Coal bed gas evaluation method and device, electronic equipment and storage medium

Abstract

The invention discloses a coal bed gas evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of seismic exploration, and the coal bed gas evaluation method comprises the steps: obtaining geological data and main control factors affecting coal bed gas enrichment, determining each weight coefficient of the seismic single prediction result corresponding to each main control factor in a preset evaluation model based on the preset evaluation model, calculating a seismic comprehensive prediction result based on each weight coefficient and each main control factor, and generating first map information based on the seismic comprehensive prediction result, image fusion is carried out on the first map information and second map information reflecting geological laws in geological data to evaluate the target coal bed gas, and the phase control constraint effect based on non-seismic maps with high geological laws is comprehensively considered through the constraint of graph fusion participating in comprehensive evaluation, so that an earthquake prediction result better conforms to the geological laws, and the prediction accuracy is improved. And the regularity and reliability of coal bed gas prediction distribution are improved.

Description

Technical Field

[0001] The present application relates to the field of seismic exploration technology, and in particular to a coalbed methane evaluation method, device, electronic equipment and storage medium. Background Art

[0002] At present, under the general trend of green and low-carbon, international oil and gas energy supply, and environmental protection, coalbed methane, as one of the clean energy sources, has gradually attracted attention for its exploration and development. However, coalbed methane belongs to the unconventional category in the oil and gas field. It is an unconventional natural gas and a highly adsorbable gas. It is basically adsorbed at extremely low abundance, or partially free in the internal cracks and joints of coal seams. This type of reservoir is different from conventional oil and gas reservoirs and has many unique characteristics. Therefore, it is quite different from conventional oil and gas exploration methods in terms of scale and difficulty. The amount of seismic geological information required for the prediction and evaluation of coalbed methane is much greater than that of conventional oil and gas. In particular, the amount of information required for the prediction and evaluation of coalbed methane sweet spots is greater, and the difficulty and challenge are greater. The seismic exploration and development technology for coalbed methane is still in its early stages, and there is an urgent need for lateral prediction and internal structure description technology for coal seams, especially the distribution prediction and evaluation of coalbed methane sweet spots, which is crucial to the effective development of coalbed methane.

[0003] However, among the above technologies, with the development of research in the field of coalbed methane, the neural network method has gradually been applied to the comprehensive evaluation of coalbed methane. However, due to the particularity of coalbed methane and its small identification scale, seismic exploration evaluation is more difficult than conventional oil and gas, with stronger multi-solution and more influencing factors. In addition, during coalbed methane exploration, due to cost reasons, the degree of seismic exploration is often low, and two-dimensional seismic, large-grid non-seismic or a small amount of three-dimensional seismic are deployed. The interpolation law of large-grid data is poor, and it cannot even reflect the geological law well.

[0004] Therefore, how to improve the reliability of coalbed methane evaluation and the regularity of coalbed methane prediction distribution trend is a problem that needs to be solved. Summary of the invention

[0005] In view of the above problems, the present application provides a coalbed methane evaluation method, device, electronic device and storage medium to at least solve the problems existing in the related technology.

[0006] In a first aspect, an embodiment of the present application provides a coalbed methane evaluation method, comprising:

[0007] Obtain geological data and the main controlling factors affecting coalbed methane enrichment;

[0008] Determine, based on a preset evaluation model, each of the weight coefficients of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model;

[0009] Calculate the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors;

[0010] First map information generated based on the comprehensive earthquake prediction results;

[0011] The first map information is image-fused with the second map information reflecting geological laws in the geological data to evaluate target coalbed methane.

[0012] In some embodiments, the preset evaluation model is established by normalizing the main control factors based on a comprehensive evaluation method, wherein the comprehensive evaluation method includes: a neural network method, a hierarchical analysis method, and an expert experience method.

[0013] In some embodiments, the calculating of the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors includes:

[0014] Calculate the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors using linear fusion information;

[0015] Based on each of the weight coefficients and each of the main control factors, nonlinear fusion information is used to calculate the comprehensive earthquake prediction results.

[0016] In some embodiments, obtaining the main controlling factors affecting coalbed methane enrichment includes:

[0017] Obtain coal seam geological characteristics and rock physical properties in the target area;

[0018] The main controlling factors affecting coalbed methane enrichment are determined based on the coal seam geological characteristics and the rock physical properties.

[0019] In some embodiments, the first map information generated based on the earthquake comprehensive prediction result includes:

[0020] Performing data gridding processing on the earthquake comprehensive prediction results to generate first grid data;

[0021] Performing data regularization processing on the first grid data to generate first image information;

[0022] The first image information is decomposed based on wavelet transformation to generate first image information.

[0023] In some embodiments, the image fusion of the first map information with the second map information reflecting the geological law in the geological data includes:

[0024] Perform image fusion based on a first pixel point of the first map information and a second pixel point of the second map information, or perform window image fusion based on the first map information and the second map information.

[0025] In some embodiments, the main controlling factors include: coal seam thickness, coal quality, permeability, structural characteristics, burial depth and coal seam cap rock lithology.

[0026] In a second aspect, an embodiment of the present application provides a coalbed methane evaluation device, comprising:

[0027] Acquisition module, used to obtain geological data and main controlling factors affecting coalbed methane enrichment;

[0028] A determination module, for determining, based on a preset evaluation model, each of the weight coefficients of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model;

[0029] A calculation module, used for calculating the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors;

[0030] A generating module, used for generating first map information based on the earthquake comprehensive prediction result;

[0031] The evaluation module is used to perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, comprising: at least one processor and a memory; the processor is used to execute a computer program stored in the memory to implement a coalbed methane evaluation method as described in any implementation method of the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing one or more programs, which can be executed by an electronic device as described in the third aspect to implement a coalbed methane evaluation method as described in any implementation method of the first aspect.

[0034] The embodiments of the present application provide a coalbed methane evaluation method, device, electronic device and storage medium, including: acquiring geological data and main controlling factors affecting coalbed methane enrichment, determining the weight coefficients of the seismic single prediction results corresponding to each main controlling factor in the preset evaluation model based on a preset evaluation model, calculating the seismic comprehensive prediction results based on the weight coefficients and each main controlling factor, and based on the first map information generated by the seismic comprehensive prediction results, performing image fusion on the first map information and the second map information reflecting the geological laws in the geological data to evaluate the target coalbed methane, and comprehensively considering the phase-controlled constraints of non-seismic maps with strong geological regularity through the constraints participating in the comprehensive evaluation through graphic fusion, so that the seismic prediction results are more in line with the geological laws, and the regularity and reliability of the coalbed methane prediction distribution are improved.

[0035] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A schematic diagram of a coalbed methane evaluation method proposed in an embodiment of the present application is shown;

[0038] Figure 2 A schematic diagram of a coalbed methane geophysical comprehensive evaluation parameter system proposed in an embodiment of the present application is shown;

[0039] Figure 3 A schematic diagram of an image fusion principle proposed in an embodiment of the present application is shown;

[0040] Figure 4 A schematic diagram of a comprehensive evaluation of a coalbed methane enrichment area in a coal seam in a target area proposed in an embodiment of the present application is shown;

[0041] Figure 5 A structural block diagram of a coalbed methane evaluation device proposed in an embodiment of the present application is shown;

[0042] Figure 6 A structural block diagram of an electronic device for executing a coalbed methane evaluation method according to an embodiment of the present application is shown;

[0043] Figure 7A computer-readable storage medium for storing or carrying a coalbed methane evaluation method according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] At present, under the general trend of green and low-carbon, international oil and gas energy supply, and environmental protection, coalbed methane, as one of the clean energy sources, has gradually attracted attention for its exploration and development. However, coalbed methane belongs to the unconventional category in the field of oil and gas. It is an unconventional natural gas and a gas with strong adsorption. It is basically adsorbed at extremely low abundance, or partially free in the internal cracks and joints of coal seams. This type of reservoir is different from conventional oil and gas reservoirs and has many unique characteristics. Therefore, it is quite different from conventional oil and gas exploration methods in terms of scale and difficulty. The amount of seismic geological information required for the prediction and evaluation of coalbed methane is much greater than that of conventional oil and gas. In particular, the amount of information required for the prediction and evaluation of coalbed methane sweet spots is greater, and the difficulty and challenge are greater. It is precisely because of the above reasons that the pace of coalbed methane exploration and development has been severely restricted. The seismic exploration and development technology for coalbed methane is still in its early stages, and there is an urgent need for lateral prediction and internal structure description technology for coal seams, especially the distribution prediction and evaluation of coalbed methane sweet spots, which is crucial to the effective development of coalbed methane.

[0047] Faced with such a complex coalbed methane prediction problem, the multi-solution of geophysical technology application is more prominent. For example, it is impossible to use a single seismic strong reflection attribute to accurately predict river sand bodies or bright spot attributes to accurately identify oil and gas in coalbed methane prediction. Single seismic analysis methods and results often have strong multi-solutions. Therefore, when predicting such a complex coalbed methane object, especially the prediction, evaluation and optimization of coalbed methane sweet spots, the first thing to consider is the prediction of multiple main controlling factors of coalbed methane, and secondly, the multi-solution of geophysical prediction of a single main controlling factor. The method to reduce multi-solutions is the dimensionality reduction method of information fusion. The so-called information fusion is to analyze multiple results elements, give appropriate weights to the results information that a single factor can better reflect the actual or is considered to be more in line with geological laws, and then integrate these information with different weights into a comprehensive result in a new dimension or dimensionless form, and finally evaluate them in a graded manner.

[0048] The inventors have found that with the development of the coalbed methane field, seismic exploration results are gradually applied to the comprehensive evaluation of coalbed methane. The comprehensive evaluation of information fusion driven by data results can be carried out through the above-mentioned evaluation method according to specific conditions and requirements. However, due to the particularity of coalbed methane and its small identification scale, the difficulty of seismic exploration evaluation is more difficult than conventional oil and gas, with stronger multi-solution and more influencing factors. In the process of coalbed methane exploration, due to cost reasons, the degree of seismic exploration is often low, and two-dimensional seismic, large-grid non-seismic or a small amount of three-dimensional seismic are deployed. The interpolation law of large-grid data is poor, and it cannot even reflect the geological law well. Some images (filling maps) with geological significance are of great significance in the comprehensive evaluation. For example, some geological maps are often formed by geologists based on the regional geology, geological outcrops and specific results of the study area. They have important results that can reflect the macroscopic understanding of sedimentation laws, etc., which can guide or even be directly used for the regular constraint conditions of comprehensive evaluation, that is, phase control. This type of map is generally based on actual statistics, and the geological results are obtained by integrating the experience of geologists. It is impossible to achieve it by using computer mapping based on various interpolation methods using statistical scattered data.

[0049] Therefore, the inventors consider that coalbed methane is an unconventional natural gas, and coalbed methane enrichment is controlled by multiple factors. The burial depth of coal seams, top and bottom plate lithology, coal quality characteristics, reservoir pressure, coal seam thickness, coal reservoir permeability, etc. all have a great impact on the formation of coalbed methane reservoirs and coalbed methane enrichment characteristics. The reliability of using a single gas content prediction method to predict and evaluate the gas content of coalbed methane reservoirs under multiple conditions is low, and seismic technology also has multiple solutions for the prediction of a single factor. Therefore, in addition to the need for dimensionality reduction and information fusion of single elements for seismic prediction, the multi-element prediction results based on the main control factors also need to be evaluated through information fusion. Due to the difficulty of coalbed methane seismic prediction and the low degree of seismic exploration, the multiple solutions of seismic prediction are manifested in the weak geological laws in the distribution trend, and the phase control of geological constraints is required to make it more in line with geological laws. At this time, large grids and large-scale geological maps or non-seismic results maps can become the conditions for such phase control.

[0050] In response to the above problems, the applicant proposed a coalbed methane evaluation method, device, electronic device and storage medium provided in the embodiments of the present application, which obtains geological data and the main controlling factors affecting coalbed methane enrichment, determines the weight coefficients of the earthquake single prediction results corresponding to each main controlling factor in the preset evaluation model based on the preset evaluation model, calculates the earthquake comprehensive prediction results based on each weight coefficient and each main controlling factor, and fuses the first map information with the second map information reflecting the geological law in the geological data based on the first map information generated by the earthquake comprehensive prediction results to evaluate the target coalbed methane, and adopts the idea of ​​fusing (earthquake prediction) data and image (geological, non-seismic, etc.) information to realize the comprehensive evaluation and optimization of coalbed methane, so that the prediction and comprehensive evaluation optimization are more in line with the geological law. Among them, the coalbed methane evaluation method is described in detail in the subsequent embodiments.

[0051] Embodiment 1

[0052] The following is an introduction to the application scenarios of the coalbed methane evaluation method provided in the embodiment of the present application:

[0053] See also Figure 1 , Figure 1 The present invention provides a flow chart of a coalbed methane evaluation method in an embodiment of the present invention, so as to realize the prediction and comprehensive evaluation of coalbed methane in the target area and the exploration and development, thereby improving the regularity and reliability of coalbed methane prediction and distribution. In this embodiment, the coalbed methane evaluation method can be applied to Figure 5 The coalbed methane evaluation device 300 shown in FIG. Figure 6In the electronic device 200 shown, the electronic device 200 can be a smart terminal such as a desktop computer, a tablet computer, a smart phone, etc., wherein the electronic device 200 can include one or more, and information can be transmitted between multiple electronic devices via wireless and / or wired methods. Multiple electronic devices can collaborate to complete the coalbed methane evaluation method. By way of example, various types of data can be obtained through the smart terminal to complete the coalbed methane evaluation method process.

[0054] The following is for Figure 1 The process shown is described in detail, and the coalbed methane evaluation method may include steps S110 to S150.

[0055] Step S110: Acquire geological data and main controlling factors affecting coalbed methane enrichment.

[0056] In the embodiment of the present application, geological data may include: geological data and maps such as coal seam-related sedimentation and non-seismic data, which may participate in the constraints of seismic results to achieve phase control. The main controlling factors affecting coalbed methane enrichment may include: structure, coal quality, coal seam physical properties, capping conditions, coal seam morphology and hydrological conditions, among which, reference may be made to Figure 2 , Figure 2 This is a schematic diagram of a coalbed methane geophysical comprehensive evaluation parameter system in an embodiment of the present application. The coalbed methane comprehensive evaluation system in the field of geophysical prediction corresponds to it, and mainly includes the seismic geometric attributes of coal seams and coalbed methane, crack detection, seismic inversion and other results.

[0057] Step S120: Determine the weight coefficients of the individual earthquake prediction results corresponding to the main control factors in the preset evaluation model based on the preset evaluation model.

[0058] In the embodiment of the present application, taking into account that coalbed methane enrichment is mainly controlled by multiple factors such as coal seam thickness, coal quality, permeability, structural characteristics, burial depth, coal seam cap lithology, etc., a comprehensive evaluation is performed according to the corresponding seismic comprehensive evaluation parameter results and geological and non-seismic results, that is, different evaluation elements are evaluated with different weight coefficients.

[0059] Step S130: Calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors.

[0060] In the embodiment of the present application, a comprehensive prediction result after calculation of all different evaluation elements and different weight coefficients can be obtained through normalization processing.

[0061] Step S140: generating first map information based on the comprehensive earthquake prediction results.

[0062] In the embodiment of the present application, the corresponding data in the comprehensive earthquake prediction results are converted into the first map information for presentation.

[0063] Step S150: Perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0064] In the embodiment of the present application, by determining the weight coefficients corresponding to each main controlling factor and calculating the comprehensive earthquake prediction results based on the single earthquake prediction results, it can help identify and evaluate the importance of the main controlling factors to coalbed methane enrichment and their contribution to earthquake prediction. By acquiring geological data and performing image fusion, combining the first map information and the second map information reflecting the geological laws, the geological laws of coalbed methane enrichment can be analyzed and evaluated, which helps to understand the impact of geological conditions on coalbed methane distribution and enrichment, and determine the importance of geological characteristics and structural conditions to coalbed methane exploration, thereby providing a scientific basis for subsequent exploration and development work. The idea of ​​​​fusion of (earthquake prediction) data and image (geological, non-seismic, etc.) information is adopted to achieve comprehensive evaluation and optimization of coalbed methane.

[0065] In some embodiments, the coalbed methane evaluation method may further include step S210.

[0066] Step S110: Acquire geological data and main controlling factors affecting coalbed methane enrichment.

[0067] Step S210: Based on the comprehensive evaluation method, the main control factors are normalized to establish a preset evaluation model, wherein the comprehensive evaluation method includes: neural network method, hierarchical analysis method and expert experience method.

[0068] In the embodiment of the present application, the comprehensive evaluation method combines the neural network method, the hierarchical analysis method and the expert experience method, and can fully consider the relationship and weight between multiple evaluation indicators and multiple main control factors. Through normalization and comprehensive evaluation methods, the influence of different factors can be comprehensively considered, and the comprehensive evaluation results of the main control factors can be obtained, which is helpful to reduce the subjectivity and limitations of a single evaluation method and improve the objectivity and accuracy of the evaluation results. The main control factors of different scales and magnitudes can be unified by using the normalization method, and the dimension differences between the factors can be eliminated. Each main control factor can have the same measurement unit in the comprehensive evaluation model, which is convenient for weight calculation and comprehensive analysis, so that different factors can participate in the comprehensive evaluation fairly. The hierarchical analysis method can analyze the importance and weight of different indicators through the hierarchy and judgment matrix, and the expert experience method can introduce the knowledge and experience of experts in the field, and provide subjective weights and factors and factor evaluation. In this way, the advantages of multiple methods can be comprehensively utilized to obtain a more comprehensive and accurate comprehensive evaluation result.

[0069] Step S120: Determine the weight coefficients of the individual earthquake prediction results corresponding to the main control factors in the preset evaluation model based on the preset evaluation model.

[0070] Step S130: Calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors.

[0071] Step S140: generating first map information based on the comprehensive earthquake prediction results.

[0072] Step S150: Perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0073] In some embodiments, calculating the comprehensive earthquake prediction results based on various weight coefficients and various main controlling factors in step S130 may include steps S132 to S134.

[0074] Step S110: Acquire geological data and main controlling factors affecting coalbed methane enrichment.

[0075] Step S210: Based on the comprehensive evaluation method, the main control factors are normalized to establish a preset evaluation model, wherein the comprehensive evaluation method includes: neural network method, hierarchical analysis method and expert experience method.

[0076] Step S120: Determine the weight coefficients of the individual earthquake prediction results corresponding to the main control factors in the preset evaluation model based on the preset evaluation model.

[0077] Step S132: Calculate the comprehensive earthquake prediction results using linear fusion information based on various weight coefficients and various main control factors.

[0078] In the embodiment of the present application, linear fusion information is applicable to the situation where there is a linear relationship between the main controlling factors, while nonlinear fusion information is applicable to the situation where there is a complex nonlinear relationship between the main controlling factors. By selecting a suitable fusion method, the interaction and comprehensive effects between the main controlling factors can be better reflected, thereby improving the accuracy and reliability of the comprehensive earthquake prediction results.

[0079] Step S134: Calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors using nonlinear fusion information.

[0080] In the embodiment of the present application, by comprehensively considering the weights and influences of different main controlling factors, linear and nonlinear fusion information can improve the accuracy and reliability of earthquake comprehensive prediction results. Through the calculation of weight coefficients and comprehensive analysis of main controlling factors, the subjective influence and uncertainty of individual factors can be reduced, and the credibility and accuracy of the prediction results can be improved.

[0081] Step S140: generating first map information based on the comprehensive earthquake prediction results.

[0082] Step S150: Perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0083] In some embodiments, obtaining the main controlling factors affecting coalbed methane enrichment in step S110 may include steps S112 to S114.

[0084] Step S112: Acquire the coal seam geological characteristics and rock physical properties of the target area.

[0085] In the embodiments of the present application, by analyzing the geological characteristics and rock physical properties of the coal seams in the target area, the distribution, scale and potential of coalbed methane resources can be understood, and the main controlling factors affecting coalbed methane enrichment can be determined.

[0086] Step S114: Determine the main controlling factors affecting coalbed methane enrichment based on the coal seam geological characteristics and rock physical properties.

[0087] In the embodiments of the present application, by determining the main controlling factors affecting coalbed methane enrichment, it can help determine the key areas and target areas for coalbed methane exploration. The analysis of the main controlling factors can reveal the formation mechanism and enrichment rules of coalbed methane, and can provide a scientific basis for coalbed methane exploration and development, improve exploration and development efficiency, and reduce development risks.

[0088] Step S120: Determine the weight coefficients of the individual earthquake prediction results corresponding to the main control factors in the preset evaluation model based on the preset evaluation model.

[0089] Step S130: Calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors.

[0090] Step S140: generating first map information based on the comprehensive earthquake prediction results.

[0091] Step S150: Perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0092] In some embodiments, in step S140, the first map information generated based on the comprehensive earthquake prediction results may include steps S142 to S146.

[0093] Step S110: Acquire geological data and main controlling factors affecting coalbed methane enrichment.

[0094] Step S210: Based on the comprehensive evaluation method, the main control factors are normalized to establish a preset evaluation model, wherein the comprehensive evaluation method includes: neural network method, hierarchical analysis method and expert experience method.

[0095] Step S120: Determine the weight coefficients of the individual earthquake prediction results corresponding to the main control factors in the preset evaluation model based on the preset evaluation model.

[0096] Step S130: Calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors.

[0097] Step S142: gridding the comprehensive earthquake prediction results to generate first grid data.

[0098] In the embodiment of the present application, the earthquake comprehensive prediction results are processed into data grids to generate the first grid data, which is to present the earthquake prediction results in a regular grid form, which is convenient for subsequent processing and analysis. Through data grid processing, the earthquake prediction results can be divided into different grid units, each unit representing a specific earthquake attribute or feature.

[0099] Step S144: performing data regularization processing on the first grid data to generate first image information.

[0100] In the embodiment of the present application, the first grid data is subjected to data regularization processing to generate the first image information, which serves to convert the grid data into an image form, making it more intuitive and easy to understand. Data regularization processing can adopt different methods, such as color mapping, contour line drawing, etc., to convert the value of each grid unit into a corresponding image pixel or color, thereby forming an image that reflects the spatial distribution and trend of earthquake prediction results.

[0101] Step S146: Decompose the first image information based on wavelet transform to generate first image information.

[0102] In an embodiment of the present application, the first image information is decomposed based on the wavelet transform to generate the first map information, which can further analyze and extract the characteristics and details of the earthquake prediction results, and can decompose the signal or image into components of different scales and frequencies, thereby revealing the local characteristics of the signal or image. By performing wavelet transform decomposition on the first image information, earthquake characteristics of different scales and frequencies can be obtained, and the spatial structure, time domain characteristics, etc. of the earthquake prediction results can be further understood.

[0103] In this embodiment, by subjecting the comprehensive earthquake prediction results to data gridding, data regularization and wavelet transform-based decomposition processing, the earthquake prediction results can be visualized, feature extracted and analyzed, and the input image information can be gridded to regenerate regularized image information in this application, and the image can be further decomposed into pixel-based data points using wavelet transform, which are then further fused and inversely transformed to form a new image.

[0104] Step S150: Perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0105] In some embodiments, in step S150, image fusion of the first map information with the second map information reflecting geological laws in the geological data may include step S152.

[0106] Step S110: Acquire geological data and main controlling factors affecting coalbed methane enrichment.

[0107] Step S120: Determine the weight coefficients of the individual earthquake prediction results corresponding to the main control factors in the preset evaluation model based on the preset evaluation model.

[0108] Step S130: Calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors.

[0109] Step S140: generating first map information based on the comprehensive earthquake prediction results.

[0110] Step S152: performing image fusion based on the first pixel point of the first image information and the second pixel point of the second image information, or performing window image fusion based on the first image information and the second image information.

[0111] In the embodiments of the present application, in some aspects, the purpose of image fusion based on the first pixel point of the first image information and the second pixel point of the second image information is to fuse the pixel points of the two images to obtain more comprehensive and accurate information. By fusing the pixel points of the two images, their features and details can be combined to obtain a new image with more information and higher resolution, which can be used to enhance the contrast, clarity and details of the image and provide better visualization effects.

[0112] In other aspects, the purpose of performing window image fusion based on the first image information and the second image information is to define a window and fuse the two images in the window to obtain a fused image. Window image fusion can be achieved through different algorithms and weight allocation strategies, such as weighted average, maximum value, minimum value, etc. The information of the two images can be interacted and integrated in the window, providing richer and more comprehensive visual information, helping to reduce noise and artifacts in the image, and improving image quality and visualization.

[0113] In the embodiment of the present application, by comprehensively considering the phase-controlled constraints of large-scale non-seismic maps with strong geological regularity, the input image information is gridded to regenerate regularized image information, and the image is further decomposed into pixel-based data points using wavelet transform, which are then further fused and inversely transformed to form a new image. The graphic fusion technology of wavelet transform is used to realize the comprehensive evaluation of coalbed methane with multiple achievement elements driven by graphics and seismic data, thereby improving the regularity and reliability of coalbed methane prediction distribution.

[0114] In summary, the present embodiment provides a coalbed methane evaluation method, which obtains geological data and main controlling factors affecting coalbed methane enrichment, determines the weight coefficients of the seismic single prediction results corresponding to each main controlling factor in the preset evaluation model based on the preset evaluation model, calculates the comprehensive seismic prediction results based on the weight coefficients and each main controlling factor, and performs image fusion on the first map information generated by the comprehensive seismic prediction results with the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane, and makes the seismic prediction results more in line with the geological law through the constraints of the graphic fusion participating in the comprehensive evaluation, comprehensively considers the phase-controlled constraint effect of the large-scale non-seismic map with strong geological regularity, and realizes the comprehensive evaluation of coalbed methane of multiple result elements driven by graphics and seismic data through the graphic fusion technology of wavelet transform. The regularity and reliability of coalbed methane prediction distribution are improved.

[0115] Embodiment 2

[0116] On the basis of Example 1, the following is a study on a specific test target area, using a coalbed methane comprehensive evaluation technology based on the fusion of data and image information. During the research process, effective earthquake prediction results related to the main controlling factors such as coal quality, coal seams, coal reservoir porosity, structure, burial depth, caprock conditions, etc. that are rich in coalbed methane are screened, and the structure, amplitude attributes, thickness, absorption attenuation, coal seam fractures, etc. are selected. After obtaining the relevant weight values ​​through the hierarchical analysis method, a comprehensive evaluation of earthquake prediction is performed, wherein the actual application process of the coalbed methane evaluation method can include the following steps:

[0117] Step S1: Optimization of factors for comprehensive evaluation of coalbed methane:

[0118] In this step, the main controlling factors of coalbed methane enrichment and accumulation are structure, coal quality, coalbed physical properties, capping conditions, coalbed morphology and hydrological conditions. In view of the above aspects, the comprehensive evaluation system of coalbed methane in the field of geophysical prediction corresponds to it, mainly including the seismic geometric attributes of coal seams and coalbed methane, fracture detection and seismic inversion, etc. Figure 2 In addition, some geological and non-seismic data and maps such as coal-related sediments can be used to constrain seismic results, that is, phase control.

[0119] Step S2: Comprehensive evaluation of coalbed methane using data and graphic information fusion technology:

[0120] In this step, as described in step S1, coalbed methane enrichment is mainly controlled by multiple factors such as coal seam thickness, coal quality, permeability, structural characteristics, burial depth, coal seam cap lithology, etc. It can be comprehensively evaluated according to the corresponding seismic comprehensive evaluation parameter results and geological and non-seismic results.

[0121] Among them, data-driven seismic results can obtain the weights of each element according to the neural network method, hierarchical analysis method or expert experience method, and carry out rating through data information fusion. The rating results are then combined with the trend geological and non-seismic results maps of the large grid through image information fusion to perform phase-controlled constraints on the seismic rating, complete the final comprehensive evaluation of coalbed methane, and select favorable enrichment sweet spots.

[0122] Exemplarily, the information fusion rating of data-driven earthquake prediction results is as follows:

[0123] Step S21: Obtain the normalized weight coefficient of each evaluation element by a comprehensive evaluation method such as the highest level analysis method and the expert experience method, as shown in Table 1, where a1, a2, a3, ...an<=1.

[0124] Table 1 Weights of evaluation elements

[0125]

[0126] Step S22: Combined with the actual predicted single element result data, calculation based on information fusion technology (linear or nonlinear) is performed according to the weight coefficient. Taking linear as an example: Z=a1x1+a2x2+a3x3+....anxn, so as to obtain the normalized earthquake prediction comprehensive evaluation result Z.

[0127] Step S23: Comprehensive evaluation of coalbed methane based on the fusion of map-type graphics. The results are formed into maps according to the comprehensive evaluation results of earthquake prediction, and the collected geological maps with geological concepts that can reflect geological laws and non-seismic maps (graphics of large grid data) are used to conduct a comprehensive evaluation under the phase control mode of geological constraints using graphic information fusion technology. Figure 3 The wavelet transform algorithm is used to grid the input image information and regenerate regularized image information. The wavelet transform is further used to decompose the image into pixel-based data points, which are then further fused and inversely transformed to form a new image.

[0128] In practical applications, the comprehensive evaluation of earthquake prediction is performed after obtaining the relevant weight values ​​through the hierarchical analysis method as shown in Table 3:

[0129] Table 2 shows the weight coefficients of the main controlling factors of coalbed methane for a specific coal.

[0130]

[0131] Table 2

[0132] According to Table 2, after analysis, the large grid electromagnetic anomaly map of the magnetotelluric in the area was collected and obtained. The geological analysis shows that the anomaly trend is consistent with the well and sedimentary background. After that, the favorable area was further evaluated and selected using the seismic comprehensive evaluation results map and the magnetotelluric results using the graphic information fusion technology based on wavelet transform. Good results were obtained after the later drilling verification.

[0133] See also Figure 4 , Figure 4 In one embodiment of the present application, an exemplary schematic diagram of comprehensive evaluation of coalbed methane enrichment area in a coal seam in a target area is proposed. Figure 4 In the study, combined with the distribution image data of abnormal areas by magnetotelluric resistivity inversion, the coalbed methane enrichment area was further evaluated through image information fusion technology. The final evaluation and optimization results show that the coalbed methane is relatively enriched in the west and east of the study area, followed by the south, relatively poor in the middle, and the worst in the north. The deployment of wells and well networks with good coalbed methane display in the east is all in the relatively rich belt of coalbed methane. The western area has the best evaluation in a large area, which is Class I area. There is no drilling in this area at present, and it has coalbed methane development prospects. The enrichment of coalbed methane is controlled by multiple factors such as coal seam thickness, physical properties, fractures, structure and other cap rock properties. The distribution of the predicted and evaluated enriched areas is uneven, indicating the differences in coalbed methane accumulation conditions and coal seam heterogeneity. The prediction and evaluation results are consistent with the laws and geological characteristics reflected by existing drilling.

[0134] In a specific implementation, the main controlling factors of coalbed methane are determined by analyzing the geological characteristics and rock physical properties of the target coal seam. The present invention mainly uses four main controlling factors, namely coal quality, coal thickness, physical gas content and structural burial depth.

[0135] According to Table 2, the weight coefficients of each single seismic prediction result of the main controlling factors of coalbed methane enrichment in the comprehensive evaluation are determined by the hierarchical analysis method.

[0136] according to Figure 4 As can be seen from the upper left figure, by applying the weight coefficients of the main controlling factors of coalbed methane enrichment, the characterization results of the main controlling factors of seismic prediction are optimized to select the structure, amplitude attributes, thickness, absorption attenuation, coal seam fractures, etc., and through normalization operations, a preliminary comprehensive evaluation map of coalbed methane enrichment seismic prediction for a coal seam in the target area is obtained.

[0137] Combine the preliminary comprehensive evaluation results of earthquake prediction with the collected large-grid magnetotelluric anomaly images (such as the attached Figure 4(Top right) Image information fusion based on wavelet transform, with magnetotelluric results weighted at 0.2. The final evaluation and optimization results are shown in the attached Figure 4 Finally, the evaluation results are divided into different areas according to the distribution of faults.

[0138] In this embodiment, the comprehensive evaluation results map shows that the distribution of coalbed methane enrichment areas is consistent with the actual drilling. Wells with good display and later gas production are all in the more favorable area of ​​Class II of the comprehensive evaluation of coalbed methane, and wells with poor display or poor gas production are in the poor coal evaluation area. Among them, a large area of ​​Class I favorable area was obtained in the western part of the target area, indicating a good prospect for exploration and development.

[0139] Embodiment 3

[0140] See also Figure 5 , Figure 5 A coalbed methane evaluation device 300 provided in the present application includes: an acquisition module 310, a determination module 320, a calculation module 330, a generation module 340 and an evaluation module 350, wherein:

[0141] The acquisition module 310 is used to acquire geological data and main controlling factors affecting coalbed methane enrichment.

[0142] The determination module 320 is used to determine the weight coefficients of the earthquake single prediction results corresponding to each main control factor in the preset evaluation model based on the preset evaluation model.

[0143] The calculation module 330 is used to calculate the comprehensive earthquake prediction results based on various weight coefficients and various main control factors.

[0144] A generating module 340, for generating first map information based on the earthquake comprehensive prediction result;

[0145] The evaluation module 350 is used to perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

[0146] It should be noted that the device embodiments in the present application correspond to each other with the aforementioned method embodiments. The specific principles in the device embodiments can be found in the contents of the aforementioned method embodiments and will not be repeated here.

[0147] In several embodiments provided in this embodiment, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0148] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0149] Embodiment 4

[0150] See also Figure 6 , Figure 6 The present application provides a structural block diagram of an electronic device 200 that can execute the above-mentioned coalbed methane evaluation method. The electronic device 200 can be a computer, a tablet computer, a smart phone, a portable computer or other devices.

[0151] The electronic device 200 further includes a processor 202 and a memory 204. The memory 204 stores a program that can execute the contents of the aforementioned embodiments, and the processor 202 can execute the program stored in the memory 204.

[0152] Among them, the processor 202 may include one or more cores and message matrix units for processing data. The processor 202 uses various interfaces and lines to connect various parts of the entire electronic device 200, and executes various functions and processes data of the electronic device 200 by running or executing instructions, programs, code sets or instruction sets stored in the memory 204, and calling data stored in the memory 204. Optionally, the processor 202 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 202 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modulation decoder. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modulation decoder may not be integrated into the processor, but may be implemented separately through a communication chip.

[0153] The memory 204 may include a random access memory (RAM) or a read-only memory (ROM). The memory 204 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 204 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., instructions for a user to obtain a random number), instructions for implementing the following various method embodiments, etc. The data storage area may also store data (e.g., random numbers) created by the terminal during use, etc.

[0154] The electronic device 200 may also include a network module and a screen. The network module is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices, such as communicating with an audio playback device. The network module may include various existing circuit components for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, user identity modules (SIM) cards, memories, etc. The network module may communicate with various networks such as the Internet, corporate intranets, wireless networks, or communicate with other devices via wireless networks. The above-mentioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The screen may display interface content and perform data interaction.

[0155] Embodiment 5

[0156] Please refer to Figure 7 , which shows a block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable storage medium 400 stores a program code 410, which can be called by a processor to execute the method described in the above method embodiment.

[0157] The computer-readable storage medium 400 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has storage space for program codes 410 that perform any method steps in the above method. These program codes 410 can be read from or written to one or more computer program products. The program codes 410 can be compressed, for example, in an appropriate form.

[0158] The embodiment of the present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the coalbed methane evaluation method described in the above various optional implementations.

[0159] In the several embodiments provided in the present disclosure, it should be understood that the disclosed method can also be implemented in other ways. The method embodiments described above are merely illustrative.

[0160] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0161] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A coalbed methane evaluation method, characterized in that: The method comprises: Obtain geological data and the main controlling factors affecting coalbed methane enrichment; Determine, based on a preset evaluation model, each of the weight coefficients of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model; Calculate the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors; First map information generated based on the comprehensive earthquake prediction results; The first map information is image-fused with the second map information reflecting geological laws in the geological data to evaluate target coalbed methane.

2. The method according to claim 1, characterized in that The method further comprises: The preset evaluation model is established by normalizing the main control factors based on a comprehensive evaluation method, wherein the comprehensive evaluation method includes: a neural network method, a hierarchical analysis method and an expert experience method.

3. The method according to claim 2, characterized in that The calculating of the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors includes: Calculate the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors using linear fusion information; Based on each of the weight coefficients and each of the main control factors, nonlinear fusion information is used to calculate the comprehensive earthquake prediction results.

4. The method according to claim 1, characterized in that: The main controlling factors affecting coalbed methane enrichment are obtained, including: Obtain coal seam geological characteristics and rock physical properties in the target area; The main controlling factors affecting coalbed methane enrichment are determined based on the coal seam geological characteristics and the rock physical properties.

5. The method according to claim 2, characterized in that: The first map information generated based on the comprehensive earthquake prediction results includes: Performing data gridding processing on the earthquake comprehensive prediction results to generate first grid data; Performing data regularization processing on the first grid data to generate first image information; The first image information is decomposed based on wavelet transformation to generate first image information.

6. The method according to claim 5, characterized in that The step of fusing the first map information with the second map information reflecting geological laws in the geological data includes: Perform image fusion based on a first pixel point of the first map information and a second pixel point of the second map information, or perform window image fusion based on the first map information and the second map information.

7. The method according to claim 1, characterized in that The main controlling factors include: coal seam thickness, coal quality, permeability, structural characteristics, burial depth and lithology of the coal seam cap rock.

8. A coalbed methane evaluation device, characterized in that: The device comprises: Acquisition module, used to obtain geological data and main controlling factors affecting coalbed methane enrichment; A determination module, for determining, based on a preset evaluation model, each of the weight coefficients of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model; A calculation module, used for calculating the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors; A generating module, used for generating first map information based on the earthquake comprehensive prediction result; The evaluation module is used to perform image fusion on the first map information and the second map information reflecting the geological law in the geological data to evaluate the target coalbed methane.

9. An electronic device, characterized in that: include: one or more processors; Memory; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the coalbed methane evaluation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, and the program codes can be called by one or more processors to execute the coalbed methane evaluation method as described in any one of claims 1-7.

Images