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

Abstract

The application discloses a coalbed methane evaluation method and device, electronic equipment and a storage medium, relates to the technical field of seismic exploration, and the coalbed methane evaluation method comprises the following steps: acquiring geological data and main control factors affecting coalbed methane enrichment, determining each weight coefficient of seismic single prediction results corresponding to each main control factor in a preset evaluation model based on the preset evaluation model, calculating seismic comprehensive prediction results based on each weight coefficient and each main control factor, generating first drawing information based on the seismic comprehensive prediction results, and performing image fusion on the first drawing information and second drawing information reflecting geological laws in the geological data, so as to evaluate target coalbed methane. Through the constraint of image fusion in comprehensive evaluation, the phase control constraint effect of a non-seismic drawing based on strong geological regularity is comprehensively considered, the seismic prediction result is more in line with the geological law, and the regularity and reliability of coalbed methane prediction and distribution are improved.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology, specifically to a method, apparatus, electronic device, and storage medium for evaluating coalbed methane. Background Technology

[0002] Currently, under the major trends of green and low-carbon development and international oil and gas energy supply and environmental protection, coalbed methane (CBM), as a clean energy source, is receiving increasing attention for its exploration and development. However, CBM falls under the unconventional category in the oil and gas field; it is an unconventional natural gas, a gas with strong adsorption properties, existing primarily in extremely low abundance or partially free states within the fissures and joints of coal seams. This type of reservoir differs from conventional oil and gas reservoirs, possessing many unique characteristics, thus requiring significantly different exploration methods in terms of scale and difficulty. The amount of seismic geological information needed for predicting and evaluating CBM is far greater than that required for conventional oil and gas. In particular, predicting and evaluating sweet spots in CBM requires even more information, presenting greater difficulty and challenges. Seismic exploration and development technologies for CBM are still in their early stages, and there is an urgent need for technologies for lateral prediction and internal structure description of coal seams, especially for predicting and evaluating the distribution of CBM sweet spots, which is crucial for the effective development of CBM.

[0003] However, among the technologies mentioned above, with the development of research in the field of coalbed methane, neural network methods have gradually been applied to the comprehensive evaluation of coalbed methane. However, due to the special characteristics of coalbed methane and its small identification scale, seismic exploration evaluation is more difficult than that of conventional oil and gas, with greater ambiguity and more influencing factors. Moreover, in the process of coalbed methane exploration, due to cost reasons, the degree of seismic exploration is often low, deploying two-dimensional seismic, large-grid non-seismic, or a small number of three-dimensional seismic data. Furthermore, the interpolation regularity of large-grid data is poor, and it may not even reflect geological regularities well.

[0004] Therefore, improving the reliability of coalbed methane evaluation and the predictability of coalbed methane distribution trends are problems that need to be addressed. Summary of the Invention

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

[0006] In a first aspect, embodiments of this application provide a method for evaluating coalbed methane, including:

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

[0008] Based on the preset evaluation model, determine the weight coefficients of each of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model.

[0009] The comprehensive earthquake prediction results are calculated based on the weight coefficients and the main control factors.

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

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

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

[0013] In some embodiments, calculating the comprehensive earthquake prediction result based on each of the weighting coefficients and each of the controlling factors includes:

[0014] Based on the weight coefficients and the main control factors, the comprehensive earthquake prediction results are calculated using linear fusion information.

[0015] The comprehensive earthquake prediction results are calculated using nonlinear fusion information based on the weighting coefficients and the main controlling factors.

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

[0017] To obtain the geological characteristics and rock physical properties of coal seams in the target area;

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

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

[0020] The comprehensive earthquake prediction results are then processed into a grid to generate the first grid data.

[0021] The first grid data is subjected to data regularization processing to generate the first image information;

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

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

[0024] Image fusion is performed based on the first pixel of the first image information and the second pixel of the second image information, or window image fusion is performed based on the first image information and the second image information.

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

[0026] Secondly, embodiments of this application provide a coalbed methane evaluation device, comprising:

[0027] The acquisition module is used to acquire geological data and the main controlling factors affecting coalbed methane enrichment;

[0028] The determination module is used to determine the weight coefficients of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model based on the preset evaluation model.

[0029] The calculation module is used to calculate the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors.

[0030] The generation module is used to generate first map information based on the comprehensive earthquake prediction results;

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

[0032] Thirdly, embodiments of this application provide an electronic device comprising: at least one processor and a memory; the processor is configured to execute a computer program stored in the memory to implement the coalbed methane evaluation method as described in any embodiment of the first aspect.

[0033] Fourthly, embodiments of this application provide 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 the coalbed methane evaluation method as described in any embodiment of the first aspect.

[0034] This application provides a coalbed methane evaluation method, apparatus, electronic device, and storage medium, comprising: acquiring geological data and the main controlling factors affecting coalbed methane enrichment; determining the weight coefficients of individual seismic prediction results corresponding to each main controlling factor in the preset evaluation model based on a preset evaluation model; calculating the comprehensive seismic prediction results based on each weight coefficient and each main controlling factor; and performing image fusion between the first map information generated based on the comprehensive seismic prediction results and the second map information reflecting geological laws in the geological data to evaluate the target coalbed methane. By using image fusion to participate in the comprehensive evaluation, the application comprehensively considers the phase control constraint effect of non-seismic maps with strong geological regularity, making the seismic prediction results more consistent with geological laws and improving the regularity and reliability of coalbed methane prediction distribution.

[0035] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of this embodiment will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A schematic diagram of a coalbed methane evaluation method proposed in one embodiment of this application is shown.

[0038] Figure 2 This illustration shows a schematic diagram of a comprehensive geophysical evaluation parameter system for coalbed methane proposed in one embodiment of this application;

[0039] Figure 3 This illustration shows a schematic diagram of an image fusion principle proposed in one embodiment of this application;

[0040] Figure 4 This illustration shows an exemplary comprehensive evaluation method for a coalbed methane enrichment zone in a target area, proposed in one embodiment of this application.

[0041] Figure 5 A structural block diagram of a coalbed methane evaluation device according to an embodiment of this application is shown;

[0042] Figure 6 A structural block diagram of an electronic device for performing a coalbed methane evaluation method according to an embodiment of this 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 Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] Currently, under the major trends of green and low-carbon development and international oil and gas energy supply and environmental protection, coalbed methane (CBM), as a clean energy source, is receiving increasing attention for its exploration and development. However, CBM falls into the unconventional category of oil and gas, being an unconventional natural gas with strong adsorption properties. It exists primarily in extremely low abundance, either adsorbed or partially free, within the fissures and joints of coal seams. This type of reservoir differs from conventional oil and gas reservoirs, possessing many unique characteristics, thus requiring significantly different exploration methods in terms of scale and difficulty. The amount of seismic geological information needed for predicting and evaluating CBM is far greater than that required for conventional oil and gas. In particular, predicting and evaluating sweet spots in CBM requires even more information, presenting greater difficulty and challenges. These factors severely restrict the pace of CBM exploration and development. Seismic exploration and development technologies for CBM are still in their early stages, and there is an urgent need for technologies for lateral prediction and internal structure description of coal seams, especially for predicting and evaluating the distribution of CBM sweet spots, which is crucial for the effective development of CBM.

[0047] Faced with such a complex coalbed methane prediction problem, the ambiguity of geophysical techniques becomes even more prominent. For example, accurately predicting channel sand bodies or identifying oil and gas using strong seismic reflection properties is impossible in coalbed methane prediction, as single seismic analysis methods and results often exhibit strong ambiguity. Therefore, when predicting such complex coalbed methane targets, especially the prediction, evaluation, and selection of sweet spots, it is essential to first consider the prediction of multiple major controlling factors of coalbed methane, and secondly, to consider the ambiguity of geophysical predictions based on a single major controlling factor. The method to reduce ambiguity is the dimensionality reduction method of information fusion. Information fusion involves analyzing multiple result elements, assigning appropriate weights to result information that best reflects reality or is considered more consistent with geological laws, and then integrating these informations with different weights into a comprehensive result in a new dimensional or dimensionless form, followed by hierarchical evaluation.

[0048] The inventors' research revealed that with the development of the coalbed methane (CBM) field, seismic exploration results are increasingly being applied to the comprehensive evaluation of CBM. Data-driven information fusion-based comprehensive evaluation can be conducted using the aforementioned evaluation methods, depending on specific circumstances and requirements. However, due to the unique characteristics of CBM and its small identification scale, seismic exploration evaluation is more challenging than that of conventional oil and gas exploration, exhibiting greater ambiguity and more influencing factors. During CBM exploration, cost considerations often limit the level of seismic exploration, deploying 2D seismic data, large-grid non-seismic data, or a limited number of 3D seismic data. Large-grid data interpolation patterns are poor and may not accurately reflect geological patterns. Some geologically significant image (filled map) information is crucial for comprehensive evaluation. For example, some geological maps are important results formed by geologists based on a close integration of regional geology, geological outcrops, and specific findings of the study area, reflecting macroscopic understandings such as sedimentary patterns. These maps can guide or even directly apply to the regular constraints of comprehensive evaluation, i.e., facies control. These maps are generally based on actual statistics, incorporating the geologists' experience and insights, and cannot be achieved by simply relying on scattered statistical data and using various interpolation methods for computer mapping.

[0049] Therefore, the inventors considered that coalbed methane (CBM) is an unconventional natural gas, and its enrichment is controlled by multiple factors. The burial depth of the coal seam, the lithology of the roof and floor, coal quality characteristics, reservoir pressure, coal seam thickness, and coal reservoir permeability all significantly influence the formation and enrichment characteristics of CBM reservoirs. Under these multiple conditions, using a single gas-bearing prediction method for predicting and evaluating the gas-bearing capacity of CBM reservoirs has low reliability. Furthermore, seismic technology also exhibits multiple solutions for single-factor predictions. Therefore, in addition to addressing the multiple solutions in seismic prediction requiring dimensionality reduction and information fusion for single elements, multi-element prediction results based on controlling factors also need to be evaluated through information fusion. Due to the difficulty of seismic prediction of CBM and the often low level of seismic exploration, the multiple solutions in seismic prediction manifest as weak geological regularity in the distribution trend, requiring facies control with geological constraints to better conform to geological regularity. In this case, large-grid and large-scale geological maps or non-seismic data maps can serve as such facies control conditions.

[0050] To address the aforementioned issues, the applicant has proposed a coalbed methane evaluation method, apparatus, electronic device, and storage medium as described in this application. This method involves acquiring geological data and identifying the main controlling factors influencing coalbed methane enrichment. Based on a pre-defined evaluation model, the weight coefficients of individual earthquake prediction results corresponding to each main controlling factor are determined within the model. A comprehensive earthquake prediction result is calculated based on these weight coefficients and the main controlling factors. The first map information generated from the comprehensive earthquake prediction result is then fused with second map information reflecting geological patterns from the geological data to evaluate the target coalbed methane. This approach employs the fusion of (earthquake prediction) data and image (geological, non-seismic, etc.) information to achieve comprehensive coalbed methane evaluation and optimization, making the prediction and comprehensive evaluation more consistent with geological patterns. The coalbed methane evaluation method will be described in detail in subsequent embodiments.

[0051] Example 1

[0052] The following describes the application scenarios of the coalbed methane evaluation method provided in the embodiments of this application:

[0053] Please see Figure 1 , Figure 1 This is a schematic flowchart of a coalbed methane evaluation method provided in this embodiment of the application, which aims to achieve coalbed methane prediction, comprehensive evaluation, optimization, exploration, and development in a target area, thereby improving the regularity and reliability of coalbed methane prediction and distribution. In this embodiment, the coalbed methane evaluation method can be applied to, for example... Figure 5 The coalbed methane evaluation device 300 shown is... Figure 6The electronic device 200 shown can be a desktop computer, tablet computer, smartphone or other smart terminal. The electronic device 200 can include one or more devices. Multiple electronic devices can transmit information wirelessly and / or via wired means. Multiple electronic devices can work together to complete the coalbed methane evaluation method. For example, various data can be obtained through a smart terminal to complete the coalbed methane evaluation method process.

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

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

[0056] In this embodiment, geological data may include: geological and non-seismic data and maps related to coal seams, such as sediments, which can be used to constrain seismic results to achieve facies control. The main controlling factors influencing coalbed methane enrichment may include: structural factors, coal quality, coal seam properties, capping conditions, coal seam morphology, and hydrological conditions, among which, please refer to... Figure 2 , Figure 2 This is a schematic diagram of a comprehensive geophysical evaluation parameter system for coalbed methane in an embodiment of this application. The comprehensive evaluation system for coalbed methane in the field of geophysical prediction corresponds to this system and mainly includes the seismic geometric attributes of coal seams and coalbed methane, fracture detection, and seismic inversion results.

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

[0058] In this embodiment of the application, considering that coalbed methane enrichment can be controlled by a variety of factors such as coal seam thickness, coal quality, permeability, structural features, burial depth, and coal seam caprock lithology, the evaluation is carried out by comprehensively evaluating the corresponding seismic comprehensive evaluation parameters and geological and non-seismic results, that is, by using different weight coefficients for different evaluation elements.

[0059] Step S130: Calculate the comprehensive earthquake prediction results based on each weight coefficient and each main control factor.

[0060] In the embodiments of this application, the comprehensive prediction results after calculation of all different evaluation elements and different weight coefficients can be obtained by normalization processing.

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

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

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

[0064] In this embodiment, by determining the weight coefficients corresponding to each main control factor and calculating the comprehensive earthquake prediction result based on the individual earthquake prediction results, it is possible to help identify and evaluate the importance of the main control factors to coalbed methane enrichment and their contribution to earthquake prediction. By acquiring geological data and performing image fusion, and combining the information from the first map and the information from the second map reflecting geological laws, the geological laws of coalbed methane enrichment can be analyzed and evaluated. This helps to understand the influence of geological conditions on the distribution and enrichment of coalbed methane, determine the importance of geological features and structural conditions for coalbed methane exploration, and thus provide a scientific basis for subsequent exploration and development work. The approach of fusing (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 also include step S210.

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

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

[0068] In this embodiment, the comprehensive evaluation method combines neural network method, analytic hierarchy process (AHP), and expert experience method. It can fully consider the relationships and weights between multiple evaluation indicators and multiple controlling factors. Through normalization and comprehensive evaluation methods, it can comprehensively consider the influence of different factors and obtain a comprehensive evaluation result for the controlling factors. This helps reduce the subjectivity and limitations of single evaluation methods and improves the objectivity and accuracy of evaluation results. The normalization method unifies controlling factors of different scales and magnitudes, eliminating dimensional differences between factors. This ensures that each controlling factor has the same unit of measurement in the comprehensive evaluation model, facilitating weight calculation and comprehensive analysis, and allowing different factors to participate fairly in the comprehensive evaluation. The AHP can analyze the importance and weight of different indicators through hierarchical structure and judgment matrix. The expert experience method can introduce the knowledge and experience of domain experts, providing subjective weights and factor evaluations. This comprehensively utilizes the advantages of multiple methods to obtain a more comprehensive and accurate comprehensive evaluation result.

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

[0070] Step S130: Calculate the comprehensive earthquake prediction results based on each weight coefficient and each main control factor.

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

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

[0073] In some implementations, step S130, which calculates the comprehensive earthquake prediction results based on each weight coefficient and each controlling factor, may include steps S132 to S134.

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

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

[0076] Step S120: Determine the weight coefficients of each earthquake prediction result corresponding to each main control factor 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 each weight coefficient and each main control factor.

[0078] In the embodiments of this application, linear fusion information is suitable for situations where there is a linear relationship between the main control factors, while nonlinear fusion information is suitable for situations where there is a complex nonlinear relationship between the main control factors. By selecting an appropriate fusion method, the interaction and comprehensive effect between the main control 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 using nonlinear fusion information based on each weight coefficient and each main control factor.

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

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

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

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

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

[0085] In the embodiments of this 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 coal seam geological characteristics and rock physical properties.

[0087] In the embodiments of this application, by identifying the main controlling factors affecting coalbed methane enrichment, it is possible to 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 law of coalbed methane, which 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 each earthquake prediction result corresponding to each main control factor in the preset evaluation model based on the preset evaluation model.

[0089] Step S130: Calculate the comprehensive earthquake prediction results based on each weight coefficient and each main control factor.

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

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

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

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

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

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

[0096] Step S130: Calculate the comprehensive earthquake prediction results based on each weight coefficient and each main control factor.

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

[0098] In this embodiment, the purpose of performing data gridding processing on the comprehensive earthquake prediction results to generate the first grid data is to present the earthquake prediction results in a regular grid format, facilitating subsequent processing and analysis. Through data gridding processing, the earthquake prediction results can be divided into different grid units, each unit representing a specific earthquake attribute or characteristic.

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

[0100] In this embodiment, the purpose of performing data regularization processing on the first grid data to generate the first image information is to convert the grid data into an image format, making it more intuitive and easier to understand. Data regularization processing can employ different methods, such as color mapping and contour plotting, to convert the value of each grid cell into corresponding image pixels or colors, 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 the first image information.

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

[0103] In this embodiment, by performing data gridding, data regularization, and wavelet transform-based decomposition on the comprehensive earthquake prediction results, the visualization, feature extraction, and analysis of the earthquake prediction results can be achieved. This allows the input image information to be gridded and regenerated into regularized image information, and further uses wavelet transform to decompose the image into pixel-based data points, which are then further fused and inversely transformed to form a new image.

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

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

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

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

[0108] Step S130: Calculate the comprehensive earthquake prediction results based on each weight coefficient and each main control factor.

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

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

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

[0112] In other aspects, the role of window image fusion based on the information of the first and second images is to define a window and fuse the two images within that window to obtain a fused image. Window image fusion can be implemented through different algorithms and weighting strategies, such as weighted average, maximum value, and minimum value. It allows the information of the two images to interact and integrate within the window, providing richer and more comprehensive visual information, helping to reduce noise and artifacts in the image, and improving image quality and visualization effects.

[0113] In this embodiment, by comprehensively considering the phase control constraint effect of large-scale non-seismic maps with strong geological regularity, the input image information is gridded to regenerate regular image information. Wavelet transform is then used to decompose the image into pixel-based data points, which are then further fused and inversely transformed to form a new image. Through wavelet transform graphics fusion technology, a comprehensive evaluation of coalbed methane based on multiple result elements driven by graphics and seismic data is achieved, improving the regularity and reliability of coalbed methane prediction and distribution.

[0114] In summary, this embodiment provides a coalbed methane evaluation method that acquires geological data and the main controlling factors influencing coalbed methane enrichment. Based on a pre-set evaluation model, it determines the weight coefficients of individual seismic prediction results corresponding to each main controlling factor within the model. Based on these weight coefficients and the main controlling factors, it calculates a comprehensive seismic prediction result. Using the first map information generated from the comprehensive seismic prediction result, it performs image fusion with second map information reflecting geological patterns from the geological data to evaluate the target coalbed methane. The constraints of image fusion in the comprehensive evaluation make the seismic prediction results more consistent with geological patterns. It comprehensively considers the phase control constraints of large-scale non-seismic maps with strong geological regularity and achieves a comprehensive evaluation of coalbed methane based on multiple result elements driven by both image and seismic data through wavelet transform image fusion technology. This improves the regularity and reliability of coalbed methane prediction and distribution.

[0115] Example 2

[0116] Building upon Example 1, the following study focuses on a specific test target area, applying a comprehensive evaluation technique for coalbed methane based on data and image information fusion. During the study, effective seismic prediction results related to key controlling factors such as coal quality, coal seam, coal reservoir porosity and permeability, structure, burial depth, and caprock conditions are screened, prioritizing factors such as structure, amplitude attributes, thickness, absorption attenuation, and coal seam fractures. After obtaining relevant weight values ​​using the analytic hierarchy process (AHP), a comprehensive evaluation of seismic predictions is performed. The practical application of the coalbed methane evaluation method may include the following steps:

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

[0118] In this step, the main controlling factors for coalbed methane enrichment are structure, coal quality, coal seam properties, capping conditions, coal seam morphology, and hydrological conditions, as mentioned above. Corresponding to these aspects, the comprehensive evaluation system for coalbed methane in the field of geophysical prediction includes results such as seismic geometric attributes of coal seams and coalbed methane, fracture detection, and seismic inversion. Figure 2 In addition, some geological and non-seismic data and maps related to coal seams, such as sediments, can be used to constrain seismic results, i.e., facies 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, including coal seam thickness, coal quality, permeability, structural characteristics, burial depth, and lithology of the coal seam caprock. It can be comprehensively evaluated based on the corresponding seismic comprehensive evaluation parameters and geological and non-seismic results.

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

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

[0123] Step S21: Obtain the normalized weight coefficients of each participating element using comprehensive evaluation methods such as the most hierarchical 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: Combining the actual predicted single-element results data, perform calculations based on information fusion technology (linear or nonlinear) according to the weighting coefficients. Taking linear as an example: Z = a1x1 + a2x2 + a3x3 + ... + anxn, thereby obtaining the normalized comprehensive evaluation result Z of earthquake prediction.

[0127] Step S23: Comprehensive evaluation of coalbed methane based on map-based graphic fusion. The results of the comprehensive evaluation of earthquake prediction are generated into maps, which are then combined with collected geological maps that reflect geological patterns and non-seismic maps (large grid data). Graphic information fusion technology is used to conduct a comprehensive evaluation under geologically constrained phase control mode. Specific details can be obtained from... Figure 3 The method is achieved through wavelet transform algorithm, which regenerates regular image information by gridding the input image information, further uses wavelet transform to decompose the image into pixel-based data points, and then further fuses and inversely transforms them to form a new image.

[0128] In practical applications, the comprehensive evaluation of earthquake prediction after obtaining the relevant weight values ​​through the analytic hierarchy process is shown in Table 3 below:

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

[0130]

[0131] Table 2

[0132] Based on Table 2, analysis revealed a large-grid electromagnetic anomaly map of the area. Geological analysis indicated that the anomaly trend was well-aligned with the well and sedimentary background. Subsequently, the favorable areas were further evaluated and optimized using wavelet transform-based graphic information fusion technology, combining the seismic comprehensive evaluation results map with the magnetotelluric data. Subsequent drilling verification yielded positive results.

[0133] Please see Figure 4 , Figure 4 This application provides an exemplary schematic diagram of a comprehensive evaluation of a coalbed methane enrichment zone in a target area, as shown in one embodiment. Figure 4 Based on the anomaly distribution image data obtained from magnetotelluric resistivity inversion, and further optimized and evaluated coalbed methane enrichment areas using image information fusion technology, the final evaluation and optimization results show that the western and eastern parts of the study block are relatively rich in coalbed methane, followed by the southern part, the central part is relatively poor, and the northern part is the worst. Wells and well networks with good actual drilling results in the eastern part are all located in relatively rich coalbed methane zones, while the western part has the best evaluation over a larger area, classified as a Class I area. This area currently has no drilling and represents a promising area for coalbed methane development. Coalbed methane enrichment is controlled by multiple factors, including coal seam thickness, physical properties, fractures, and caprock lithology. The uneven distribution of the predicted enrichment areas indicates differences in coalbed methane accumulation conditions and the heterogeneity of the coal seams. The predicted evaluation results are consistent with the patterns and geological characteristics reflected in existing wells.

[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. This invention primarily applies four main controlling factors: coal quality, coal thickness, gas content based on physical properties, and structural burial depth.

[0135] As shown in Table 2, the weight coefficients of each individual seismic prediction result of the main controlling factor of coalbed methane enrichment in the comprehensive evaluation were determined by the analytic hierarchy process.

[0136] according to Figure 4 As shown in the upper left figure, by applying the weight coefficients of the main controlling factors of coalbed methane enrichment, the results of the characterization of the main controlling factors of earthquake prediction are evaluated, and the structural, amplitude attributes, thickness, absorption attenuation, and coal seam fractures are selected. Through normalization calculation, a preliminary comprehensive evaluation map of coalbed methane enrichment earthquake prediction for a certain coal seam in the target area is obtained.

[0137] The preliminary comprehensive evaluation results of earthquake prediction were combined with the collected large-grid magnetotelluric anomaly images (as attached). Figure 4(Top right figure) Image information fusion was performed using wavelet transform, with the magnetotelluric data weighted at 0.2. The final evaluation and optimization results are shown in the appendix. Figure 4 See the image below. Finally, the evaluation results are divided into zones based on the distribution of fractures.

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

[0139] Example 3

[0140] Please see Figure 5 , Figure 5 This application provides a coalbed methane evaluation device 300, which 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 the main controlling factors affecting coalbed methane enrichment.

[0142] The determination module 320 is used to determine the weight coefficients of the individual earthquake 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] Generation module 340 is used to generate the first map information based on the comprehensive earthquake prediction results;

[0145] Evaluation module 350 is used to perform image fusion between the first map information and the second map information reflecting geological laws in the geological data in order to evaluate the target coalbed methane.

[0146] It should be noted that the device embodiments in this application correspond to the aforementioned method embodiments. The specific principles in the device embodiments can be found in the content of the aforementioned method embodiments, and will not be repeated here.

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

[0148] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0149] Example 4

[0150] Please see Figure 6 , Figure 6 The present application provides a structural block diagram of an electronic device 200 that can perform the above-described coalbed methane evaluation method. The electronic device 200 may be a computer, tablet computer, smartphone, or portable computer.

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

[0152] The processor 202 may include one or more cores for data processing and message matrix units. The processor 202 connects to various parts of the electronic device 200 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 204, and by calling data stored in the memory 204. Optionally, the processor 202 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 202 may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem / decoder. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem is used for wireless communication. It is understood that the modem / decoder may also not be integrated into the processor and may be implemented separately through a communication chip.

[0153] Memory 204 may include random access memory (RAM) or read-only memory (ROM). Memory 204 can be used to store instructions, programs, code, code sets, or instruction sets. 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 random numbers), instructions for implementing the various method embodiments described below, etc. The data storage area may also store data (e.g., random numbers) created by the terminal during use.

[0154] Electronic device 200 may also include a network module and a screen. The network module is used to receive and transmit electromagnetic waves, converting electromagnetic waves into electrical signals, thereby enabling communication with communication networks or other devices, such as audio playback devices. The network module may include various existing circuit elements used to perform these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, SIM cards, memory, etc. The network module can communicate with various networks such as the Internet, corporate intranets, and wireless networks, or communicate with other devices via wireless networks. The aforementioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The screen can display interface content and facilitate data interaction.

[0155] Example 5

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

[0157] The computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or 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 code 410 that performs any of the method steps described above. This program code 410 can be read from or written to one or more computer program products. The program code 410 may, for example, be compressed in a suitable form.

[0158] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the coalbed methane evaluation method described in the various optional implementations above.

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

[0160] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0161] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 this application.

Claims

1. A method for evaluating coalbed methane, characterized in that, The method includes: Obtaining geological data and identifying the main controlling factors affecting coalbed methane enrichment; Based on the preset evaluation model, determine the weight coefficients of the individual earthquake prediction results corresponding to each of the main control factors in the preset evaluation model. The comprehensive earthquake prediction results are calculated based on the weight coefficients and the main control factors. First map information generated based on the comprehensive earthquake prediction results; The first map information is fused with the second map information reflecting geological patterns in the geological data to evaluate the target coalbed methane. The first map information generated based on the comprehensive earthquake prediction results includes: The comprehensive earthquake prediction results are then processed into a grid to generate the first grid data. The first grid data is subjected to data regularization processing to generate the first image information; The first image information is decomposed based on wavelet transform to generate the first image information; The step of image fusion of the first map information with the second map information reflecting geological patterns in the geological data includes: Image fusion is performed based on the first pixel of the first image information and the second pixel of the second image information, or window image fusion is performed based on the first image information and the second image information.

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

3. The method according to claim 2, characterized in that, The calculation of the comprehensive earthquake prediction result based on each of the weighting coefficients and each of the main controlling factors includes: Based on the weight coefficients and the main control factors, the comprehensive earthquake prediction results are calculated using linear fusion information. The comprehensive earthquake prediction results are calculated using nonlinear fusion information based on the weighting coefficients and the main controlling factors.

4. The method according to claim 1, characterized in that, The main controlling factors influencing coalbed methane enrichment were identified, including: To obtain the geological characteristics and rock physical properties of coal seams in the target area; Based on the geological characteristics of the coal seam and the physical properties of the rock, the main controlling factors affecting coalbed methane enrichment are determined.

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

6. A coalbed methane evaluation apparatus for implementing the coalbed methane evaluation method according to any one of claims 1-5, characterized in that, The device includes: The acquisition module is used to acquire geological data and the main controlling factors affecting coalbed methane enrichment; The determination module is used to determine the weight coefficients of the earthquake single prediction results corresponding to each of the main control factors in the preset evaluation model based on the preset evaluation model. The calculation module is used to calculate the comprehensive earthquake prediction results based on each of the weight coefficients and each of the main control factors. The generation module is used to generate first map information based on the comprehensive earthquake prediction results; The evaluation module is used to perform image fusion between the first map information and the second map information reflecting geological laws in the geological data to evaluate the target coalbed methane.

7. 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, the one or more programs being configured to perform the coalbed methane evaluation method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by one or more processors to execute the coalbed methane evaluation method as described in any one of claims 1-5.

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