A 3D seismic exploration data analysis system for coalbed methane fields

Abstract

The present invention relates to the field of data analysis technology, and specifically discloses a three-dimensional seismic exploration data analysis system for coalbed methane fields. The system is used to solve the problems encountered when using three-dimensional seismic exploration to ascertain structural development and coal seam occurrence, such as complex ground environment, interference of different surface conditions with seismic wave signals, difficulty in accurately obtaining the first arrival time and amplitude information of the wave due to multiple layers and large variations in burial depth, and low signal-to-noise ratio affecting data accuracy. The system comprises a seismic acquisition control module, a geological modeling and analysis module, a data analysis module, a three-dimensional imaging display module, an environment recognition module, and an acquisition strategy optimization module. The system utilizes the environment recognition module to identify the surface type, accurately adjust the explosive source parameters, and improve signal quality. The waveform feature extraction module processes the seismic wave signal to improve data accuracy. The data analysis module obtains modeling data and evaluation indicators, and optimizes 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 sections, weathered material areas and gully sections, different surface conditions cause severe 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, improves the signal quality, a waveform feature extraction module processes the seismic wave signals, improves the data accuracy, and a data analysis module obtains modeling data and evaluation indicators, optimizes the acquisition method, solves traditional exploration problems, and facilitates the exploration of coalbed methane fields.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] 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 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 through 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 through 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.

[0006] 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:

[0007] ;

[0008] In the formula: is the explosive source parameter, 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, is the well depth analysis coefficient vector, , where , , are the empirical weights, is the well depth analysis eigenvector, ,in 、 are 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 dose analysis coefficient vector, ,in 、 、 is the experience weight, is the dose analysis feature vector, , As the source of explosives, Calculated when The value is obtained, is the maximum dimensionless amplitude of the seismic wave, is the attenuation coefficient of seismic waves during propagation, is a constant term.

[0009] 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 lithologic change parameters of the shallow strata, the depth range of the stratum interface, and the impact data of the shallow strata on the propagation of seismic waves; the second depth feature includes but is not limited to the thickness change parameters of the middle strata and the manifestation characteristics of the preset geological structure in the seismic wave signal; the third depth feature includes the lithologic combination parameters of the deep strata and the deep geological structure parameters.

[0010] As a further technical solution of the present invention, in the waveform feature extraction module, the shallow strata, the middle strata and the deep strata are respectively 0-500 meters, 501-2000 meters and strata below 2000 meters in depth. The first depth feature and the second depth feature help to filter out invalid third depth features by establishing a correspondence between the first depth feature, the second depth feature and the underground geological structure based on existing geological knowledge and historical coalbed methane field exploration data. By using this correspondence, a reasonable range of signal features in the third depth feature under the premise of meeting this correspondence is inferred. The third depth feature is compared with the reasonable range inferred based on the first depth feature and the second depth feature, and the features in the third depth feature that exceed the reasonable range are judged as invalid features, and the invalid features in the third depth feature are filtered out.

[0011] As a further technical solution of the present invention, in the data analysis module, the three-dimensional modeling data of the geological structure of the coalbed methane field includes but is not limited to stratigraphic data, fault data, fold data and lithologic data. The stratigraphic data includes the depth, thickness and inclination of each stratum, the fault data includes the position, strike and drop data of the fault, the fold data includes the shape, axis and amplitude data of the fold, and the lithologic data includes the rock type, rock density and wave velocity of the seismic wave passing through each stratum.

[0012] As a further technical solution of the present invention, in the data analysis module, stratigraphic data are obtained by analyzing the first depth feature, the second depth feature and the filtered third depth feature, fault data and fold data are identified by analyzing the seismic wave amplitude signal, and lithologic data are inferred by combining the stratigraphic data and the seismic wave amplitude signal through the relationship between rock physical parameters and seismic wave velocity and amplitude.

[0013] As a further technical solution of the present invention, 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 machine learning model analysis. 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 interacts these data with the acquisition strategy optimization module.

[0014] As a further technical solution of the present invention, in the acquisition strategy optimization module, the detector spacing of each area is adjusted according to the resource abundance data. The preset detector spacing adjustment formula is:

[0015] ;

[0016] Where: is the index of the study area, For the The adjusted spacing of the detectors in the study area is 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 CBM fields in the study area and the average resource abundance. is the average resource abundance;

[0017] The detector frequency response is adjusted according to the reservoir permeability. The preset detector frequency response adjustment formula is:

[0018] ;

[0019] Where: For the The adjusted frequency response value of the detector in the study area, is the second correction coefficient, is the reservoir permeability correlation coefficient, which is adjusted according to the relative size of the reservoir permeability of the coalbed methane field in the study area and the preset detector response frequency. Preset detector response threshold.

[0020] As a further technical solution of the present invention, in the acquisition strategy optimization module, the detector sensitivity is adjusted based on the gas saturation. The preset detector sensitivity adjustment formula is:

[0021] ;

[0022] Where: For the The adjusted sensitivity of the detectors in the study area is is the initial sensitivity, is the third correction coefficient, is the gas saturation correlation coefficient, which is based on the relative size of the gas saturation in the current study area and the preset gas saturation threshold;

[0023] The detector azimuth 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 used as the key monitoring direction, and the detector azimuth is adjusted to face the key monitoring direction.

[0024] 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 seismic wave first arrival time signal processing result and the standard result, and the weight and threshold of the multi-layer perceptron are adjusted according to the deviation value. In the seismic acquisition control module, the second feedback data is the degree of matching between the processed seismic wave amplitude signal and the expected geological structure and lithological characteristics. Based on the preset fuzzy rules, the first correction value, the second correction value, and the third correction value for adjusting the detector distribution spacing, frequency response, sensitivity and detector azimuth are generated.

[0025] The technical effects of the three-dimensional seismic exploration data analysis system for coalbed methane fields proposed by the present invention are as follows:

[0026] Through the environmental recognition module, the present invention intelligently recognizes different surface types such as bedrock outcrop areas and farmland sections, precisely adjusts the well depth and charge parameters of the explosive source through a preset formula, reduces the interference of seismic wave propagation, improves 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, filters out invalid information, and can also effectively process the seismic wave amplitude signal, improving the accuracy and effectiveness of the data. The data analysis module, based on the processed data, obtains three-dimensional modeling data of the coalbed methane field geological structure, covering information on strata, faults, folds, and lithology. At the same time, through a machine learning model, geological evaluation indicators such as resource abundance and reservoir permeability are obtained, 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

[0027] Figure 1 is the system block diagram of the present invention;

[0028] Figure 2 is the real-time update diagram of the seismic waveform data of the present invention;

[0029] Figure 3 is the real-time environmental monitoring curve diagram of the present invention;

[0030] Figure 4 is the waveform recognition and analysis diagram of the present invention;

[0031] Figure 5 is the three-dimensional model preview diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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 of 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.

[0033] 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 out 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 geological structure of the coalbed methane field and the geological evaluation index of the coalbed methane field 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 geological structure of the coalbed methane field to the three-dimensional imaging display module, and transmits the geological evaluation index of the coalbed methane field 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.

[0034] As Figures 2 to 3As shown, 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 for coalbed methane fields proposed by the present invention are shown. The current time is displayed in the upper right corner of the interface, indicating that the system is in real-time monitoring status. In this interface, there is a sensor display area, which displays the operating conditions of several sensors in the form of a bar graph. The current display is all operating normally. The middle area presents seismic waveform data, which can be updated in real time to reflect the current monitored seismic wave conditions. A spectrum analysis chart is provided in the interface to show the distribution characteristics of seismic waves in different frequency ranges. A schematic diagram of the three-dimensional geological model is also shown to intuitively present the underground stratum structure. At the same time, a formula for adaptive adjustment of detector parameters is given to provide an intuitive basis for optimizing detector parameters, and the rich resources, energy storage permeability, gas saturation, and single well production are listed. The geological evaluation indicators of coalbed methane fields are displayed, and suggestions are provided for the relevant adjustment coefficients and azimuth adjustments. The relevant formulas of the intelligent processing feedback mechanism (the calculation formulas of the first feedback and second feedback data), as well as the weight adjustment and deviation calculation formulas are exposed to facilitate operators to grasp the sensor operation status and seismic waveform data in real time and detect abnormal situations in time. Spectral analysis helps to understand the characteristics of the seismic wave spectrum and provides a reference for analyzing the underground geological structure of coalbed methane fields. Through the three-dimensional geological model and the detector parameter adjustment formula, combined with the evaluation indicators, the detector parameters can be optimized according to the actual geological conditions of the coalbed methane field, and the accuracy and effectiveness of data acquisition can be improved. The relevant formulas of the intelligent processing feedback mechanism can help the system to make feedback adjustments based on the processing results, continuously optimize the data processing and analysis process, and improve the reliability of the overall system and exploration results.

[0035] It should be noted that the seismic acquisition control module is a detector located at several locations and depths underground. The environment recognition module analyzes the collected surface physical characteristic data to obtain surface type characteristics. Combined with the time domain characteristics and frequency domain characteristics of the 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 explosive source well depth and charge parameters suitable for the current environment. The preset explosive source parameter analysis formula is:

[0036] ;

[0037] Where: are the explosive source parameters, including the depth and charge of the explosive source well. is the starting coefficient, hour, is the explosive source charge, hour, is the depth of the explosive source well, such as Figure 3 As shown, the system proposed by the present invention can monitor the data of real-time environment and display the function value of the formula in two manual setting modes respectively. is the well depth analysis coefficient vector, , where , , are empirical weights, which are obtained based on the historical exploration practice experience of the current region and the data analysis and summary under different geological conditions, and are 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 and then analyzing and processing the signals. The geophone records the time when the seismic waves propagate 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, which is obtained by calculating the value at , is the maximum dimensionless amplitude of the seismic wave, which is obtained by the geophone in the seismic acquisition control module collecting the seismic wave signal, amplifying and filtering the signal, and then analyzing the amplitude of the signal to find the maximum dimensionless amplitude value, is the attenuation coefficient of the seismic wave during propagation, which is obtained by the seismic acquisition control module acquiring seismic wave signals at different positions and depths, and calculating the attenuation coefficient 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.

[0038] Based on the seismic acquisition control module (composed of detectors at different underground locations and depths), the environment identification module, and the preset explosive source parameter analysis formula, data is acquired through sensors and the surface physical characteristic data is analyzed and processed by the environment identification module in combination with the time domain and frequency domain characteristics of the seismic wave. The processing results are substituted into the formula, and factors such as surface medium density (obtained through geological surveys) and seismic wave propagation speed (obtained by sensor acquisition and analysis signals) are comprehensively considered to accurately calculate the well depth and charge parameters of the explosive source, thereby achieving accurate determination of parameters in seismic exploration, improving exploration efficiency and quality, enhancing the system's adaptability to different geological environments, and achieving efficient flow of data from acquisition to analysis and application, providing a scientific theoretical basis and practical guidance for seismic exploration.

[0039] It should be noted that in the waveform feature extraction module, the first depth feature includes but is not limited to the lithologic change parameters of shallow strata, the depth range of the stratum interface, and the impact data of shallow strata on seismic wave propagation; the second depth feature includes but is not limited to the thickness change parameters of middle strata and the manifestation characteristics of preset geological structures in seismic wave signals; the third depth feature includes the lithologic combination parameters of deep strata and deep geological structure parameters.

[0040] The data is divided into the first depth characteristics (related to shallow strata), the second depth characteristics (related to middle strata), and the third depth characteristics (related to deep strata) for processing. This is based on the differences in geological characteristics of strata at different depths and the needs of seismic exploration. Its technical effect is that it can use appropriate algorithms and models to improve the targeted analysis of the shallow layer, which is greatly affected by human activities and weathering and erosion, and has frequent lithologic changes, the middle layer has preset geological structures, and the deep layer has complex lithologic combinations and deep geological structures. At the same time, computing resources are allocated on demand, prioritizing the processing of key shallow information, and then going deeper into the middle and deep layers. While ensuring the quality of analysis, it also improves processing efficiency and reduces costs. Integrating the characteristic information of strata at each depth after separate processing can fully understand the underground geological structure, assist in comprehensive geological assessment, and also help to accurately analyze the propagation characteristics of seismic wave signals in each layer. Combining the characteristics of each layer can more accurately invert and analyze the underground geological structure, improve the accuracy of seismic exploration, and provide a reliable basis for subsequent resource exploration, engineering construction, etc.

[0041] It should be noted that in the waveform feature extraction module, the shallow strata, the middle strata and the deep strata are respectively 0-500 meters, 501-2000 meters and strata below 2000 meters in depth. The first depth feature and the second depth feature help to filter out invalid third depth features by establishing a correspondence between the first depth feature, the second depth feature and the underground geological structure based on existing geological knowledge and historical coalbed methane field exploration data. By using this correspondence, a reasonable range of signal features in the third depth feature is inferred under the premise of meeting this correspondence. The third depth feature is compared with the reasonable range inferred based on the first depth feature and the second depth feature, and the features in the third depth feature that exceed the reasonable range are judged as invalid features, and the invalid features in the third depth feature are filtered out.

[0042] The invalid values in the third depth feature are filtered out by using the first and second depth features because there is an intrinsic connection between strata at different depths. Based on existing geological knowledge and historical coalbed methane field exploration data, the corresponding relationship between the first and second depth features and the underground geological structure is established. The shallow (0-500 meters) and middle (501-2000 meters) stratum characteristics are used to infer the reasonable signal range of the deep layer (below 2000 meters). Since deep data acquisition is easily interfered by various factors and produces abnormal values, invalid features can be eliminated through comparison and filtering, thereby improving the accuracy of the third depth feature data and providing a reliable basis for subsequent analysis. The geological structure of deep strata is complex, the amount of collected data is large and contains a lot of invalid information. Directly analyzing all the data will consume a lot of computing resources and time. Using the first two layers of features to filter invalid values can reduce the amount of data, reduce the analysis dimension, improve analysis efficiency, and make the waveform feature extraction module run more efficiently. In addition, accurate third-depth feature data is of great significance to geological analysis and coalbed methane field exploration. Filtering invalid values can avoid erroneous analysis results due to invalid data, making geological structure inference, resource reserve assessment and other work based on reliable data more credible, helping to make scientific and reasonable decisions and reduce exploration risks and costs.

[0043] Example 2: 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 figure shows the waveform of the filtering identification analysis, and the low-frequency noise, signal integrity and phase interference information of the AI anomaly detection are displayed below the waveform. The seismic wave spectrum analysis bar graph is displayed on the right side of the waveform, and the main frequency, bandwidth and energy concentration information are displayed below the bar graph. A predictive analysis line graph of the spectrum analysis is displayed. In the data analysis module, the three-dimensional modeling data of the geological structure of the coalbed methane field includes but is not limited to stratigraphic level data, fault data, fold data and lithological data. The stratigraphic level data includes the depth, thickness and inclination of each stratum, the fault data includes the position, strike and drop data of the fault, the fold data includes the shape, axis and amplitude data of the fold, and the lithological data includes the rock type, rock density and wave velocity of the seismic wave passing through each stratum.

[0044] It should be noted that in the data analysis module, stratigraphic data are obtained through analysis of the first depth feature, the second depth feature and the filtered third depth feature, fault data and fold data are identified through analysis of the seismic wave amplitude signal, and lithologic data are inferred through the relationship between rock physical parameters and seismic wave velocity and amplitude, combined with stratigraphic data and seismic wave amplitude signals.

[0045] It should be noted that in the data analysis module, the geological evaluation indicators of coalbed methane fields include the resource abundance, reservoir permeability, gas saturation and single-well production prediction indicators of the coalbed methane fields, which are obtained through machine learning model analysis. The seismic acquisition control module receives the resource abundance, reservoir permeability, gas saturation and single-well production prediction indicators of the coalbed methane fields, and interacts these data with the acquisition strategy optimization module.

[0046] It should be noted that in the acquisition strategy optimization module, the detector spacing of each area is adjusted according to the resource abundance data. The preset detector spacing adjustment formula is:

[0047] ;

[0048] Where: is the index of the study area, For the The adjusted spacing of the detectors in the study area is 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 CBM fields in the study area and the average resource abundance. is the average resource abundance;

[0049] The detector frequency response is adjusted according to the reservoir permeability. The preset detector frequency response adjustment formula is:

[0050] ;

[0051] Where: For the The adjusted frequency response value of the detector in the study area, is the second correction coefficient, is the reservoir permeability correlation coefficient, which is adjusted according to the relative size of the reservoir permeability of the coalbed methane field in the study area and the preset detector response frequency. Preset detector response threshold.

[0052] Adjusting the detector spacing based on resource abundance data, appropriately reducing the spacing in areas with high resource abundance, enables more intensive data collection and captures subtler geological signal changes. This improves the accuracy of identifying the boundaries and internal structure of CBM fields with complex distribution. Increasing the spacing in areas with low resource abundance avoids resource waste while ensuring the acquisition of critical information. Adjusting the detector frequency response based on reservoir permeability allows the detector to more sensitively capture seismic wave signals related to reservoir permeability, improving the ability to detect reservoir characteristics and thus more accurately assessing the recovery potential and difficulty of CBM fields. Reasonable adjustment of the detector spacing and frequency response avoids the use of uniform spacing and frequency response settings across all areas, which can lead to resource waste or insufficient exploration. Through precise matching, while still meeting exploration needs, unnecessary equipment investment and data processing can be reduced, improving exploration efficiency and reducing exploration costs.

[0053] Specific implementation method:

[0054] (1) Detector spacing adjustment:

[0055] First, the resource abundance data of each study area was determined and the average resource abundance was calculated.

[0056] The resource abundance correlation coefficient is determined based on the relative abundance of the CBM fields in the study area compared to the average resource abundance. If the resource abundance in a region is higher than the average resource abundance, the value is less than 1, and the detector spacing in that region is reduced. If the resource abundance is lower than the average resource abundance, the value is greater than 1, and the detector spacing is increased.

[0057] Combined with the first correction coefficient, using the formula , calculate the adjusted spacing of the detectors in the study area, and arrange the detectors in the area according to the adjusted spacing.

[0058] (2) Detector frequency response adjustment:

[0059] The reservoir permeability data of the coalbed methane fields in each study area were measured to determine the preset detector response threshold.

[0060] Adjust the correlation coefficient of reservoir permeability according to the relative magnitude between the reservoir permeability of the coalbed methane field in the research area and the preset response frequency of the geophone. If the reservoir permeability is high, it is necessary to increase the frequency response of the geophone, and the value is greater than 1; conversely, if the reservoir permeability is low, the value is less than 1.

[0061] Combined with the second correction coefficient, use the formula , calculate the adjusted frequency response value of the geophone in the th research area, and perform corresponding frequency response settings on the geophone in this area.

[0062] 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:

[0063] ;

[0064] 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 between the gas saturation of the current research area and the preset gas saturation threshold;

[0065] 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.

[0066] 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 on 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 to 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.

[0067] Specific implementation method:

[0068] (1) Geophone sensitivity adjustment:

[0069] First, determine the gas saturation data and the preset threshold of gas saturation in the current research area.

[0070] According to the relative magnitude between the gas saturation in the research area and the preset threshold, determine the correlation coefficient of gas saturation. If the gas saturation is higher than the preset threshold, the value is greater than 1. At this time, in the formula , the adjusted sensitivity of the geophone will be appropriately reduced; if the gas saturation is lower than the preset threshold, the value is less than 1, and the adjusted sensitivity of the geophone will be correspondingly increased.

[0071] Combined with the third correction coefficient and the preset initial sensitivity, calculate the adjusted sensitivity of the geophone in the th research area through the formula, and set the sensitivity of the geophone in this area.

[0072] (2) Geophone azimuth adjustment:

[0073] 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.

[0074] Compare the change rates in each direction with the preset threshold, find out the directions where the change rate of influence exceeds the preset threshold, and determine them as the key monitoring directions.

[0075] According to the determined key monitoring directions, adjust the azimuth of the geophone so that it faces the key monitoring directions, thereby realizing effective monitoring of the key reservoir areas.

[0076] 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.

[0077] 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 geophone based on preset fuzzy rules can enable the parameter configuration of the geophone 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 geophone of the seismic acquisition control module can maintain a good working state 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.

[0078] The calculation formula for the adjustment parameters of the above geophone is shown in Figure 2 .

[0079] As Figure 5 shown, it is the 3D imaging interface of the system proposed by the present invention, where the 3D model preview part presents a 3D geological structure model with rich colors and distinct layers, the model accuracy is 0.1m, it contains 12 horizons, and the coverage range reaches 5km 2 , and the modeling takes 2.5h; on the right side of the interface, there is a comparison chart of the real-time waveform and the standard waveform, through which 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 18.5dB, 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 18.5dB, the waveform recognition accuracy rate reaches 95%, the energy distribution of the spectrum analysis is concentrated and reasonable, and it is recommended to keep 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 - 15Hz frequency band, and it is recommended to adjust the low-pass filter 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 record length being 6000, the low-pass filter 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.

[0080] In summary, the present invention intelligently identifies different surface types such as bedrock outcrop areas and farmland sections through the environmental recognition module, accurately adjusts the well depth and charge parameters of the explosive source through a preset formula, reduces the interference of seismic wave propagation, improves 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, filters out invalid information, and can also effectively process the seismic wave amplitude signal, improving the accuracy and effectiveness of the data. The data analysis module obtains three-dimensional modeling data of the geological structure of the coalbed methane field based on the processed data, 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.

[0081] 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.

[0082] 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 collection 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 the 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 three-dimensional modeling data of the coalbed methane field geological structure 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 through 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 through 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.

2. The three-dimensional seismic exploration data analysis system for coalbed methane fields according to claim 1, wherein 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 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: ; where: is the explosive source parameter, including the well depth and charge amount of the explosive source, is the activation 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 the density of the surface medium and the propagation speed of seismic waves in the surface medium respectively, 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. A three-dimensional seismic exploration data analysis system for a coalbed methane field according to claim 1, wherein 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.

4. The three-dimensional seismic exploration data analysis system for a coalbed methane field according to claim 3, wherein 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 that the first depth feature and the second depth feature help filter out the invalid third depth feature is to establish the corresponding relationship between the first depth feature, the second depth feature, and the underground geological structure based on the existing geological knowledge and historical coalbed methane field exploration data. Using this corresponding relationship, the reasonable range of the signal features in the third depth feature is speculated on the premise of conforming to this corresponding relationship. The third depth feature is compared with the reasonable range speculated from the first depth feature and the second depth feature, and the features in the third depth feature that exceed the reasonable range are determined as invalid features, and the invalid features in the third depth feature are filtered out.

5. A 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, wherein 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. A 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 interacts these data with the acquisition strategy optimization module.

8. A 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: ; where: 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, adjusted according to the relative magnitude 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: ; 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 geophone response frequency, is the preset geophone response threshold.

9. A three-dimensional seismic exploration data analysis system for a coalbed methane field 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. A three-dimensional seismic exploration data analysis system for a coalbed methane field 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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