Microseismic real-time monitoring method and device based on compressed sensing and 5G node acquisition

Abstract

The application provides a microseismic real-time monitoring method and device based on compressed sensing and 5G node collection, solves the problem that microseismic monitoring data is easy to be submerged in noise interference, and microseismic data collection equipment cannot transmit data in real time, or the transmitted data is uneven and incomplete. It comprises the following steps: collecting data, establishing a geological and geophysical model, simulating fracturing based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring observation system; establishing an adaptive optimal learning dictionary to form a compressed sensing collection scheme; conducting a due diligence investigation of field obstacles and interference sources to form a compressed sensing active obstacle avoidance scheme; using a 5G node collection system to collect field data; applying compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data; carrying out microseismic data processing and interpretation, and feeding back the microseismic analysis results to the fracturing site to realize real-time monitoring of the fracturing construction process.

Description

Technical Field

[0001] This invention relates to the field of microseismic monitoring technology, specifically to a method and device for real-time microseismic monitoring based on compressed sensing and 5G node acquisition. Background Technology

[0002] Microseismic events are phenomena associated with rock mass deformation, crack initiation, and propagation. They are earthquakes with a magnitude of less than 3, and compared to high-magnitude natural earthquakes, they have weaker energy and lower signal-to-noise ratios. Microseismic monitoring technology is widely used in areas such as fracturing monitoring in tight oil reservoirs, rockburst monitoring in mines, CO2 geological seismic storage monitoring, and monitoring of injection-induced earthquakes in conventional oilfields. In addition, there are many application cases of microseismic monitoring technology in rock slopes, highway tunnels, and water conservancy projects.

[0003] In the field of oil and gas exploration, my country has numerous low-permeability oil and gas reservoirs containing hydrocarbon strata. The exploitation of unconventional oil and gas reservoirs, represented by tight oil, shale gas, and coalbed methane, has become a hot topic, and the proportion of unconventional oil and gas in oil and gas field production is gradually increasing. As a key technology for improving the recovery rate and even the success rate of production wells, the demand for hydraulic fracturing is also increasing. The evaluation of the effectiveness of hydraulic fracturing is generally achieved by monitoring the microseismic events generated. Microseismic monitoring is currently one of the most accurate, timely, and information-rich monitoring methods for reservoir fracturing.

[0004] Based on the deployment method of microseismic monitoring equipment, microseismic monitoring can be divided into two types: well-based monitoring and surface monitoring. Among these, surface microseismic monitoring technology has been widely researched and applied in recent years due to its advantages of simplicity, economy, strong adaptability, large data volume, high lateral resolution, and high positioning accuracy. However, in actual construction, microseismic signal energy is relatively weak, and noise interference is widely distributed on the ground, resulting in a low signal-to-noise ratio for surface microseismic data, causing microseismic events to be submerged in noise interference. Due to limitations in acquisition conditions, cableless node acquisition equipment cannot transmit data in real time, failing to achieve the purpose of real-time monitoring; while cabled transmission is limited by equipment length (the cable spacing of mainstream seismic detectors is generally no more than 55m), resulting in data acquired during microseismic monitoring that is often uneven, incomplete, and has a low signal-to-noise ratio, severely affecting the processing and interpretation of microseismic signals. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and device for real-time monitoring of microseismic events based on compressed sensing and 5G node acquisition, which solves the problems that microseismic monitoring data is easily submerged in noise interference, and that microseismic data acquisition equipment cannot transmit data in real time, or that the transmitted data is uneven or incomplete.

[0006] In a first aspect, an embodiment of the present invention provides a real-time microseismic monitoring method based on compressed sensing and 5G node acquisition, comprising:

[0007] Collect data and establish a geological and geophysical model. Based on the parameters of the well to be fractured, conduct fracturing simulation to demonstrate the parameters of the microseismic monitoring and observation system.

[0008] An adaptive optimal learning dictionary is established, and a compressed sensing-based acquisition scheme is formed based on the adaptive optimal learning dictionary.

[0009] The microseismic monitoring and observation system is projected onto high-definition satellite images to conduct due diligence on obstacles and interference sources in the field, so as to form a compressed sensing active obstacle avoidance scheme.

[0010] Based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme, a 5G node acquisition system is used to carry out field data acquisition.

[0011] Compressed sensing data reconstruction technology is applied to reconstruct the collected field data to form three-dimensional microseismic monitoring data;

[0012] Based on the aforementioned three-dimensional microseismic monitoring data, microseismic data processing and interpretation are carried out, and the microseismic analysis results are fed back to the fracturing site in real time to achieve real-time monitoring of the fracturing construction process.

[0013] In one embodiment, the parameters of the microseismic monitoring and observation system include implementation type, deployment range, and trace spacing; wherein the deployment range is greater than the maximum burial depth of the fracturing point.

[0014] In one implementation, establishing an adaptive optimal learning dictionary and forming a compressed sensing-based acquisition scheme based on the adaptive optimal learning dictionary includes:

[0015] Within the compressed sensing framework, an adaptive optimal learning dictionary is established to achieve the optimal sparse representation of seismic data;

[0016] The optimal detector point position is obtained, forming a compressed sensing-based acquisition scheme.

[0017] In one implementation, the microseismic monitoring and observation system is projected onto high-definition satellite imagery to conduct due diligence on field obstacles and interference sources in order to form a compressed sensing active obstacle avoidance scheme, including: an observation matrix optimization technique based on obstacle constraints, which sets the observation matrix and sparse representation basis to have the lowest correlation.

[0018] In one implementation, the application of compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data includes:

[0019] A deep learning-based algorithm for reconstructing missing data has been developed, forming a technical process and modules.

[0020] Based on the aforementioned technical process and modules, the main framework of a deep learning network model is established, with the goal of reconstructing collected data using deep learning technology.

[0021] In one embodiment, the seismic data processing and interpretation includes at least one of: microseismic signal detection, phase picking, earthquake location, focal mechanism analysis, and magnitude analysis.

[0022] Secondly, an embodiment of the present invention provides a real-time microseismic monitoring device based on compressed sensing and 5G node acquisition, comprising:

[0023] The parameter verification module is used to collect data, establish geological and geophysical models, and perform fracturing simulations based on the parameters of the well to be fractured in order to verify the parameters of the microseismic monitoring and observation system.

[0024] The compressed sensing acquisition scheme module is used to establish an adaptive optimal learning dictionary, and to form a compressed sensing acquisition scheme based on the adaptive optimal learning dictionary.

[0025] The compressed sensing active obstacle avoidance scheme module is used to project the microseismic monitoring and observation system onto high-definition satellite images to conduct due diligence investigations of obstacles and interference sources in the field, so as to form a compressed sensing active obstacle avoidance scheme.

[0026] The data acquisition module is used to conduct field data acquisition using a 5G node acquisition system based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme.

[0027] The data reconstruction module is used to reconstruct the collected field data using compressed sensing data reconstruction technology to form three-dimensional microseismic monitoring data.

[0028] The data processing and interpretation module is used to perform microseismic data processing and interpretation based on the three-dimensional microseismic monitoring data;

[0029] The monitoring module is used for real-time monitoring of the fracturing process.

[0030] Thirdly, an embodiment of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0031] Fourthly, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0032] Fifthly, an embodiment of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0033] This invention provides a method and device for real-time microseismic monitoring based on compressed sensing and 5G node acquisition. The method involves collecting data, establishing a geological and geophysical model, and performing fracturing simulations based on the parameters of the well to be fractured to validate the parameters of the microseismic monitoring system. An adaptive optimal learning dictionary is established, and a compressed sensing acquisition scheme is formed based on this dictionary. The microseismic monitoring system is projected onto high-definition satellite imagery to conduct due diligence on field obstacles and interference sources, thus forming a compressed sensing active obstacle avoidance scheme. Field data is acquired using a 5G node acquisition system based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme. Compressed sensing data reconstruction technology is applied to reconstruct the acquired field data to form three-dimensional microseismic monitoring data. Microseismic data processing and interpretation are performed based on the three-dimensional microseismic monitoring data, and the microseismic analysis results are fed back to the fracturing site in real time to achieve real-time monitoring of the fracturing process. Compared with existing technologies, this invention utilizes compressed sensing technology for design and node acquisition, overcoming the influence of field terrain, interference sources, and obstacles, and reducing the cost of field data acquisition. By employing compressed sensing active obstacle avoidance and data reconstruction technologies, it achieves sufficient spatial sampling and obtains high-quality raw data. Furthermore, the use of a 5G node acquisition system enables high-speed data transmission and real-time microseismic monitoring. This is a practical and feasible microseismic monitoring technology solution. Attached Figure Description

[0034] Figure 1 The diagram shown is a flowchart of a real-time microseismic monitoring method based on compressed sensing and 5G node acquisition provided by an embodiment of the present invention.

[0035] Figure 2 The diagram shown is a deployment diagram of a theoretical high-density three-dimensional microseismic monitoring and observation system provided in an embodiment of the present invention.

[0036] Figure 3 The figure shown is a deployment diagram of a microseismic monitoring and observation system designed with compressed sensing sparse acquisition according to an embodiment of the present invention.

[0037] Figure 4 The diagram shown is a schematic representation of a microseismic real-time monitoring device based on compressed sensing and 5G node acquisition, according to an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] This invention provides a low-cost, high-precision monitoring method for real-time acquisition of the location and time of microseismic events. Utilizing compressed sensing technology and node acquisition, it overcomes the influence of terrain, interference sources, and obstacles, reducing the cost of field data acquisition. By employing compressed sensing active obstacle avoidance and data reconstruction techniques, it achieves sufficient spatial sampling and obtains high-quality raw data. The 5G node acquisition system enables high-speed data transmission, achieving real-time microseismic monitoring. This is a practical and feasible microseismic monitoring technology solution. Specific implementation methods are described in the following embodiments.

[0040] Example 1:

[0041] Figure 1 The diagram shown is a flowchart of a real-time microseismic monitoring method based on compressed sensing and 5G node acquisition provided by an embodiment of the present invention.

[0042] This embodiment provides a method for real-time monitoring of microseismic events based on compressed sensing and 5G node acquisition, such as... Figure 1 As shown, this real-time microseismic monitoring method based on compressed sensing and 5G node acquisition includes:

[0043] Step 01: Collect data and establish a geological and geophysical model. Based on the parameters of the well to be fractured, conduct a fracturing simulation to demonstrate the parameters of the microseismic monitoring and observation system.

[0044] Step 02: Establish an adaptive optimal learning dictionary, and form a compressed sensing-based acquisition scheme based on the adaptive optimal learning dictionary;

[0045] Step 03: Project the microseismic monitoring and observation system onto high-definition satellite images to conduct due diligence on obstacles and interference sources in the field, so as to form a compressed sensing active obstacle avoidance scheme;

[0046] Step 04: Based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme, use a 5G node acquisition system to conduct field data acquisition;

[0047] Step 05: Apply compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data;

[0048] Step 06: Based on the three-dimensional microseismic monitoring data, perform microseismic data processing and interpretation, and feed the microseismic analysis results back to the fracturing site in real time to achieve real-time monitoring of the fracturing construction process.

[0049] The most significant innovation of the microseismic real-time monitoring method based on compressed sensing and 5G node acquisition provided in this embodiment compared to existing technologies lies in combining previous microseismic monitoring technologies with compressed sensing acquisition and data reconstruction technologies, and 5G node seismic acquisition technologies, achieving the goal of low-cost, high-density, and real-time monitoring. The use of compressed sensing technology in the design and node acquisition overcomes the influence of field terrain, interference sources, and obstacles. Taking the replacement of all 1500 wired seismic devices in a single project with node seismographs as an example, it is estimated that the cost of field data acquisition will be reduced by 30%. The use of compressed sensing active obstacle avoidance and data reconstruction technologies achieves sufficient spatial sampling, obtaining high-density, all-round three-dimensional microseismic data, with an estimated improvement in the signal-to-noise ratio of 20%. The 5G node acquisition system enables high-speed data transmission, achieving real-time microseismic monitoring for timely early warning or guidance on adjusting fracturing construction parameters.

[0050] Example 2:

[0051] Based on the above embodiments, this embodiment provides a real-time microseismic monitoring method based on compressed sensing and 5G node acquisition. This real-time microseismic monitoring method based on compressed sensing and 5G node acquisition includes:

[0052] Step 01: Modeling and Demonstrating the Microseismic Monitoring and Observation System. Collect data and establish a geological and geophysical model. Perform fracturing simulations based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring and observation system. The data includes geological, drilling, and seismic data. A geological and geophysical model is established based on the target layer depth, horizontal section length, fracturing design displacement, and predicted pressure of the well to be fractured. Simulate the fracturing process to demonstrate the implementation type, deployment range, and key parameters of the microseismic surface observation system, including channel spacing. The deployment range is greater than the maximum burial depth of the fracturing point; however, it is generally not less than the maximum burial depth of the fracturing point, while also considering the influence of the imaging aperture and appropriately enlarging it. Channel spacing is designed based on the shortest wavelength of the microseismic signal, requiring sufficient sampling.

[0053] Step 02: Compressed Sensing Sparse Acquisition Scheme Design. An adaptive optimal learning dictionary is established, and a compressed sensing-based acquisition scheme is formed based on this dictionary. This means that within the framework of compressed sensing, an adaptive optimal learning dictionary is established to achieve the optimal sparse representation of seismic data, obtain the optimal receiver locations, and form a compressed sensing-based sparse acquisition scheme. The key technology is dictionary learning technology, which adaptively learns corresponding features according to the characteristics of different seismic data to better achieve the sparse representation of complex microseismic data.

[0054] Step 03: Compressed Sensing Active Obstacle Avoidance Scheme Design. The microseismic monitoring and observation system is projected onto high-resolution satellite imagery to conduct due diligence on field obstacles and interference sources, thereby formulating a compressed sensing active obstacle avoidance scheme. This involves projecting the designed observation system onto high-resolution satellite imagery to conduct due diligence on interference sources and large field obstacles, thus completing the design of the compressed sensing active obstacle avoidance scheme. Through obstacle-constrained observation matrix optimization techniques, the correlation between the observation matrix and the sparse representation basis is minimized, which is more conducive to data reconstruction.

[0055] Step 04: Based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme, conduct field data acquisition using a 5G node acquisition system. Specifically, conduct field data acquisition using a 5G node acquisition system and transmit the acquired data back to the cloud at high speed for subsequent data reconstruction.

[0056] Specifically, 5G (Fifth Generation Mobile Communication Technology) is a new generation of broadband mobile communication technology characterized by high speed, low latency, and massive connectivity. The intelligent seismic node acquisition system built on 5G technology achieves wireless and miniaturized equipment. Leveraging the high bandwidth, wide connectivity, and low latency of the 5G network, it provides "mobile, extendable, and real-time processing" wireless exploration capabilities. This system boasts three powerful technical features: centralized access of tens of thousands of seismic nodes, significantly improving acquisition efficiency; a locally deployed data analysis platform for real-time data analysis, transmitting the data back to the node equipment for real-time high-quality control, significantly improving acquisition accuracy. 5G node acquisition is not limited by the construction environment in field operations, offering advantages such as real-time data transmission, intelligent status monitoring, and lightweight and efficient construction. It can overcome the limitations of conventional wired seismic acquisition equipment, achieving sparse acquisition as required by compressed sensing technology; simultaneously, it enables real-time high-speed transmission and quality control of field data, meeting the needs of real-time monitoring.

[0057] Step 05: Apply compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data. This can be understood as using a deep learning-based missing data reconstruction algorithm to form corresponding technical processes and modules, establishing the main framework of a deep learning network model, and achieving the goal of reconstructing collected data using deep learning technology. To further increase the network training accuracy, structural similarity constraints can be added to the conventional L2 norm constraints to improve the network's accuracy.

[0058] Specifically, compressed sensing, also known as compressed sampling or sparse sampling, is a technique for finding sparse solutions to underdetermined linear systems. Compressed sensing is applied in signal processing to acquire and reconstruct sparse or compressible signals. Compressed sensing methods discard redundant information in the current signal sampling. It directly obtains compressed samples from continuous-time signal transformations, and then processes these compressed samples using optimization methods in digital signal processing. Highly efficient seismic data acquisition methods based on compressed sensing fully utilize the sparsity constraints of seismic data, reducing the amount of seismic data acquired and transmitted, whether for intra-node compressed acquisition or sparse acquisition in the spatial domain. This results in higher acquisition efficiency than traditional data compression and encoding methods. Compressed sensing theory overcomes the limitation of the Nyquist sampling theorem on the original signal sampling rate; by selecting specific measurement matrices and reconstruction algorithms, high-precision reconstruction of the original signal can be achieved with only a lower sampling rate. Therefore, compressed sensing technology can be applied to the field of microseismic monitoring data acquisition. Within the framework of compressed sensing, an adaptive optimal learning dictionary can be established to achieve the optimal sparse representation of seismic data. Based on obstacle constraints, the observation matrix can be optimized to obtain the optimal receiver locations. Then, in the data processing stage, a deep learning-based missing data reconstruction technique is used to restore high-density, comprehensive 3D microseismic acquisition data. Fine-grained data processing is then performed to improve the weak signal identification capability and positioning accuracy of microseismic events.

[0059] Step 06: Based on the aforementioned 3D microseismic monitoring data, perform microseismic data processing and interpretation, and feed the microseismic analysis results back to the fracturing site in real time to achieve real-time monitoring of the fracturing process. This means utilizing the reconstructed 3D high-density seismic monitoring data to perform microseismic signal detection, phase picking, earthquake location, focal mechanism analysis, magnitude analysis, and other data processing and interpretation. The microseismic analysis results are then displayed in real time via the cloud, enabling uninterrupted real-time monitoring of the fracturing process. This data can be used to guide parameter adjustments at the fracturing site or for real-time early warning of microseismic signals.

[0060] The microseismic real-time monitoring method based on compressed sensing and 5G node acquisition provided in this embodiment is a method for real-time microseismic monitoring in complex surface areas. It utilizes compressed sensing to acquire and reconstruct surface microseismic monitoring data, and then transmits the data using a 5G node acquisition system. Building upon previous microseismic monitoring technologies, it applies the latest compressed sensing technology to achieve sparse acquisition and data reconstruction, realizing high-density, omnidirectional three-dimensional microseismic data acquisition at a lower cost. This improves the ability to identify extremely weak signals and enhances positioning accuracy through high-density, omnidirectional sampling. The 5G node acquisition system transmits the acquired data to the cloud in real time, and then the microseismic data processing and interpretation results are fed back to the fracturing site in real time, achieving real-time microseismic monitoring. This method is a low-cost, high-precision monitoring method that can obtain the location and time of microseismic events in real time. It overcomes the shortcomings of conventional wired seismic acquisition systems, which are limited by terrain, obstacles, and other factors, resulting in insufficient spatial data sampling and high costs.

[0061] Example 3:

[0062] Based on the above embodiments, a certain shale oil and gas fracturing well is designed with a well depth of 4500 meters, a vertical depth of 3000 meters, a horizontal section to be fractured of 1500 meters, and a fracturing project designed for 30 sections.

[0063] After modeling and verification using the method in step 01 of the above embodiments, a high-density, all-around three-dimensional grid-like observation system is adopted for monitoring. The observation range is 3000-4500 meters, ensuring that the observation angle is greater than 45°. The detector grid density is 50m*50m, and it is estimated that 15625 receiving detectors need to be deployed. (Reference) Figure 2 As shown. Following the compressed sensing technology design process described in step 02 of the above embodiment, it is estimated that 3125 receiving detectors will need to be deployed. (Refer to...) Figure 3 As shown, the number of receiving points is only 1 / 5 of the original high-density three-dimensional sampling receiving points, greatly reducing the number of receiving points. The deployment method of the microseismic real-time monitoring method based on compressed sensing and 5G node acquisition provided by this invention is comparable to the number of conventional wired devices, but the distribution is relatively uniform, the sampling is more sufficient, the quality of the reconstructed data after processing is higher, the weak signal identification capability is stronger, and the positioning accuracy is improved.

[0064] Example 4:

[0065] Figure 4 The diagram shown is a schematic representation of a microseismic real-time monitoring device based on compressed sensing and 5G node acquisition, according to an embodiment of the present invention.

[0066] This embodiment provides a real-time microseismic monitoring device 100 based on compressed sensing and 5G node acquisition, for reference... Figure 4 As shown, the real-time microseismic monitoring device 100 based on compressed sensing and 5G node acquisition includes: a parameter verification module 10, a compressed sensing acquisition scheme module 20, a compressed sensing active obstacle avoidance scheme module 30, a data acquisition module 40, a data reconstruction module 50, a data processing and interpretation module 60, and a monitoring module 70. Among them,

[0067] The parameter verification module 10 is used to collect data, establish a geological and geophysical model, and perform fracturing simulation based on the parameters of the well to be fractured in order to verify the parameters of the microseismic monitoring and observation system.

[0068] Specifically, the data includes geological, drilling, and seismic data. A geological and geophysical model is established based on the collected geological, drilling, and seismic data. The fracturing process is simulated based on the target layer depth, horizontal section length, fracturing design displacement, and predicted pressure of the well to be fractured. This simulation demonstrates the implementation type, deployment range, and key parameters of the microseismic ground observation system, including channel spacing. The deployment range is greater than the maximum burial depth of the fracturing point; however, it is generally not less than the maximum burial depth of the fracturing point, while also considering the influence of the imaging aperture and appropriately enlarging it. Channel spacing is designed based on the shortest wavelength of the microseismic signal, requiring sufficient sampling.

[0069] The compressed sensing acquisition scheme module 20 is used to establish an adaptive optimal learning dictionary, and to form a compressed sensing acquisition scheme based on the adaptive optimal learning dictionary.

[0070] Specifically, within the framework of compressed sensing, an adaptive optimal learning dictionary is established to achieve the optimal sparse representation of seismic data, obtain the optimal receiver location, and form a sparse acquisition scheme based on compressed sensing. The key technology is dictionary learning technology, which adaptively learns corresponding features according to the characteristics of different seismic data to better achieve the sparse representation of complex microseismic data.

[0071] The compressed sensing active obstacle avoidance scheme module 30 is used to project the microseismic monitoring and observation system onto high-definition satellite images to conduct due diligence investigations of obstacles and interference sources in the field, so as to form a compressed sensing active obstacle avoidance scheme.

[0072] Specifically, the designed observation system is projected onto high-resolution satellite imagery to conduct due diligence on interference sources and large obstacles in the field, thus completing the design of a compressed sensing active obstacle avoidance scheme. By employing an observation matrix optimization technique based on obstacle constraints, the correlation between the observation matrix and the sparse representation basis is minimized, which is more conducive to data reconstruction.

[0073] The data acquisition module 40 is used to conduct field data acquisition using a 5G node acquisition system based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme.

[0074] Specifically, a 5G node acquisition system is used to collect data in the field and the collected data is transmitted back to the cloud at high speed for further data reconstruction.

[0075] The data reconstruction module 50 is used to apply compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data.

[0076] Specifically, based on deep learning-based missing data reconstruction algorithms, corresponding technical processes and modules are developed, establishing the main framework of a deep learning network model to achieve the goal of reconstructing collected data using deep learning technology. To further increase network training accuracy, structural similarity constraints can be added to the conventional L2 norm constraints to improve network accuracy.

[0077] The data processing and interpretation module 60 is used to perform microseismic data processing and interpretation based on the three-dimensional microseismic monitoring data.

[0078] Specifically, the reconstructed three-dimensional high-density seismic monitoring data is used to carry out data processing and interpretation, including microseismic signal detection, phase picking, earthquake location, focal mechanism analysis, and magnitude analysis.

[0079] The monitoring module 70 is used for real-time monitoring of the fracturing process. Specifically, it displays the microseismic analysis results in real time via the cloud, enabling uninterrupted real-time monitoring of the fracturing process to guide the adjustment of on-site fracturing parameters; or it can be used for real-time early warning of microseismic signals.

[0080] Example 5:

[0081] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0082] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that, when executed by a processor, the computer program implements the steps of the method described in the above embodiments, including:

[0083] Step 01: Modeling and Demonstrating the Microseismic Monitoring and Observation System. Collect data and establish a geological and geophysical model. Perform fracturing simulations based on the parameters of the well to be fractured to demonstrate the parameters of the microseismic monitoring and observation system. The data includes geological, drilling, and seismic data. A geological and geophysical model is established based on the target layer depth, horizontal section length, fracturing design displacement, and predicted pressure of the well to be fractured. Simulate the fracturing process to demonstrate the implementation type, deployment range, and key parameters of the microseismic surface observation system, including channel spacing. The deployment range is greater than the maximum burial depth of the fracturing point; however, it is generally not less than the maximum burial depth of the fracturing point, while also considering the influence of the imaging aperture and appropriately enlarging it. Channel spacing is designed based on the shortest wavelength of the microseismic signal, requiring sufficient sampling.

[0084] Step 02: Compressed Sensing Sparse Acquisition Scheme Design. An adaptive optimal learning dictionary is established, and a compressed sensing-based acquisition scheme is formed based on this dictionary. This means that within the framework of compressed sensing, an adaptive optimal learning dictionary is established to achieve the optimal sparse representation of seismic data, obtain the optimal receiver locations, and form a compressed sensing-based sparse acquisition scheme. The key technology is dictionary learning technology, which adaptively learns corresponding features according to the characteristics of different seismic data to better achieve the sparse representation of complex microseismic data.

[0085] Step 03: Compressed Sensing Active Obstacle Avoidance Scheme Design. The microseismic monitoring and observation system is projected onto high-resolution satellite imagery to conduct due diligence on field obstacles and interference sources, thereby formulating a compressed sensing active obstacle avoidance scheme. This involves projecting the designed observation system onto high-resolution satellite imagery to conduct due diligence on interference sources and large field obstacles, thus completing the design of the compressed sensing active obstacle avoidance scheme. Through obstacle-constrained observation matrix optimization techniques, the correlation between the observation matrix and the sparse representation basis is minimized, which is more conducive to data reconstruction.

[0086] Step 04: Based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme, conduct field data acquisition using a 5G node acquisition system. Specifically, conduct field data acquisition using a 5G node acquisition system and transmit the acquired data back to the cloud at high speed for subsequent data reconstruction.

[0087] Specifically, 5G (Fifth Generation Mobile Communication Technology) is a new generation of broadband mobile communication technology characterized by high speed, low latency, and massive connectivity. The intelligent seismic node acquisition system built on 5G technology achieves wireless and miniaturized equipment. Leveraging the high bandwidth, wide connectivity, and low latency of the 5G network, it provides "mobile, extendable, and real-time processing" wireless exploration capabilities. This system boasts three powerful technical features: centralized access of tens of thousands of seismic nodes, significantly improving acquisition efficiency; a locally deployed data analysis platform for real-time data analysis, transmitting the data back to the node equipment for real-time high-quality control, significantly improving acquisition accuracy. 5G node acquisition is not limited by the construction environment in field operations, offering advantages such as real-time data transmission, intelligent status monitoring, and lightweight and efficient construction. It can overcome the limitations of conventional wired seismic acquisition equipment, achieving sparse acquisition as required by compressed sensing technology; simultaneously, it enables real-time high-speed transmission and quality control of field data, meeting the needs of real-time monitoring.

[0088] Step 05: Apply compressed sensing data reconstruction technology to reconstruct the collected field data to form three-dimensional microseismic monitoring data. This can be understood as using a deep learning-based missing data reconstruction algorithm to form corresponding technical processes and modules, establishing the main framework of a deep learning network model, and achieving the goal of reconstructing collected data using deep learning technology. To further increase the network training accuracy, structural similarity constraints can be added to the conventional L2 norm constraints to improve the network's accuracy.

[0089] Specifically, compressed sensing, also known as compressed sampling or sparse sampling, is a technique for finding sparse solutions to underdetermined linear systems. Compressed sensing is applied in signal processing to acquire and reconstruct sparse or compressible signals. Compressed sensing methods discard redundant information in the current signal sampling. It directly obtains compressed samples from continuous-time signal transformations, and then processes these compressed samples using optimization methods in digital signal processing. Highly efficient seismic data acquisition methods based on compressed sensing fully utilize the sparsity constraints of seismic data, reducing the amount of seismic data acquired and transmitted, whether for intra-node compressed acquisition or sparse acquisition in the spatial domain. This results in higher acquisition efficiency than traditional data compression and encoding methods. Compressed sensing theory overcomes the limitation of the Nyquist sampling theorem on the original signal sampling rate; by selecting specific measurement matrices and reconstruction algorithms, high-precision reconstruction of the original signal can be achieved with only a lower sampling rate. Therefore, compressed sensing technology can be applied to the field of microseismic monitoring data acquisition. Within the framework of compressed sensing, an adaptive optimal learning dictionary can be established to achieve the optimal sparse representation of seismic data. Based on obstacle constraints, the observation matrix can be optimized to obtain the optimal receiver locations. Then, in the data processing stage, a deep learning-based missing data reconstruction technique is used to restore high-density, comprehensive 3D microseismic acquisition data. Fine-grained data processing is then performed to improve the weak signal identification capability and positioning accuracy of microseismic events.

[0090] Step 06: Based on the aforementioned 3D microseismic monitoring data, perform microseismic data processing and interpretation, and feed the microseismic analysis results back to the fracturing site in real time to achieve real-time monitoring of the fracturing process. This means utilizing the reconstructed 3D high-density seismic monitoring data to perform microseismic signal detection, phase picking, earthquake location, focal mechanism analysis, magnitude analysis, and other data processing and interpretation. The microseismic analysis results are then displayed in real time via the cloud, enabling uninterrupted real-time monitoring of the fracturing process. This data can be used to guide parameter adjustments at the fracturing site or for real-time early warning of microseismic signals.

[0091] The microseismic real-time monitoring method based on compressed sensing and 5G node acquisition provided in this embodiment is a method for real-time microseismic monitoring in complex surface areas. It utilizes compressed sensing to acquire and reconstruct surface microseismic monitoring data, and then transmits the data using a 5G node acquisition system. Building upon previous microseismic monitoring technologies, it applies the latest compressed sensing technology to achieve sparse acquisition and data reconstruction, realizing high-density, omnidirectional three-dimensional microseismic data acquisition at a lower cost. This improves the ability to identify extremely weak signals and enhances positioning accuracy through high-density, omnidirectional sampling. The 5G node acquisition system transmits the acquired data to the cloud in real time, and then the microseismic data processing and interpretation results are fed back to the fracturing site in real time, achieving real-time microseismic monitoring. This method is a low-cost, high-precision monitoring method that can obtain the location and time of microseismic events in real time. It overcomes the shortcomings of conventional wired seismic acquisition systems, which are limited by terrain, obstacles, and other factors, resulting in insufficient spatial data sampling and high costs.

[0092] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0093] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.

[0094] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0095] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0096] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0097] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0098] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0099] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0100] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0101] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for real-time monitoring of microseismic events based on compressed sensing and 5G node acquisition, characterized in that, include: Collect data and establish a geological and geophysical model. Based on the parameters of the well to be fractured, conduct fracturing simulation to demonstrate the parameters of the microseismic monitoring and observation system. An adaptive optimal learning dictionary is established, and a compressed sensing-based acquisition scheme is formed based on the adaptive optimal learning dictionary. The microseismic monitoring and observation system is projected onto high-definition satellite images to conduct due diligence on obstacles and interference sources in the field, so as to form a compressed sensing active obstacle avoidance scheme. Based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme, a 5G node acquisition system is used to carry out field data acquisition. Compressed sensing data reconstruction technology is applied to reconstruct the collected field data to form three-dimensional microseismic monitoring data; Based on the aforementioned three-dimensional microseismic monitoring data, microseismic data processing and interpretation are carried out, and the microseismic analysis results are fed back to the fracturing site in real time to achieve real-time monitoring of the fracturing construction process.

2. The real-time microseismic monitoring method based on compressed sensing and 5G node acquisition according to claim 1, characterized in that, The parameters of the microseismic monitoring and observation system include implementation type, deployment range, and trace spacing; wherein, the deployment range is greater than the maximum burial depth of the fracturing point.

3. The real-time microseismic monitoring method based on compressed sensing and 5G node acquisition according to claim 1, characterized in that, The step of establishing an adaptive optimal learning dictionary and forming a compressed sensing-based acquisition scheme based on the adaptive optimal learning dictionary includes: Within the compressed sensing framework, an adaptive optimal learning dictionary is established to achieve the optimal sparse representation of seismic data; The optimal detector point position is obtained, forming a compressed sensing-based acquisition scheme.

4. The real-time microseismic monitoring method based on compressed sensing and 5G node acquisition according to claim 1, characterized in that, The process involves projecting the microseismic monitoring and observation system onto high-definition satellite imagery to conduct due diligence on field obstacles and interference sources, thereby forming a compressed sensing active obstacle avoidance scheme. This includes: an observation matrix optimization technique based on obstacle constraints, where the observation matrix and sparse representation basis are set to have the lowest correlation.

5. The real-time microseismic monitoring method based on compressed sensing and 5G node acquisition according to claim 1, characterized in that, The application of compressed sensing data reconstruction technology reconstructs the collected field data to form three-dimensional microseismic monitoring data, including: A deep learning-based algorithm for reconstructing missing data has been developed, forming a technical process and modules. Based on the aforementioned technical process and modules, the main framework of a deep learning network model is established, with the goal of reconstructing collected data using deep learning technology.

6. The real-time microseismic monitoring method based on compressed sensing and 5G node acquisition according to claim 1, characterized in that, The earthquake data processing and interpretation includes at least one of the following: microseismic signal detection, phase picking, earthquake location, focal mechanism analysis, and magnitude analysis.

7. A real-time microseismic monitoring device based on compressed sensing and 5G node acquisition, characterized in that, include: The parameter verification module is used to collect data, establish geological and geophysical models, and perform fracturing simulations based on the parameters of the well to be fractured in order to verify the parameters of the microseismic monitoring and observation system. The compressed sensing acquisition scheme module is used to establish an adaptive optimal learning dictionary, and to form a compressed sensing acquisition scheme based on the adaptive optimal learning dictionary. The compressed sensing active obstacle avoidance scheme module is used to project the microseismic monitoring and observation system onto high-definition satellite images to conduct due diligence investigations of obstacles and interference sources in the field, so as to form a compressed sensing active obstacle avoidance scheme. The data acquisition module is used to conduct field data acquisition using a 5G node acquisition system based on the compressed sensing acquisition scheme and the compressed sensing active obstacle avoidance scheme. The data reconstruction module is used to reconstruct the collected field data using compressed sensing data reconstruction technology to form three-dimensional microseismic monitoring data. The data processing and interpretation module is used to perform microseismic data processing and interpretation based on the three-dimensional microseismic monitoring data; The monitoring module is used for real-time monitoring of the fracturing process.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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