic matter that can generate and store coalbed methane, increasing the total gas content of the reservoir. Therefore, the thicker the coal seam, the higher the gas content. Figure 2e The schematic diagram of the relationship between density and coal seam gas content provided in the embodiment of the present application is as follows: Figure 2e As shown, the denser the coal body structure, the higher the gas content, the better the fracture development, and the easier the gas migration and escape. The coal body structure and fracture development can be indirectly reflected by density. The smaller the density, the higher the gas content of the coal seam. Figure 2f A schematic diagram of the relationship between the thickness of the coal seam roof sandstone and the coal seam gas content provided in the embodiment of the present application. Figure 2f As shown in the figure, from the perspective of cap rock gas preservation, the lithology, thickness, continuity and geological structure of the cap rock determine its sealing effect on coalbed methane. A good cap rock can effectively seal coalbed methane and prevent gas escape. The thicker the roof sandstone is, the weaker the sealing ability is and the smaller the gas content of the coal seam is. Figure 2g The schematic diagram of the relationship between the inclination angle and the gas content of the coal seam provided in the embodiment of the present application is as follows: Figure 2g As shown in Figure 1, the inclination angle can indirectly reflect the geological structure of the coal seam, such as faults. Faults can be coal seam gas migration channels, and can also play a side blockage to prevent coal seam gas migration; in addition, the stress near the fault development is more complex, which is easy to form cracks in the coal seam and increase the coal seam gas storage space. Within a certain range, the greater the inclination angle of the coal seam roof, the higher the gas content of the coal seam.

[0079] S204: Preprocess the main control factor data to obtain parameter data with the same scale.

[0080] Among them, the main controlling factor data include seismic data, well logging data and geological data.

[0081] Specifically, the data preprocessing process includes:

[0082] Sa1: Perform mathematical transformation on the seismic data to obtain seismic attributes related to the gas content of the coal seam.

[0083] Among them, mathematical transformations may include spectrum analysis related transformations, wavelet transformations, and attribute transformations.

[0084] Specifically, when seismic waves propagate underground, their frequency components will be affected by the gas content of the coal seam. Different coal seam gas contents will result in different absorption and reflection of seismic waves, causing changes in frequency components. Through spectrum analysis after Fourier transform, the main frequency, bandwidth, etc. can be extracted. Wavelet transform can capture the local frequency and time characteristic changes of seismic waves when passing through coal seams. When changes in coal seam gas content cause changes in the frequency of seismic waves within a specific time period, wavelet transform can accurately locate this time period and analyze frequency changes, thereby obtaining properties related to coal seam gas content. More sensitive properties can be obtained by mathematically transforming the amplitude, and the gas content of coal seams can be inferred by analyzing changes in amplitude-based properties such as reflection coefficients.

[0085] Sa2: For the logging data, a three-dimensional data volume with the same scale as the seismic data is obtained through a high-resolution inversion method.

[0086] When seismic waves propagate underground, their propagation characteristics such as speed, amplitude and phase are closely related to the lithology and physical parameters of the strata. High-resolution inversion utilizes this relationship to infer the detailed parameter distribution of underground strata through known seismic data.

[0087] Specifically, with the help of existing geological, logging and seismic data, an initial model of the underground stratum structure and physical parameter distribution is built. Using the mathematical model of seismic wave propagation, the initial model is forward calculated to simulate the propagation process of seismic waves in the model, and then the theoretical seismic response data is obtained. The seismic response data obtained by the forward simulation is compared with the actual collected seismic data to find out the differences between the two. Based on these differences, the parameters of the initial model are adjusted according to a specific inversion algorithm to make the simulation results closer to the actual seismic data. Since the logging data can accurately reflect the physical parameters of the stratum at the wellbore, it can be used as a calibration point to ensure that the inverted model is consistent with the actual measurement results at the wellbore position, thereby guiding the inversion process in a more accurate direction. Through the high-resolution inversion method, a three-dimensional data volume is finally generated. This data volume is consistent with the seismic data in spatial range and covers the three-dimensional space of the entire study area. At the same time, the high-resolution information of the logging data is incorporated.

[0088] Sa3: For the geological data, the geological attributes having the same scale as the seismic data are obtained through indirect reflection of the seismic attributes and / or the three-dimensional data volume.

[0089] Among them, geological data include coal body structure and fracture data, fault data and discrete factor data.

[0090] Specifically, the geological data is indirectly reflected through the seismic attributes and / or the three-dimensional data volume, including:

[0091] Sb1: indirectly reflecting the coal body structure and fracture data through density values, wherein the density values ​​are determined through the well logging data.

[0092] Sb2: indirectly reflecting the fault data through dip attributes, wherein the dip attributes are determined by the seismic attributes.

[0093] Sb3: Determine the estimated value of each discrete factor data by Kriging interpolation method, where the formula for determining the estimated value of each discrete factor data is:

[0094]

[0095] In the formula, G(x0,y0) represents the estimated value of the point (x0,y0) in the spatiotemporal domain; G(x i ,y j ) are the discrete factor data; k(i,j) are the weight coefficients of different spatial points.

[0096] S205: Inputting parameter data of the same scale into a pre-trained seismic scale coal seam gas content prediction model to obtain a seismic scale coal seam gas content prediction result for the target area.

[0097] Among them, the parameter data includes seismic scale data.

[0098] Specifically, different machine learning algorithms, such as random forest, are preferably used for seismic scale data to adapt to the characteristics of data at different scales to establish a single-scale gas content prediction model, including:

[0099] Sc1: Using different machine learning algorithms on the seismic scale data, respectively construct initial seismic scale coal seam gas content prediction models.

[0100] Sc2: For any machine learning algorithm, according to each training result, the parameters of the seismic-scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the seismic-scale coal seam gas content prediction error is calculated.

[0101] Among them, hyperparameters are parameters that need to be set in advance in machine learning algorithms. They are not learned through training data, but affect the training process and performance of the model. For example, for decision trees, the maximum depth, minimum number of sample splits, etc. are hyperparameters; for neural networks, the number of hidden layers, the number of neurons in each layer, the learning rate, etc. are hyperparameters.

[0102] Specifically, Bayesian optimization establishes a substitute function based on the evaluation result of the objective function and finds the value that minimizes the objective function. The specific process is:

[0103] 1) Initialize a surrogate model prior distribution, and often use Gaussian process as a surrogate function to approximate the target function: f(x)~GP(μ(x),k(x,x ’ )), where μ(x) is the mathematical expectation, k(x,x') is the covariance function, and f(x) is the objective function.

[0104] 2) Select the next hyperparameter combination through the acquisition function, using the expected improvement as the acquisition function:

[0105]

[0106] In the formula, x and x t+1 are the hyperparameter combinations of the current and next samples, respectively, X is the hyperparameter search space, and EI(·) is the expected function.

[0107] 3) Use hyperparameter combination x t+1 Update the proxy model.

[0108] 4) Repeat steps 2) and 3) until the iteration stop condition is reached.

[0109] 5) Returns the optimal hyperparameter combination among the hyperparameter combinations evaluated by the objective function.

[0110] Sc3: Among the different machine learning algorithms, select the machine learning algorithm with the smallest seismic-scale coal seam gas content prediction error to establish a trained seismic-scale coal seam gas content prediction model.

[0111] Specifically, for seismic scale data, the machine learning method with the smallest coal seam gas content prediction error is selected to establish the optimal coal seam gas content prediction model for seismic scale data.

[0112] S206: Inputting parameter data of the same scale into a pre-trained well logging scale coal seam gas content prediction model to obtain a well logging scale coal seam gas content prediction result for the target area.

[0113] Among them, parameter data includes logging scale data.

[0114] Specifically, different machine learning algorithms, such as random forest, are selected for well logging scale data to adapt to the characteristics of data at different scales to establish a single-scale gas content prediction model, including:

[0115] Sd1: Use different machine learning algorithms to construct initial well logging scale coal seam gas content prediction models for the well logging scale data.

[0116] Sd2: For any machine learning algorithm, according to each training result, the parameters of the well logging scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the well logging scale coal seam gas content prediction error is calculated.

[0117] Sd3: Among the different machine learning algorithms, select the machine learning algorithm with the smallest prediction error of the well logging scale coal seam gas content to establish a trained well logging scale coal seam gas content prediction model.

[0118] S207: Inputting parameter data of the same scale into a pre-trained geological scale coal seam gas content prediction model to obtain a geological scale coal seam gas content prediction result for the target area.

[0119] Among them, parameter data includes geological scale data.

[0120] Specifically, different machine learning algorithms, such as random forest, are selected for geological scale data to adapt to the characteristics of data at different scales to establish a single-scale gas content prediction model, including:

[0121] Se1: Use different machine learning algorithms to construct initial geological scale coal seam gas content prediction models for the geological scale data.

[0122] Se2: For any machine learning algorithm, according to each training result, the parameters of the geological scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the geological scale coal seam gas content prediction error is calculated.

[0123] Se3: Among the different machine learning algorithms, select the machine learning algorithm with the smallest geological scale coal seam gas content prediction error to establish a trained geological scale coal seam gas content prediction model.

[0124] S208: A nonlinear integration method is used to nonlinearly fuse the seismic scale coal seam gas content prediction results, the logging scale coal seam gas content prediction results and the geological scale coal seam gas content prediction results to obtain a coal seam gas content prediction value.

[0125] Specifically, the well logging scale coal seam gas content prediction model RF1, the seismic scale coal seam gas content prediction model RF2 and the geological scale coal seam gas content prediction model RF3 were constructed, and the coal seam gas content prediction nonlinear integration multi-scale composite model at multiple scales was integrated. The final output of the model is:

[0126]

[0127] In the formula, Represents the predicted value of gas content in the coal seam; RF (1,2,3) They represent the coal seam gas content prediction model at the seismic scale, the coal seam gas content prediction model at the well logging scale, and the coal seam gas content prediction model at the geological scale respectively; W l (1,2,3) represents the weight of the coal seam gas content prediction model at different scales in the lth layer, b l represents the bias of the lth layer, F l represents the activation function of the lth layer, l = 1, 2, …, L.

[0128] S209: Outputting a planar distribution map of coal seam gas content in the target area according to the predicted value of coal seam gas content.

[0129] Specifically, according to the predicted value of coal seam gas content, first organize it and the corresponding spatial position information of the target area, select drawing libraries and other tools, and set the coordinate system and color mapping according to the actual target area. If the predicted value is a discrete point, you can first draw the point and assign color according to the color mapping, or you can use the interpolation algorithm to expand it to continuous surface data to draw contour maps or fill contour maps, and then add coordinate axis labels, legends, figure names and other annotations, and finally output the plane distribution map of coal seam gas content in the target area in the required format.

[0130] In summary, for multi-source and multi-scale data such as seismic data, well logging data and geological data, artificial intelligence algorithms are used to output seismic-scale coal seam gas content prediction results, well logging-scale coal seam gas content prediction results and geological-scale coal seam gas content prediction results. The nonlinear fusion algorithm is used to realize the fusion of data from different sources and scales in high-dimensional space, which overcomes the limitations of conventional methods in terms of the singleness of data source and insufficient nonlinear processing capabilities, and improves the prediction accuracy of coal seam gas content.

[0131] In another embodiment provided in the present application, in the training of the seismic scale coal seam gas content prediction model, a random forest algorithm is used as an example to establish a model, and the training process includes:

[0132] S301: Generate a plurality of different training subsets by randomly selecting samples with replacement, wherein each training subset includes parameter data in a sample well.

[0133] Specifically, assuming that the seismic scale data of the training input is X, that is, multiple parameter data in the same scale, the corresponding label Y is the gas content of the coal seam, and multiple different training subsets are generated by randomly extracting samples with replacement. The single subset is represented as: D = {(x1, y1), (x2, y2), …, (x N ,y N )},x i =(x i 1 ,x i 2 ,…x i n ). In the formula, x i is the same scale data as the input, y i is the corresponding coal seam gas content, n is the number of data features at this scale, and N is the number of samples in itself.

[0134] S302: For each data subset, a decision tree is constructed, and a plurality of feature data are randomly selected to perform subset data modeling.

[0135] Specifically, a decision tree is constructed for each data subset to train the subset data. K feature data are randomly selected from the data for subset data modeling, where k≤n.

[0136] S303: Selecting an optimal characteristic value from the plurality of characteristic data as a dividing point according to a preset criterion, and dividing the coal seam gas content data.

[0137] Specifically, according to a preset criterion such as minimizing the square error, the eigenvalue of the best feature among the k features is selected as the split point, such as the jth feature x in the subset j and its value s as the segmentation variable and segmentation point, and define two regions R1(j,s)={x|x j ≤s} and R2(j,s)={x|x j ≥s}, the data partition error corresponding to different j and s is:

[0138]

[0139] In the formula, y i The distribution is the gas content value of coal seams in different areas; Represents the region R1(j,s)={x|x j ≤s} the average value of coal seam gas content; The region R2(j,s)={x|x j ≥s}; MSE(j,i) represents traversing the multiple randomly selected feature data, and taking the feature value with the smallest MSE as the optimal feature value.

[0140]

[0141] In the formula, x j represents the jth feature in the subset; s represents the split point; N1 represents the number of samples in the R1 region, and N2 represents the number of samples in the R2 region.

[0142] S304: Continue to split the two data regions obtained by division according to the preset criteria until a preset stop condition is met, thereby completing the establishment of a seismic-scale coal seam gas content prediction model corresponding to the data subset.

[0143] Specifically, the two divided areas are split according to the same criteria until the preset stop condition is met, and the gas content prediction model of a single data subset is established. Common stop conditions include: first, the maximum tree depth is reached; second, the number of samples is less than the minimum number of samples; third, the mean square error or other error measure of the current node is less than the set threshold, etc.

[0144] S305: The seismic scale coal seam gas content prediction models corresponding to all data subsets constitute the seismic scale coal seam gas content prediction model.

[0145] Specifically, the gas content prediction model T for each subset b The calculation formula for the gas content prediction result is given as:

[0146]

[0147] The calculation formula of the predicted value of the seismic scale coal seam gas content prediction model constructed by B subset coal seam gas content prediction models is:

[0148]

[0149] Where, T b represents the coal seam gas content prediction model of each subset; x test represents each training subset; represents the coal seam gas content prediction result given by the coal seam gas content prediction model of each subset; B represents the number of subsets; It represents the prediction result of the seismic scale coal seam gas content prediction model.

[0150] In summary, the random forest algorithm enhances its anti-overfitting ability through random sampling and feature selection and can process high-dimensional data with a large number of features, and performs well when there is a lot of feature redundancy.

[0151] In the embodiment provided in the present application, 4 wells are randomly selected as test wells and 14 wells are randomly selected as training wells from 18 wells in the target area. Random forest is preferably used to construct coal seam gas content prediction models of different scales and Bayesian hyperparameter optimization is used to find the optimal parameter combination of models at different scales and optimize the nonlinear fusion process of models at different scales.

[0152] Figure 3a A schematic diagram of the relative error of the predicted value on a single well provided in the embodiment of the present application is shown in FIG. Figure 3a As shown, the dashed box contains data from 4 test wells. Overall, the prediction error of coal seam gas content in 14 training wells is below 6%, and the prediction error of coal seam gas content in 4 test wells is below 10%.

[0153] Figure 3b The comparison chart of the predicted results and actual results of the coal seam gas content in a single well provided in the embodiment of the present application is as follows: Figure 3b As shown in the figure, the predicted results of coal seam gas content are very close to the actual results, and the absolute error of coal seam gas content prediction is within 1m 3 / t, and overall the predicted trend of gas content change is basically consistent with the actual trend of gas content change, with no major deviation.

[0154] Figure 3c This is a schematic diagram of the prediction results on a single well when different training wells and test wells are randomly selected according to the embodiment of the present application. Figure 3c As shown in the figure, under different random selection conditions, the difference between the predicted results and the actual gas content is small, and the change trends are basically consistent.

[0155] Figure 3d The predicted coal seam gas content plane distribution diagram provided in the embodiment of the present application is as follows: Figure 3d As shown in the figure, the lower and northeastern parts of the target area are the main coalbed methane storage areas, with coalbed gas content up to 9m 3 / t, the gas content of coal seams in the western part of the target area is relatively low, and its gas content is basically 8m 3 / t approximately.

[0156] It can be seen from the above actual data test results that the coal seam gas content prediction method provided in the embodiment of the present application has high accuracy, robustness and generalization in coal seam gas content prediction.

[0157] Figure 4 This is a schematic diagram of the structure of the coal seam gas content prediction device provided in the embodiment of the present application. Figure 4As shown, the device includes: a data acquisition module 401, an intersection diagram drawing module 402, a main control factor data determination module 403, a data preprocessing module 404, a seismic scale coal seam gas content prediction module 405, a logging scale coal seam gas content prediction module 406, a geological scale coal seam gas content prediction module 407, a nonlinear fusion module 408 and an output module 409.

[0158] The data acquisition module 401 is used to acquire multi-scale data from different sources in the target area.

[0159] The cross-plot drawing module 402 is used to obtain the corresponding relationship between the coal seam gas content and the multi-scale data from different sources, and to produce a plurality of cross-plots according to the corresponding relationship.

[0160] The main controlling factor data determination module 403 is used to determine the main controlling factor data of the coal seam gas content according to the multiple intersection diagrams.

[0161] The data preprocessing module 404 is used to perform data preprocessing on the main control factor data to obtain parameter data with the same scale.

[0162] The seismic scale coal seam gas content prediction module 405 is used to input the parameter data of the same scale into the pre-trained seismic scale coal seam gas content prediction model to obtain the seismic scale coal seam gas content prediction result of the target area.

[0163] The well logging scale coal seam gas content prediction module 406 is used to input the parameter data of the same scale into the pre-trained well logging scale coal seam gas content prediction model to obtain the well logging scale coal seam gas content prediction result of the target area.

[0164] The geological scale coal seam gas content prediction module 407 is used to input the parameter data of the same scale into the pre-trained geological scale coal seam gas content prediction model to obtain the geological scale coal seam gas content prediction result of the target area.

[0165] The nonlinear fusion module 408 is used to nonlinearly fuse the seismic scale coal seam gas content prediction result, the logging scale coal seam gas content prediction result and the geological scale coal seam gas content prediction result using a nonlinear integration method to obtain a coal seam gas content prediction value.

[0166] The output module 409 is used to output a planar distribution map of the coal seam gas content in the target area according to the predicted value of the coal seam gas content.

[0167] In one possible implementation, the device also includes a seismic-scale coal seam gas content prediction model training module, which is used to use different machine learning algorithms to respectively construct initial seismic-scale coal seam gas content prediction models for the seismic-scale data; for any machine learning algorithm, according to each training result, the parameters of the seismic-scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the seismic-scale coal seam gas content prediction error is calculated; among the different machine learning algorithms, the machine learning algorithm with the smallest seismic-scale coal seam gas content prediction error is selected to establish a trained seismic-scale coal seam gas content prediction model.

[0168] In one possible implementation, the machine learning algorithm is a random forest algorithm, and the seismic-scale coal seam gas content prediction model training module is specifically used to generate multiple different training subsets by randomly extracting samples with replacement, wherein each training subset includes parameter data in a sample well; for each data subset, a decision tree is constructed, and multiple feature data are randomly selected for subset data modeling; according to a preset criterion, the optimal feature value is selected from the multiple feature data as a split point to divide the coal seam gas content data; the two data regions obtained by the division are continued to be split according to the preset criterion until a preset stop condition is met, thereby completing the establishment of the seismic-scale coal seam gas content prediction model corresponding to the data subset; the seismic-scale coal seam gas content prediction models corresponding to all data subsets constitute the seismic-scale coal seam gas content prediction model.

[0169] In a possible implementation, the formula for dividing the coal seam gas content data in the seismic scale coal seam gas content prediction model is:

[0170]

[0171] In the formula, y i The distribution is the gas content value of coal seams in different areas; Represents the region R1(j,s)={x|x j ≤s} the average value of coal seam gas content; The region R2(j,s)={x|x j ≥s} the average gas content of coal seams; x j represents the jth feature in the subset; s represents the split point; N1 represents the number of samples in the R1 region, and N2 represents the number of samples in the R2 region; MSE(j,i) represents traversing the multiple randomly selected feature data and taking the feature value with the smallest MSE as the optimal feature value.

[0172] In a possible implementation, the calculation formula of the prediction result of the seismic scale coal seam gas content prediction model in the seismic scale coal seam gas content prediction model is:

[0173]

[0174]

[0175] Where, T b represents the coal seam gas content prediction model of each subset; x test represents each training subset; represents the coal seam gas content prediction result given by the coal seam gas content prediction model of each subset; B represents the number of subsets; It represents the prediction result of the seismic scale coal seam gas content prediction model.

[0176] In a possible implementation, the device also includes a well logging scale coal seam gas content prediction model training module, which is used to use different machine learning algorithms to respectively construct initial well logging scale coal seam gas content prediction models for the well logging scale data; for any machine learning algorithm, according to each training result, the parameters of the well logging scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the well logging scale coal seam gas content prediction error is calculated; among the different machine learning algorithms, the machine learning algorithm with the smallest well logging scale coal seam gas content prediction error is selected to establish a trained well logging scale coal seam gas content prediction model.

[0177] In one possible implementation, the device also includes a geological scale coal seam gas content prediction model training module, which is used to use different machine learning algorithms to respectively construct initial geological scale coal seam gas content prediction models for the geological scale data; for any machine learning algorithm, according to each training result, the parameters of the geological scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the geological scale coal seam gas content prediction error is calculated; among the different machine learning algorithms, the machine learning algorithm with the smallest geological scale coal seam gas content prediction error is selected to establish a trained geological scale coal seam gas content prediction model.

[0178] In a possible implementation, the calculation formula of the predicted value of coal seam gas content in the nonlinear fusion module 408 is:

[0179]

[0180] In the formula, Represents the predicted value of gas content in the coal seam; RF (1,2,3) W respectively represent the seismic scale coal seam gas content prediction model, the well logging scale coal seam gas content prediction model and the geological scale coal seam gas content prediction model; l(1,2,3) represents the weight of the coal seam gas content prediction model at different scales in the lth layer, b l represents the bias of the lth layer, F l represents the activation function of the lth layer, l = 1, 2, …, L.

[0181] In one possible implementation, the data preprocessing module 404 is specifically used to perform mathematical transformation on the seismic data to obtain seismic attributes related to the gas content of the coal seam; for the well logging data, a three-dimensional data volume of the same scale as the seismic data is obtained through a high-resolution inversion method; for the geological data, geological attributes of the same scale as the seismic data are obtained through indirect reflection through the seismic attributes and / or the three-dimensional data volume.

[0182] In a possible implementation, the geological data in the data preprocessing module 404 includes coal body structure and fracture data, fault data and discrete factor data; accordingly, the geological data is indirectly reflected through the seismic attributes and / or the three-dimensional data body, including: indirectly reflecting the coal body structure and fracture data through density values, wherein the density values ​​are determined through the logging data; indirectly reflecting the fault data through dip attributes, wherein the dip attributes are determined through the seismic attributes; determining the estimated values ​​of each discrete factor data through the Kriging interpolation method, wherein the determination formula of the estimated values ​​of each discrete factor data is:

[0183]

[0184] In the formula, G(x0,y0) represents the estimated value of the point (x0,y0) in the spatiotemporal domain; G(x i ,y j ) are the discrete factor data; k(i,j) are the weight coefficients of different spatial points.

[0185] The coal seam gas content prediction device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0186] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 5 As shown, the computer device provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the computer device also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0187] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0188] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0189] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0190] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0191] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0192] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0193] An embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0194] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0195] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0196] The division of units is only a logical function division, and there may be other divisions 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. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0197] 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 solution of this embodiment.

[0198] In addition, each functional unit in each embodiment of the present invention 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.

[0199] If the function 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. 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, etc. Various media that can store program codes.

[0200] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0201] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for predicting coal seam gas content, characterized in that: Applicable to computer equipment, including: Obtain multi-scale data from different sources in the target area; Obtaining the corresponding relationship between the coal seam gas content and the multi-scale data from different sources, and making a plurality of cross-plots according to the corresponding relationship; Determining the main control factor data of the coal seam gas content according to the multiple intersection diagrams; Performing data preprocessing on the main control factor data to obtain parameter data of the same scale; Inputting the parameter data of the same scale into a pre-trained seismic scale coal seam gas content prediction model to obtain a seismic scale coal seam gas content prediction result of the target area; Inputting the parameter data of the same scale into a pre-trained well logging scale coal seam gas content prediction model to obtain a well logging scale coal seam gas content prediction result of the target area; Inputting the parameter data of the same scale into a pre-trained geological scale coal seam gas content prediction model to obtain a geological scale coal seam gas content prediction result of the target area; A nonlinear integration method is used to nonlinearly fuse the seismic scale coal seam gas content prediction result, the well logging scale coal seam gas content prediction result and the geological scale coal seam gas content prediction result to obtain a coal seam gas content prediction value; According to the predicted value of coal seam gas content, a plan distribution map of coal seam gas content in the target area is output.

2. The method according to claim 1, characterized in that The parameter data includes seismic scale data; Accordingly, the training process of the seismic scale coal seam gas content prediction model includes: Using different machine learning algorithms on the seismic scale data to respectively construct initial seismic scale coal seam gas content prediction models; For any machine learning algorithm, according to each training result, the parameters of the seismic scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the seismic scale coal seam gas content prediction error is calculated; Among the different machine learning algorithms, the machine learning algorithm with the smallest seismic-scale coal seam gas content prediction error is selected to establish a trained seismic-scale coal seam gas content prediction model.

3. The method according to claim 2, characterized in that The machine learning algorithm is a random forest algorithm; Accordingly, the training process of the seismic scale coal seam gas content prediction model includes: Generating a plurality of different training subsets by randomly sampling samples with replacement, wherein each training subset includes parameter data in the sample well; For each data subset, a decision tree is constructed, and multiple feature data are randomly selected for subset data modeling; Selecting an optimal characteristic value from the plurality of characteristic data as a dividing point according to a preset criterion, and dividing the coal seam gas content data; The two data regions obtained by division are continuously split according to the preset criteria until the preset stop condition is met, thereby completing the establishment of the seismic scale coal seam gas content prediction model corresponding to the data subset; The seismic scale coal seam gas content prediction model corresponding to all data subsets constitutes the seismic scale coal seam gas content prediction model.

4. The method according to claim 3, characterized in that The formula for dividing the coal seam gas content data is: In the formula, y i The distribution is the gas content value of coal seams in different areas; Represents the region R1(j,s)={x|x j ≤s} the average value of coal seam gas content; The region R2(j,s)={x|x j ≥s} the average value of coal seam gas content; x j represents the jth feature in the subset; s represents the split point; N1 represents the number of samples in the R1 region, and N2 represents the number of samples in the R2 region; MSE(j,i) represents traversing the multiple randomly selected feature data and taking the feature value with the smallest MSE as the optimal feature value.

5. The method according to claim 3, characterized in that: The calculation formula of the prediction result of the seismic scale coal seam gas content prediction model is: Where, T b represents the coal seam gas content prediction model of each subset; x test represents each training subset; represents the coal seam gas content prediction result given by the coal seam gas content prediction model of each subset; B represents the number of subsets; It represents the prediction result of the seismic scale coal seam gas content prediction model.

6. The method according to claim 2, characterized in that The parameter data also includes well logging scale data; Accordingly, the training process of the well logging scale coal seam gas content prediction model includes: Using different machine learning algorithms on the well logging scale data to respectively construct initial well logging scale coal seam gas content prediction models; For any machine learning algorithm, according to each training result, the parameters of the well logging scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the well logging scale coal seam gas content prediction error is calculated; Among the different machine learning algorithms, the machine learning algorithm with the smallest logging-scale coal seam gas content prediction error is selected to establish a trained logging-scale coal seam gas content prediction model.

7. The method according to claim 2, characterized in that: The parameter data also includes geological scale data; Accordingly, the training process of the geological scale coal seam gas content prediction model includes: Using different machine learning algorithms on the geological scale data to respectively construct initial geological scale coal seam gas content prediction models; For any machine learning algorithm, according to each training result, the parameters of the geological scale coal seam gas content prediction model are optimized using the Bayesian hyperparameter optimization method, and the geological scale coal seam gas content prediction error is calculated; Among the different machine learning algorithms, the machine learning algorithm with the smallest geological scale coal seam gas content prediction error is selected to establish a trained geological scale coal seam gas content prediction model.

8. The method according to claim 1, characterized in that The calculation formula for the predicted value of coal seam gas content is: In the formula, Represents the predicted value of gas content in the coal seam; RF (1,2,3) W respectively represent the seismic scale coal seam gas content prediction model, the well logging scale coal seam gas content prediction model and the geological scale coal seam gas content prediction model; l (1,2,3) represents the weight of the coal seam gas content prediction model at different scales in the lth layer, b l represents the bias of the lth layer, F l represents the activation function of the lth layer, l = 1, 2, …, L.

9. The method according to claim 1, characterized in that: The main control factor data include seismic data, well logging data and geological data; Accordingly, the data preprocessing of the main control factor data includes: Performing mathematical transformation on the seismic data to obtain seismic attributes related to the gas content of the coal seam; For the well logging data, a three-dimensional data volume having the same scale as the seismic data is obtained by a high-resolution inversion method; For the geological data, geological attributes having the same scale as the seismic data are obtained through indirect reflection of the seismic attributes and / or the three-dimensional data volume.

10. The method according to claim 9, characterized in that The geological data include coal body structure and fracture data, fault data and discrete factor data; Accordingly, the indirect reflection of the geological data through the seismic attributes and / or the three-dimensional data volume includes: indirectly reflecting the coal body structure and fracture data through density values, wherein the density values ​​are determined through the well logging data; indirectly reflecting the fault data through a dip attribute, wherein the dip attribute is determined by the seismic attribute; The estimated value of each discrete factor data is determined by the Kriging interpolation method, wherein the formula for determining the estimated value of each discrete factor data is: In the formula, G(x0,y0) represents the estimated value of the point (x0,y0) in the spatiotemporal domain; G(x i ,y j ) is the discrete factor data; k(i,j) is the weight coefficient of different spatial points.

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