Three-dimensional seismic exploration data analysis system for coal bed gas field

Abstract

The invention relates to the technical field of data analysis, and particularly discloses a three-dimensional seismic exploration data analysis system for a coal bed gas field, which is used for solving the problems of complex ground environment, high reliability and the like when three-dimensional seismic exploration is used for finding out tectonic development and coal bed occurrence conditions. The problems that seismic wave signals are interfered by different earth surface conditions, first arrival time and amplitude information of waves are difficult to accurately obtain due to many layer sections and large buried depth change, and data accuracy is influenced by low signal-to-noise ratio are solved. Comprising an earthquake acquisition control module, a geological modeling analysis module, a data analysis module, a three-dimensional imaging display module, an environment identification module and an acquisition strategy optimization module. According to the invention, the environment identification module is used for identifying the surface type, accurately adjusting explosive source parameters and improving signal quality, the waveform feature extraction module is used for processing seismic wave signals and improving data accuracy, and the data analysis module is used for acquiring modeling data and evaluation indexes and optimizing the acquisition method.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and more specifically, to a three-dimensional seismic exploration data analysis system for coalbed methane fields. Background Art

[0002] For the coalbed methane well field area with complex ground environment, covering bedrock outcrop areas, farmland areas, weathered material areas and gully areas, different surface conditions cause serious interference to seismic wave signals during propagation and reception, affecting signal quality and making it impossible to accurately obtain effective information. At the same time, there are many layers in such areas and the burial depth varies greatly. Conventional exploration methods are difficult to accurately obtain the first arrival time and amplitude information of seismic waves, and it is impossible to effectively infer the underground stratigraphic structure and lithology, making it difficult to meet the exploration requirements. In addition, there is a problem of low signal-to-noise ratio in such areas, and noise interference seriously affects the accuracy and reliability of data, resulting in difficulties in subsequent geological analysis and interpretation work. Therefore, there is an urgent need for a three-dimensional seismic exploration data analysis system for coalbed methane fields to solve the above problems. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a three-dimensional seismic exploration data analysis system for coalbed methane fields, which uses an environment recognition module to identify the surface type, accurately adjusts the explosive source parameters to improve signal quality, a waveform feature extraction module to process seismic wave signals to improve data accuracy, and a data analysis module to obtain modeling data and evaluation indicators, optimize the acquisition method, solve traditional exploration problems, and assist in the exploration of coalbed methane fields.

[0004] To achieve the above object, the present invention provides the following technical solutions: A three-dimensional seismic exploration data analysis system for coalbed methane fields, including a seismic acquisition control module, a geological modeling analysis module, a data analysis module, a three-dimensional imaging display module, an environment recognition module, and a acquisition strategy optimization module. The environment recognition module identifies the surface environment, analyzes the surface type and seismic wave signals. The seismic acquisition control module transmits the seismic wave first arrival time signal and the amplitude signal to the geological modeling analysis module and the waveform feature extraction module respectively. The geological modeling analysis module corrects the seismic wave first arrival time signal and transmits the corrected signal to the waveform feature extraction module. The waveform feature extraction module picks up the first depth feature, the second depth feature, and the third depth feature in the first arrival time signal, filters out the invalid features of the third depth feature using the first depth feature and the second depth feature, and then transmits the data to the data analysis module. The waveform feature extraction module obtains the frequency domain signal of the seismic wave amplitude using the short-time Fourier transform, corrects the amplitude frequency domain signal using the formation absorption attenuation model, converts the frequency domain signal back to the time domain using the inverse short-time Fourier transform, and transmits it to the data analysis module. The data analysis module analyzes the received data to obtain the three-dimensional modeling data of the geological structure of the coalbed methane field and the geological evaluation index, and transmits the two to the three-dimensional imaging display module and the seismic acquisition control module respectively. At the same time, the first feedback data is obtained using the first depth feature, the second depth feature, and the filtered third depth feature, and then transmitted to the waveform feature extraction module. The second feedback data is obtained using the amplitude signal and then transmitted to the seismic acquisition control module. The seismic acquisition control module interacts with the acquisition strategy optimization module to automatically generate an improved scheme for the data acquisition method.

[0005] As a further technical solution of the present invention, the seismic acquisition control module is a geophone located at several underground positions and depths. The environment recognition module analyzes the collected surface physical feature data to obtain the surface type feature, combines the time domain feature and the frequency domain feature of the seismic wave, further refines the surface type feature, and inputs the surface type feature into a preset explosive source parameter analysis formula to obtain the well depth and charge parameters of the explosive source suitable for the current environment. The preset explosive source parameter analysis formula is: ;

[0006] In the formula: is the explosive source parameter, including the well depth and charge of the explosive source, is the start coefficient, When is the charge of the explosive source, When is the well depth of the explosive source, is the well depth analysis coefficient vector, , where 、 、 are the empirical weights, is the well depth analysis feature vector, , where and are the density of the surface medium and the propagation velocity of seismic waves in the surface medium respectively, is the main frequency of the seismic wave signal, is the vector of charge analysis coefficients, , where , , are empirical weights, is the charge analysis feature vector, , is the explosive source, obtained by calculating the value at , is the maximum dimensionless amplitude of the seismic wave, is the attenuation coefficient of the seismic wave during propagation, is the constant term.

[0007] As a further technical solution of the present invention, in the waveform feature extraction module, the first depth feature includes but is not limited to the lithology change parameters of the shallow formation, the depth range of the formation interface, and the data on the influence of the shallow formation on the propagation of seismic waves. The second depth feature includes but is not limited to the thickness change parameters of the middle formation and the performance characteristics of the preset geological structure in the seismic wave signal. The third depth feature includes the lithology combination parameters of the deep formation and the deep geological structure parameters.

[0008] As a further technical solution of the present invention, in the waveform feature extraction module, the shallow formation, the middle formation, and the deep formation are the formations from 0 to 500 meters, the formations from 501 to 2000 meters, and the formations below 2000 meters respectively. The way that the first depth feature and the second depth feature help filter the invalid third depth feature is as follows: based on the existing geological knowledge and historical coalbed methane field exploration data, establish the corresponding relationship between the first depth feature, the second depth feature and the underground geological structure. Using this corresponding relationship, speculate the reasonable range of the signal features in the third depth feature under the premise of conforming to this corresponding relationship. Compare the third depth feature with the reasonable range speculated from the first depth feature and the second depth feature, and determine the features in the third depth feature that exceed the reasonable range as invalid features, and filter out the invalid features in the third depth feature.

[0009] As a further technical solution of the present invention, in the data analysis module, the three-dimensional modeling data of the coalbed methane field geological structure includes but is not limited to formation surface data, fault data, fold data, and lithology data. The formation surface data includes the depth, thickness, and dip angle of each formation. The fault data includes the position, strike, and throw data of the fault. The fold data includes the shape, axial direction, and amplitude data of the fold. The lithology data includes the rock type, rock density, and seismic wave velocity through each formation of each formation.

[0010] As a further technical solution of the present invention, in the data analysis module, the formation surface data is obtained by analyzing the first depth feature, the second depth feature, and the filtered third depth feature. The fault data and fold data are identified by analyzing the seismic wave amplitude signal. The lithology data is inferred by combining the formation surface data and the seismic wave amplitude signal through the relationship between the rock physical parameters and the seismic wave velocity and amplitude.

[0011] As a further technical solution of the present invention, in the data analysis module, the geological evaluation indexes of the coalbed methane field include the resource abundance, reservoir permeability, gas saturation, and single well production prediction index of the coalbed methane field, which are obtained by analyzing through a machine learning model. The seismic acquisition control module receives the resource abundance, reservoir permeability, gas saturation, and single well production prediction index of the coalbed methane field, and exchanges these data with the acquisition strategy optimization module.

[0012] As a further technical solution of the present invention, in the acquisition strategy optimization module, the geophone spacing of each area is adjusted according to the resource abundance data. The preset geophone spacing adjustment formula is: ;

[0013] In the formula: is the index of the research area, is the adjusted spacing of the geophone in the th research area, is the first correction coefficient, is the resource abundance correlation coefficient, which is adjusted according to the relative size of the resource abundance of the coalbed methane field in the research area and the average resource abundance, is the average resource abundance; The geophone frequency response is adjusted according to the reservoir permeability. The preset geophone frequency response adjustment formula is: ;

[0014] In the formula: is the adjusted frequency response value of the geophone in the th research area, is the second correction coefficient, is the correlation coefficient of reservoir permeability, which is adjusted according to the relative magnitudes of the reservoir permeability of the coalbed methane field in the study area and the preset geophone response frequency. The preset geophone response threshold.

[0015] As a further technical solution of the present invention, in the acquisition strategy optimization module, the geophone sensitivity is adjusted based on the gas saturation. The preset geophone sensitivity adjustment formula is: ;

[0016] In the formula: is the adjusted sensitivity of the geophone in the th study area, is the initial sensitivity, is the third correction coefficient, is the gas saturation correlation coefficient, which is related to the relative magnitudes of the gas saturation in the current study area and the preset gas saturation threshold; The geophone azimuth angle is adjusted according to the single-well production prediction index. When the change rate of the single-well prediction index affected by the reservoir permeability in one direction exceeds the preset threshold, this direction is taken as the key monitoring direction, and the azimuth angle of the geophone is adjusted to face the key monitoring direction.

[0017] As a further technical solution of the present invention, in the waveform feature extraction module, the first feedback data is the deviation value between the processing result of the seismic wave first arrival time signal and the standard result. The weights and thresholds of the multi-layer perceptron are adjusted according to this deviation value. In the seismic acquisition control module, the second feedback data is the matching degree between the processed seismic wave amplitude signal and the expected geological structure and lithology characteristics. Based on the preset fuzzy rules, the first correction value, the second correction value, the third correction value for adjusting the geophone distribution spacing, frequency response, sensitivity, and geophone azimuth angle are generated.

[0018] The technical effects of a three-dimensional seismic exploration data analysis system for coalbed methane fields proposed by the present invention: The present invention can intelligently identify different surface types such as bedrock outcrop areas and farmland areas through an environmental recognition module, accurately adjust the well depth and charge parameters of the explosive source through a preset formula, reduce the interference of seismic wave propagation, and improve the signal quality. The waveform feature extraction module uses a multi-layer perceptron and a specific algorithm to accurately pick up the depth features of the seismic wave first arrival time signal, filter out invalid information, and can also effectively process the seismic wave amplitude signal, improving the accuracy and effectiveness of the data. Based on the processed data, the data analysis module obtains three-dimensional modeling data of the coalbed methane field geological structure, covering information on strata, faults, folds, and lithology. At the same time, geological evaluation indicators such as resource abundance and reservoir permeability are obtained through a machine learning model, comprehensively reflecting the geological characteristics of the coalbed methane field. According to the geological evaluation indicators and feedback data, the acquisition method is automatically optimized, improving the pertinence and accuracy of data acquisition, forming a virtuous cycle of data acquisition and analysis, effectively solving the problems faced by traditional exploration methods in complex coalbed methane fields, reducing manual intervention, improving data accuracy and model accuracy, providing a reliable decision-making basis for the exploration and development of coalbed methane fields, promoting the efficient development of coalbed methane field exploration work, and reducing exploration costs and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system block diagram of the present invention; Figure 2 is a real-time update diagram of the seismic waveform data of the present invention; Figure 3 is a real-time environmental monitoring curve diagram of the present invention; Figure 4 is a waveform recognition and analysis diagram of the present invention; Figure 5 is a three-dimensional model preview diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Example 1: As Figure 1As shown in the figure, a three-dimensional seismic exploration data analysis system proposed by the present invention for coalbed methane fields includes a seismic acquisition control module, a geological modeling analysis module, a data analysis module, a three-dimensional imaging display module, an environment recognition module, and a acquisition strategy optimization module. The environment recognition module intelligently recognizes the surface environment, analyzes the surface type and seismic wave signals, and switches the explosive source to the appropriate well depth and charge parameters. The seismic acquisition control module transmits the seismic wave first arrival time signal and the seismic wave amplitude signal to the geological modeling analysis module and the waveform feature extraction module respectively. The geological modeling analysis module corrects the seismic wave first arrival time signal and transmits the corrected signal to the waveform feature extraction module. The waveform feature extraction module picks up the first depth feature, the second depth feature, and the third depth feature in the corrected seismic wave first arrival time signal through a multi-layer perceptron, filters the invalid features of the third depth feature using the first depth feature and the second depth feature, and transmits the first depth feature, the second depth feature, and the filtered third depth feature to the data analysis module. The waveform feature extraction module obtains the frequency domain signal of the seismic wave amplitude signal using the short-time Fourier transform, corrects the seismic wave amplitude frequency domain signal using the formation absorption attenuation model, converts the seismic wave amplitude frequency domain signal back to the time domain using the inverse short-time Fourier transform, and transmits the processed seismic wave amplitude signal to the data analysis module. The data analysis module obtains the three-dimensional modeling data of the coalbed methane field geological structure and the coalbed methane field geological evaluation index through the first depth feature, the second depth feature, the filtered third depth feature, and the processed seismic wave amplitude signal, transmits the three-dimensional modeling data of the coalbed methane field geological structure to the three-dimensional imaging display module, and transmits the coalbed methane field geological evaluation index to the seismic acquisition control module. The first feedback data is obtained through the first depth feature, the second depth feature, and the filtered third depth feature and transmitted to the waveform feature extraction module. The second feedback data is obtained through the processed seismic wave amplitude signal and transmitted to the seismic acquisition control module. The seismic acquisition control module and the acquisition strategy optimization module perform data interaction to automatically generate an improved scheme for the data acquisition method.

[0022] As Figures 2 to 3As shown in the figure, this is the seismic waveform data graph and real-time environmental monitoring in the real-time monitoring panel interface of the three-dimensional seismic exploration data analysis system proposed by the present invention. The current time is displayed in the upper right corner of the interface, indicating that the system is in the real-time monitoring state. In this interface, there is a sensor display area that shows the operating conditions of several sensors in the form of a bar chart. The current display shows normal operation. The middle area presents the seismic waveform data, which can be updated in real time to reflect the currently monitored seismic wave conditions. A spectrum analysis chart is also provided in the interface to show the distribution characteristics of seismic waves in different frequency ranges. The schematic diagram of the three-dimensional geological model is also shown, visually presenting the underground stratigraphic structure. At the same time, the adaptive adjustment formula for geophone parameters is given, providing an intuitive basis for optimizing geophone parameters. The geological evaluation indicators of coalbed methane fields such as rich resources, energy storage permeability, gas saturation, and single-well production are listed, and suggestion information displays for relevant adjustment coefficients and azimuth adjustments are provided. The relevant formulas of the intelligent processing feedback mechanism (the calculation formulas for the first feedback and the second feedback data), as well as the formulas for weight adjustment and deviation calculation, etc., are provided, facilitating the operator to grasp the operating state of the sensors and the seismic waveform data in real time, and promptly discovering abnormal situations. Spectrum analysis helps to understand the spectral characteristics of seismic waves and provides a reference for analyzing the underground geological structure of coalbed methane fields. Through the three-dimensional geological model and the geophone parameter adjustment formula, combined with the evaluation indicators, the geophone parameters can be optimized according to the actual geological conditions of the coalbed methane field, improving the accuracy and effectiveness of data acquisition. The relevant formulas of the intelligent processing feedback mechanism can help the system make feedback adjustments according to the processing results, continuously optimizing the data processing and analysis process, and enhancing the reliability of the overall system and exploration results.

[0023] It should be noted that the seismic acquisition control module is geophones located at several underground positions and depths. The environmental recognition module analyzes the collected surface physical characteristic data to obtain the surface type characteristics. Combining the time-domain characteristics and frequency-domain characteristics of seismic waves, the surface type characteristics are further refined. The surface type characteristics are input into the preset explosive source parameter analysis formula to obtain the well depth and charge parameters of the explosive source suitable for the current environment. The preset explosive source parameter analysis formula is: ;

[0024] In the formula: are the explosive source parameters, including the well depth and charge of the explosive source, is the starting coefficient, When, is the charge of the explosive source, When, is the well depth of the explosive source. As Figure 3 shown, for the data of real-time environmental monitoring, the system proposed by the present invention respectively displays the function values of this formula in two artificially set modes. is the well depth analysis coefficient vector, , where , , are empirical weights, obtained based on the historical exploration practice experience in the current region and the data analysis and summary under different geological conditions, obtained from professional geological research institutions and the historical data analysis of seismic exploration projects, is the well depth analysis feature vector, , where , are the surface medium density and the propagation speed of seismic waves in the surface medium respectively. The surface medium density is obtained through geological exploration (measured at a set position underground using a density logging tool), and the propagation speed of seismic waves in the surface medium is obtained by collecting seismic wave signals with a geophone in the seismic acquisition control module, analyzing and processing the signals. The geophone records the time when the seismic wave propagates to different positions, and combines the known distance information to calculate using the wave speed calculation formula, is the main frequency of the seismic wave signal, which is obtained by analyzing the frequency spectrum of the seismic wave signal. The frequency component with the largest energy proportion is the main frequency of the seismic wave signal, is the charge amount analysis coefficient vector, , where , , are empirical weights, which are empirical values obtained based on past seismic exploration engineering practices, experimental data, and theoretical analysis, and are determined by experts in the industry based on actual case summaries, is the charge amount analysis feature vector, , is the explosive source, obtained by calculating the value at , is the maximum dimensionless amplitude of the seismic wave. The seismic wave signal is collected by a geophone in the seismic acquisition control module, and after signal amplification and filtering processing, the amplitude of the signal is analyzed to find the maximum dimensionless amplitude value, is the attenuation coefficient of the seismic wave during propagation. The seismic wave signals at different positions and depths are obtained through the seismic acquisition control module, and the attenuation coefficient is calculated by comparing the intensity changes of the signals at different positions. It can also be estimated by referring to the geological characteristics of the reference area and the seismic wave attenuation data under geological conditions with expected similarity in the past, is the constant term, which is a constant determined according to specific geological conditions, the characteristics of the exploration area, and past experience, and is a correction value set to make the formula calculation result more in line with the actual situation.

[0025] Based on a seismic acquisition control module (composed of geophones at different underground positions and depths), an environment recognition module, and a preset analysis formula for explosive source parameters, data is acquired through sensors and analyzed by the environment recognition module by combining the physical characteristic data of the surface with the time-domain and frequency-domain characteristics of seismic waves. The processing results are substituted into the formula, and factors such as the density of the surface medium (obtained through geological exploration) and the seismic wave propagation velocity (obtained by analyzing signals collected by sensors) are comprehensively considered to accurately calculate the well depth and charge parameters of the explosive source, thereby achieving the precise determination of parameters in seismic exploration, improving the exploration efficiency and quality, enhancing the adaptability of the system to different geological environments, achieving the efficient transfer of data from acquisition to analysis and application, and providing a scientific theoretical basis and practical guidance for seismic exploration.

[0026] It should be noted that in the waveform feature extraction module, the first depth feature includes but is not limited to the lithology change parameters of the shallow strata, the depth range of the stratigraphic interface, and the data on the influence of the shallow strata on seismic wave propagation. The second depth feature includes but is not limited to the thickness change parameters of the middle strata and the performance characteristics of the preset geological structure in the seismic wave signal. The third depth feature includes the lithology combination parameters of the deep strata and the deep geological structure parameters.

[0027] Dividing the data into the first depth feature (related to shallow strata), the second depth feature (related to middle strata), and the third depth feature (related to deep strata) for processing is based on the geological feature differences of strata at different depths and the requirements of seismic exploration. The technical effect is that for the characteristics that the shallow layer is greatly affected by human activities and weathering erosion, with frequent lithology changes, there are preset geological structures in the middle layer, and the lithology combination and deep geological structure in the deep layer are complex, appropriate algorithms and models are respectively adopted to improve the analysis pertinence. At the same time, computing resources are allocated according to needs, giving priority to processing the key information of the shallow layer, and then gradually delving into the middle and deep layers. While ensuring the analysis quality, the processing efficiency is improved, the cost is reduced, and after separate processing, the characteristic information of each depth stratum is integrated, enabling a comprehensive understanding of the underground geological structure, assisting in comprehensive geological assessment, and also helping to accurately analyze the propagation characteristics of seismic waves in each layer, and more accurately inversely analyze the underground geological structure in combination with the characteristics of each layer, improving the accuracy of seismic exploration, and providing a reliable basis for subsequent resource exploration, engineering construction, etc.

[0028] It should be noted that in the waveform feature extraction module, the shallow stratum, the middle stratum, and the deep stratum are the strata from 0 to 500 meters, the strata from 501 to 2000 meters, and the strata below 2000 meters respectively. The way that the first depth feature and the second depth feature help filter out invalid third depth features is as follows: based on existing geological knowledge and historical coalbed methane field exploration data, establish the corresponding relationships between the first depth feature, the second depth feature and the underground geological structure. Using these corresponding relationships, speculate on the reasonable range of signal features in the third depth feature under the premise of conforming to these corresponding relationships. Compare the third depth feature with the reasonable range speculated based on the first depth feature and the second depth feature, and determine the features in the third depth feature that exceed the reasonable range as invalid features, and filter out the invalid features in the third depth feature.

[0029] Filtering out the invalid values in the third depth feature using the first depth feature and the second depth feature is because there are internal connections among strata at different depths. Based on existing geological knowledge and historical coalbed methane field exploration data, establish the corresponding relationships between the first and second depth features and the underground geological structure, and use the features of the shallow (0 - 500 meters) and middle (501 - 2000 meters) strata to speculate on the reasonable signal range of the deep (below 2000 meters) strata. Since the deep data acquisition is easily interfered by various factors and generates abnormal values, filtering through comparison can eliminate invalid features, improve the accuracy of the third depth feature data, and provide a reliable basis for subsequent analysis; at the same time, the geological structure of the deep stratum is complex, the amount of collected data is large and contains a large amount of invalid information. Directly analyzing all the data will consume a large amount of computing resources and time. Using the features of the first two layers to filter out invalid values can reduce the amount of data, reduce the analysis dimension, improve the analysis efficiency, and make the waveform feature extraction module run more efficiently; in addition, accurate third depth feature data is of great significance for geological analysis and coalbed methane field exploration. Filtering out invalid values can avoid incorrect analysis results caused by invalid data, make the work of inferring geological structure, evaluating resource reserves, etc. based on reliable data more credible, contribute to making scientific and reasonable decisions, and reduce exploration risks and costs.

[0030] Example 2: As Figure 4As shown in the figure, the relevant content of the intelligent analysis interface in the system proposed by the present invention is shown. The waveform diagram of filtering recognition and analysis is shown in the figure, and the low-frequency noise, signal integrity, and phase interference information of AI anomaly detection are shown below the waveform diagram. The bar chart of seismic wave spectrum analysis is shown on the right side of the waveform diagram, and the main frequency, frequency band width, and energy concentration information are shown below the bar chart. The prediction analysis line chart of spectrum analysis is shown. In the data analysis module, the three-dimensional modeling data of the coalbed methane field geological structure includes, but is not limited to, formation layer data, fault data, fold data, and lithology data. The formation layer data includes the depth, thickness, and dip angle of each formation. The fault data includes the position, strike, and throw data of the fault. The fold data includes the shape, axis, and amplitude data of the fold. The lithology data includes the rock type, rock density, and seismic wave velocity through each formation of each formation.

[0031] It should be noted that in the data analysis module, the formation layer data is obtained through the analysis of the first depth feature, the second depth feature, and the filtered third depth feature. The fault data and fold data are identified through the analysis of the seismic wave amplitude signal. The lithology data is inferred through the relationship between the rock physical parameters and the seismic wave velocity and amplitude, combined with the formation layer data and the seismic wave amplitude signal.

[0032] It should be noted that in the data analysis module, the geological evaluation indicators of the coalbed methane field include the resource abundance, reservoir permeability, gas saturation, and single-well production prediction indicators of the coalbed methane field, which are obtained through the analysis of the machine learning model. The seismic acquisition control module receives the resource abundance, reservoir permeability, gas saturation, and single-well production prediction indicators of the coalbed methane field, and exchanges these data with the acquisition strategy optimization module.

[0033] It should be noted that in the acquisition strategy optimization module, the geophone spacing of each area is adjusted according to the resource abundance data. The preset geophone spacing adjustment formula is: ;

[0034] In the formula: is the index of the research area, is the adjusted spacing of the geophones in the th research area, is the first correction coefficient, is the resource abundance correlation coefficient, which is adjusted according to the relative size of the resource abundance of the coalbed methane field in the research area and the average resource abundance, is the average resource abundance; ;

[0035] In the formula: is the adjusted frequency response value of the geophone in the th research area, is the second correction coefficient, is the reservoir permeability correlation coefficient, which is adjusted according to the relative magnitude of the reservoir permeability of the coalbed methane field in the research area and the preset geophone response frequency, and is the preset geophone response threshold.

[0036] Adjust the geophone spacing according to the resource abundance data. Appropriately reduce the spacing in areas with high resource abundance to collect data more densely and capture more subtle changes in geological signals. For areas with complex coalbed methane field distributions, it can improve the recognition accuracy of their boundaries and internal structures; increase the spacing in areas with low resource abundance to avoid resource waste while ensuring the acquisition of key information. Adjust the geophone frequency response according to the reservoir permeability so that the geophone can more sensitively capture seismic wave signals related to the reservoir permeability, improve the detection ability of reservoir characteristics, and thus more accurately evaluate the exploitation potential and difficulty of the coalbed methane field. Reasonably adjust the geophone spacing and frequency response to avoid resource waste or insufficient exploration caused by using a unified spacing and frequency response setting in all areas. Through precise matching, reduce unnecessary equipment investment and data processing volume on the premise of meeting exploration requirements, improve exploration efficiency, and reduce exploration costs.

[0037] Specific implementation method: (1) Geophone spacing adjustment: First, determine the resource abundance data of each research area and calculate the average resource abundance.

[0038] Determine the resource abundance correlation coefficient according to the relative magnitude of the resource abundance of the coalbed methane field in the research area and the average resource abundance. If the resource abundance of a certain area is higher than the average resource abundance, the value is less than 1, and the geophone spacing in this area is reduced; if it is lower than the average resource abundance, the value is greater than 1, and the geophone spacing is increased.

[0039] Combined with the first correction coefficient, use the formula , calculate the adjusted spacing of the geophone in the

[0040] th research area, and arrange the geophones in this area according to the adjusted spacing. (2) Geophone frequency response adjustment:

[0041] Measure the reservoir permeability data of the coalbed methane field in each research area and determine the preset geophone response threshold.

[0042] Combined with the second correction coefficient, using the formula , calculate the adjusted frequency response value of the geophone in the th research area, and perform corresponding frequency response settings for the geophones in this area.

[0043] It should be noted that in the acquisition strategy optimization module, the geophone sensitivity is adjusted based on the gas saturation. The preset geophone sensitivity adjustment formula is: ;

[0044] In the formula: is the adjusted sensitivity of the geophone in the th research area, is the initial sensitivity, is the third correction coefficient, is the gas saturation correlation coefficient, which is related to the relative magnitude of the gas saturation in the current research area and the preset gas saturation threshold; Adjust the azimuth angle of the geophone according to the single-well production prediction index. When the change rate of the single-well prediction index affected by the reservoir permeability in one direction exceeds the preset threshold, take this direction as the key monitoring direction and adjust the azimuth angle of the geophone to face the key monitoring direction.

[0045] Adjusting the geophone sensitivity based on the gas saturation can make the geophone's signal response to different gas saturation areas more accurate. In areas with high gas saturation, increasing the geophone sensitivity can more clearly capture seismic wave signals related to coalbed methane and effectively identify subtle changes in gas-bearing characteristics; in areas with low gas saturation, the sensitivity is adjusted accordingly to avoid interference from invalid signals, thereby improving the pertinence and effectiveness of data acquisition and providing more accurate data for subsequent coalbed methane field analysis. Adjusting the azimuth angle of the geophone according to the single-well production prediction index can focus on the reservoir direction that has the greatest impact on the single-well production. When the change rate of the reservoir permeability in a certain direction affecting the single-well prediction index exceeds the preset threshold, adjusting the azimuth angle of the geophone to this direction can more intensively monitor the key reservoir area, obtain more abundant effective information, help more accurately evaluate the single-well production and exploitation potential, enhance the exploration effect, and provide strong support for the development of the coalbed methane field.

[0046] Specific implementation method: (1) Geophone sensitivity adjustment: First, determine the gas saturation data of the current research area and the preset gas saturation threshold.

[0047] According to the relative magnitude of the gas saturation in the research area and the preset threshold, determine the gas saturation correlation coefficient. If the gas saturation is higher than the preset threshold, take a value greater than 1. At this time, in the formula Among them, the adjusted sensitivity of the geophone will be appropriately reduced; if the gas saturation is lower than the preset threshold and the value is less than 1, the adjusted sensitivity of the geophone will be correspondingly increased.

[0048] Combined with the third correction coefficient and the preset initial sensitivity, calculate the adjusted sensitivity of the geophone in the nth research area through the formula, and set the sensitivity of the geophone in this area.

[0049] (2) Geophone azimuth adjustment: Calculate the single-well production prediction index, and analyze the change rate of the influence of reservoir permeability in different directions on the single-well prediction index.

[0050] Compare the change rates in each direction with the preset threshold, find the directions where the change rate exceeds the preset threshold, and determine them as the key monitoring directions.

[0051] According to the determined key monitoring directions, adjust the azimuth of the geophone so that it faces the key monitoring directions, thereby realizing the effective monitoring of the key reservoir areas.

[0052] It should be noted that in the waveform feature extraction module, the first feedback data is the deviation value between the processing result of the seismic wave first arrival time signal and the standard result. According to this deviation value, the weights and thresholds of the multi-layer perceptron are adjusted. In the seismic acquisition control module, the second feedback data is the matching degree between the processed seismic wave amplitude signal and the expected geological structure and lithology characteristics. Based on the preset fuzzy rules, the first correction value, the second correction value, the third correction value for adjusting the distribution spacing, frequency response, sensitivity and azimuth of the geophone are generated.

[0053] Taking the deviation value between the processing result of the seismic wave first arrival time signal and the standard result as the first feedback data to adjust the weights and thresholds of the multi-layer perceptron can enable the multi-layer perceptron to continuously optimize its own parameters according to the difference between the actual and the standard, improve the processing accuracy of the seismic wave first arrival time signal, and then more accurately analyze the propagation characteristics of the seismic wave, providing a more reliable data basis for subsequent geological structure inference. Using the matching degree between the processed seismic wave amplitude signal and the expected geological structure and lithological characteristics as the second feedback data, and generating correction values for the distribution spacing, frequency response, sensitivity and azimuth angle of the geophones based on preset fuzzy rules can enable the parameter configuration of the geophones to be dynamically adjusted according to the difference between the actual geological situation and the expectation, so as to more accurately collect seismic wave signals, adapt to the exploration requirements under different geological conditions, and improve the quality and effectiveness of data collection. Through the utilization of feedback data and parameter adjustment, both the multi-layer perceptron of the waveform feature extraction module and the geophones of the seismic acquisition control module can maintain good working conditions under different geological conditions, enhance the adaptability of the entire system to the complex and changeable geological environment, improve the efficiency and accuracy of seismic exploration work, and provide more reliable support for geological analysis and resource exploration.

[0054] The calculation formula for the adjustment parameters of the above geophones is shown in Figure 2 .

[0055] As Figure 5 shown, it is the 3D imaging interface of the system proposed by the present invention. The 3D model preview part presents a 3D geological structure model with rich colors and distinct layers. The model accuracy is 0.1 m, including 12 horizons, and the coverage range reaches 5 km 2 , and the modeling takes 2.5 h; on the right side of the interface, there is a comparison chart of the real-time waveform and the standard waveform, and the waveform difference can be visually viewed; below the interface, data such as the waveform matching degree reaching 92%, the signal-to-noise ratio being 18.5 dB, and the recognition accuracy rate being 95% are displayed, reflecting the data quality situation; the intelligent analysis report evaluates the data quality, points out that the overall quality of the currently collected seismic data is good, the signal-to-noise ratio is maintained above 18.5 dB, the waveform recognition accuracy rate reaches 95%, the energy distribution of the spectrum analysis is concentrated and reasonable, and it is recommended to maintain the current acquisition parameter settings. At the same time, low-frequency noise and phase interference anomalies are detected. The low-frequency noise is concentrated in the 5 - 15 Hz frequency band, and it is recommended to adjust the low-pass filtering parameters. The phase interference may be related to the equipment jitter, and it is recommended to check the equipment fixation status. Optimization suggestions such as increasing the sampling points to improve the data resolution, adjusting the gain control parameters to optimize the signal amplitude range, and collecting data when the background noise is lower during the period from 14:00 to 16:00 are also given; in addition, parameters such as the sampling interval being 0.5, the recording length being 6000, the low-pass filtering being 5, the AGC window length being 500, and the stacking times being 48 can be set in the system configuration interface, and there are also advanced parameter setting options such as automatic noise suppression and frequency compensation.

[0056] In summary, the present invention uses an environment recognition module to intelligently identify different surface types in bedrock outcrop areas and farmland sections. By presetting formulas, it accurately adjusts the well depth and charge parameters of the explosive source, reduces the interference of seismic wave propagation, and improves the signal quality. The waveform feature extraction module uses a multi-layer perceptron and specific algorithms to accurately pick up the depth features of the first arrival time signal of seismic waves, filter out invalid information, and can also effectively process the amplitude signal of seismic waves, improving the accuracy and effectiveness of the data. The data analysis module, based on the processed data, obtains three-dimensional modeling data of the geological structure of the coalbed methane field, covering information on strata, faults, folds, and lithology. At the same time, geological evaluation indicators such as resource abundance and reservoir permeability are obtained through a machine learning model, comprehensively reflecting the geological characteristics of the coalbed methane field. According to the geological evaluation indicators and feedback data, the acquisition method is automatically optimized, improving the pertinence and accuracy of data acquisition, forming a virtuous cycle of data acquisition and analysis, effectively solving the problems faced by traditional exploration methods in complex coalbed methane fields, reducing manual intervention, improving data accuracy and model accuracy, providing a reliable decision-making basis for the exploration and development of coalbed methane fields, promoting the efficient development of coalbed methane field exploration work, and reducing exploration costs and risks.

[0057] The above is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

[0058] Finally, the above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A three-dimensional seismic exploration data analysis system for coalbed methane fields, comprising a seismic acquisition control module, a geological modeling analysis module, a data analysis module, a three-dimensional imaging display module, an environment recognition module and a acquisition strategy optimization module, characterized in that, The environmental recognition module recognizes the surface environment, analyzes the surface type and seismic wave signals. The seismic acquisition control module transmits the seismic wave first arrival time signal and amplitude signal to the geological modeling and analysis module and the waveform feature extraction module respectively. The geological modeling and analysis module corrects the seismic wave first arrival time signal and transmits the corrected signal to the waveform feature extraction module. The waveform feature extraction module picks up the first depth feature, the second depth feature, and the third depth feature in the first arrival time signal, filters out the invalid features of the third depth feature using the first depth feature and the second depth feature, and then transmits the data to the data analysis module. The waveform feature extraction module obtains the frequency domain signal of the seismic wave amplitude using the short-time Fourier transform, corrects the amplitude frequency domain signal using the formation absorption attenuation model, converts the frequency domain signal back to the time domain using the inverse short-time Fourier transform, and transmits it to the data analysis module. The data analysis module analyzes the received data to obtain the 3D modeling data of the coalbed methane field geological structure and the geological evaluation index, and transmits the two to the 3D imaging display module and the seismic acquisition control module respectively. At the same time, it obtains the first feedback data through the first depth feature, the second depth feature, and the filtered third depth feature, and then transmits it to the waveform feature extraction module, obtains the second feedback data through the amplitude signal, and then transmits it to the seismic acquisition control module. The seismic acquisition control module interacts with the acquisition strategy optimization module to automatically generate an improved scheme for the data acquisition method.

2. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 1, characterized in that, The seismic acquisition control module is geophones located at several underground positions and depths. The environmental recognition module analyzes the collected surface physical feature data, obtains the surface type features, combines the time domain features and frequency domain features of the seismic waves, further refines the surface type features, and inputs the surface type features into a preset explosive source parameter analysis formula to obtain the well depth and charge parameters of the explosive source suitable for the current environment. The preset explosive source parameter analysis formula is: ; Wherein: is the explosive source parameter, including the well depth and charge amount of the explosive source, is the starting coefficient, When is the charge amount of the explosive source, When is the well depth of the explosive source, is the well depth analysis coefficient vector, , where , , are the empirical weights, is the well depth analysis feature vector, , where , are respectively the surface medium density and the propagation speed of seismic waves in the surface medium, is the main frequency of the seismic wave signal, is the charge amount analysis coefficient vector, , where , , are the empirical weights, is the charge amount analysis feature vector, , is the explosive source, obtained by calculating the value at , is the maximum dimensionless amplitude of the seismic wave, is the attenuation coefficient of the seismic wave during propagation, is the constant term.

3. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 1, characterized in that, In the waveform feature extraction module, the first depth feature includes but is not limited to the lithology change parameters of the shallow formation, the depth range of the formation interface, and the data on the influence of the shallow formation on the propagation of seismic waves. The second depth feature includes but is not limited to the thickness change parameters of the middle formation and the manifestation characteristics of the preset geological structure in the seismic wave signal. The third depth feature includes the lithology combination parameters of the deep formation and the deep geological structure parameters.

4. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 3, characterized in that, In the waveform feature extraction module, the shallow formation, the middle formation, and the deep formation are the formations from 0 to 500 meters, the formations from 501 to 2000 meters, and the formations below 2000 meters deep respectively. The way the first depth feature and the second depth feature help filter out the invalid third depth features is as follows: Based on the existing geological knowledge and historical coalbed methane field exploration data, establish the corresponding relationship between the first depth feature, the second depth feature, and the underground geological structure. Using this corresponding relationship, infer the reasonable range of the signal features in the third depth feature under the premise of conforming to this corresponding relationship. Compare the third depth feature with the reasonable range inferred from the first depth feature and the second depth feature, determine the features in the third depth feature that exceed the reasonable range as invalid features, and filter out the invalid features in the third depth feature.

5. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 1, characterized in that, In the data analysis module, the 3D modeling data of the coalbed methane field geological structure includes but is not limited to formation surface data, fault data, fold data, and lithology data. The formation surface data includes the depth, thickness, and dip angle of each formation. The fault data includes the location, strike, and throw data of the faults. The fold data includes the shape, axial direction, and amplitude data of the folds. The lithology data includes the rock type, rock density, and seismic wave velocity passing through each formation of each formation.

6. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 5, characterized in that, In the data analysis module, the formation surface data is obtained through the analysis of the first depth feature, the second depth feature, and the filtered third depth feature. The fault data and fold data are identified through the analysis of the seismic wave amplitude signal. The lithology data is inferred through the relationship between the rock physical parameters and the seismic wave velocity and amplitude, combined with the formation surface data and the seismic wave amplitude signal.

7. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 2, characterized in that, In the data analysis module, the geological evaluation indicators of the coalbed methane field include the resource abundance, reservoir permeability, gas saturation, and single well production prediction index of the coalbed methane field, which are obtained through the analysis of the machine learning model. The seismic acquisition control module receives the resource abundance, reservoir permeability, gas saturation, and single well production prediction index of the coalbed methane field, and exchanges these data with the acquisition strategy optimization module.

8. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 7, characterized in that, In the acquisition strategy optimization module, the geophone spacing of each area is adjusted according to the resource abundance data. The preset geophone spacing adjustment formula is: ; wherein: is the index of the study area, is the adjusted spacing of the geophones in the th study area, is the first correction coefficient, is the resource abundance correlation coefficient, which is adjusted according to the relative magnitude of the resource abundance of the coalbed methane field in the study area and the average resource abundance, is the average resource abundance; The geophone frequency response is adjusted according to the reservoir permeability. The preset geophone frequency response adjustment formula is: ; where: is the adjusted frequency response value of the geophone in the th research area, is the second correction coefficient, is the reservoir permeability correlation coefficient, which is adjusted according to the relative magnitude of the reservoir permeability of the coalbed methane field in the research area and the preset response frequency of the geophone, the preset response threshold of the geophone.

9. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 8, characterized in that, In the acquisition strategy optimization module, the geophone sensitivity is adjusted based on the gas saturation. The preset geophone sensitivity adjustment formula is: ; where: is the adjusted sensitivity of the geophone in the th research area, is the initial sensitivity, is the third correction coefficient, is the gas saturation correlation coefficient, which is related to the relative magnitude of the gas saturation in the current research area and the preset threshold of gas saturation; The geophone azimuth angle is adjusted according to the single well production prediction index. When the change rate of the single well prediction index affected by the reservoir permeability in one direction exceeds the preset threshold, this direction is taken as the key monitoring direction, and the azimuth angle of the geophone is adjusted to face the key monitoring direction.

10. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 9, characterized in that, In the waveform feature extraction module, the first feedback data is the deviation value between the processing result of the seismic wave first arrival time signal and the standard result. The weights and thresholds of the multi-layer perceptron are adjusted according to this deviation value. In the seismic acquisition control module, the second feedback data is the matching degree between the processed seismic wave amplitude signal and the expected geological structure and lithology characteristics. The first correction value, the second correction value, the third correction value for adjusting the geophone distribution spacing, frequency response, sensitivity, and geophone azimuth angle are generated based on the preset fuzzy rules.

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