Natural fracture prediction device and system for deep shale

Abstract

The invention discloses a natural fracture prediction device for deep shale, which relates to the technical field of geological engineering and comprises a sensor unit, a data storage unit, a data processing unit and a prediction unit. The sensor unit is used for deploying various sensors in a deep shale area so as to collect different types of data, and the sensors comprise but are not limited to a resistivity sensor used for measuring the resistivity of shale, a fluid sensor used for detecting the fluid content in the shale and a stress sensor used for sensing the stress state of the shale. The invention further discloses a natural fracture prediction system for the deep shale. According to the natural fracture prediction device and system, multiple sensors are integrated, key data of a deep shale area can be comprehensively collected, a rich and accurate information basis is provided for fracture prediction, original data are preprocessed through the data processing unit, the quality and consistency of the data are effectively improved, and the prediction accuracy is improved. And a foundation is laid for subsequent analysis.

Description

Technical Field

[0001] The present invention relates to the field of geological engineering technology, and in particular to a natural fracture prediction device and system for deep shale. Background Art

[0002] Fracture prediction in deep shale reservoirs is crucial in the exploration and development of energy resources such as oil and natural gas. The presence of fractures not only affects the storage and migration of shale gas but also directly affects extraction efficiency and production. However, the geological conditions of deep shale are complex, and fracture development is difficult to visually assess. Traditional methods often rely on the experience and intuitive judgment of geological prospectors, resulting in limited prediction accuracy. With the advancement of science and technology, especially the development of sensor technology and data processing technology, new means have been provided for the prediction of deep shale fractures. By deploying a variety of sensors, such as resistivity sensors, fluid sensors, and stress sensors, multi-type data about shale reservoirs can be collected. These data contain key information such as the physical properties of shale, fluid content, and stress state, providing a rich data source for fracture prediction.

[0003] However, relying solely on data collection is not enough to achieve accurate fracture prediction. On the one hand, the raw data often has problems such as noise and missing information, which require effective preprocessing. On the other hand, how to extract key information related to fracture development from massive amounts of data and establish an effective prediction model is a technical challenge that needs to be solved urgently. To this end, we proposed a natural fracture prediction device and system for deep shale. Summary of the Invention

[0004] In order to solve the above technical problems, a natural fracture prediction device and system for deep shale are provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: A natural fracture prediction device for deep shale, comprising: a sensor unit, a data storage unit, a data processing unit and a prediction unit; The sensor unit is used to deploy a variety of sensors in a deep shale area to collect different types of data, including but not limited to: a resistivity sensor for measuring shale resistivity, a fluid sensor for detecting fluid content in shale, and a stress sensor for sensing shale stress state; The data storage unit is used to store the raw data collected by the sensor and adopts a redundant storage mechanism to ensure the security and reliability of the data; The data processing unit reads data from the data storage unit and performs pre-processing on the data, including filtering the resistivity data and smoothing the fluid content data; The prediction unit uses statistical analysis methods based on the processed data to predict the possibility of natural fractures.

[0006] Preferably, the stress components measured by the stress sensor in the sensor unit include horizontal stress and vertical stress. A stress measurement array is formed by arranging a plurality of stress sensors at intervals at different depths. The stress difference is calculated by an algorithm and used as an indicator of shale deformation and possible cracks. The calculation formula of stress difference is: , Where, represents the stress difference, and represents the horizontal stress, represents vertical stress; The data processing unit correlates the stress data with other data for analysis and calculation of strain, and combines the strain data with the fluid content data; Among them, the expression for correlation analysis between stress data and other data is: , Where, represents strain data, represents Young's modulus, represents Poisson's ratio; When combining strain data with fluid content data for analysis, the influence of fluid on shale deformation is considered, and the algorithm model expression is: , Where, represents the coupling parameter, represents the function obtained by fitting the experimental data, Indicates fluid content; Fitting functions to experimental data , by analyzing the changes in coupling parameters to predict the possible location and scale of cracks.

[0007] Preferably, the filtering process on the resistivity data and the smoothing process on the fluid content data specifically include: Among them, the filtering formula for resistivity data filtering is: , Where, represents the filtered signal, represents the impulse response of the filter, represents the index of the output sample, represents the index of the filter coefficient, Indicates that a delay operation is performed on the original signal. represents the original input signal sequence, represents the filter length; Among them, the smoothing formula for fluid content data smoothing is: , Where, After smoothing, The value of the data point, represents the raw fluid content data point, Indicates the smoothing window size.

[0008] Preferably, the pre-processing operation in the data processing unit further includes time synchronization of different sensor data; Since the timestamps of data collected by different sensors are different, a time synchronization algorithm is used to adjust the data to the same time base: For timestamps and The time difference between the two sets of data is found by the least squares method. The expression for finding the time difference by the least squares method is: , Where, Indicates the time difference, and Indicates the two sets of data The timestamp of each data point, Indicates the number of data points to ensure temporal consistency across different data sources; The data is spatially registered. When sensors are distributed at different locations, the data at different locations are mapped to a unified spatial coordinate system based on the geological structure of the shale and the relative position information of the sensors. The coordinate transformation formula is: , , , Where, represents the original sensor coordinates, represents the coordinates after registration, Represents the transformation coefficient, ensuring the spatial continuity of the data and providing an accurate data basis for subsequent predictions.

[0009] Preferably, the prediction unit uses a machine learning algorithm to predict natural fractures; The processed data is used as the input feature vector ,in Representing different characteristics, including: resistivity, fluid content, stress, and strain after pre-processing; Construct SVM classification function: , Where, represents the sample label, represents the Lagrange multiplier, represents the kernel function, represents the bias term; By training a sample set of known shale fracture data and using an optimization algorithm to solve and, the classification hyperplane can accurately divide the data with and without fractures, thus realizing the prediction of natural fractures in deep shale.

[0010] Preferably, the data storage unit introduces data compression technology on the basis of redundant storage mechanism to save storage space; For the raw data collected by the sensor, DCT is used for compression: Based on the raw data collected by the sensor, the data sequence Perform DCT transformation, and its transformation formula is: , Where, Represents the first elements, Represents the first data elements, Indicates the length of the original data sequence, represents the kernel function of discrete cosine transform; By setting the threshold and discarding some high-frequency coefficients, data compression can be achieved; When reading data, the compressed data is subjected to inverse DCT transformation to restore the original data. The inverse transformation formula is: .

[0011] A natural fracture prediction system for deep shale, used to implement the natural fracture prediction device for deep shale, comprising: a remote data transmission module and a remote control center; The remote data transmission module uses wireless communication technology to transmit the data collected and processed by the device to the remote control center in real time; After receiving the data, the remote control center backs up and stores the data. The backup storage adopts an off-site backup strategy and stores the data in data centers in different geographical locations. The remote control center sends control instructions to the prediction device. These instructions include adjusting the sampling frequency of the sensor, adjusting the algorithm parameters of the data processing unit, and adjusting the control signal to remotely operate the entire prediction device based on the thresholds and rules set by expert experience according to the different geological conditions of the shale layer.

[0012] Preferably, after receiving the data, the remote control center predicts the trend of the collected data through time series analysis: Assume the data sequence is , the expression of the time series analysis model is: , Where, represents the autoregressive coefficient, represents the moving average coefficient, represents a white noise sequence; Determine the model parameters using the minimum information criterion algorithm and , to predict the stress data in the future, where the formula of the minimum information criterion algorithm is: , Where, is the number of parameters of the model, including The natural regression coefficients, Moving average coefficients and a constant term, is the maximum likelihood function of the model; The prediction results are combined with the current prediction model to determine in advance the potential impact of shale internal stress change trends on the formation of natural fractures.

[0013] Preferably, the remote control center performs quality assessment on the transmitted data to ensure the reliability of the data; The completeness of the data is measured by calculating the proportion of missing data points: , Where, represents the integrity index, represents the number of missing data points, Represents the total number of data points. If the value falls below the set threshold, a data missing alert will be issued and the location of the missing data will be marked; The standard deviation is used to assess the degree of dispersion of data: , Where, represents the standard deviation, Indicates the actual measurement value, indicates the average value, which is determined based on sensor accuracy and historical data Error range, if If the error range is exceeded, the accuracy of the data is considered to be in doubt and needs to be remeasured or corrected.

[0014] Preferably, after receiving the data from the remote data transmission module, the remote control center standardizes the data collected by different types of sensors, unifies various types of data into a specific data interval, eliminates the deviation of the data due to different dimensions, and uses the principal component analysis method to perform dimensionality reduction operations on the standardized data to find the principal components that play a dominant role in the data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a natural fracture prediction device and system that can comprehensively collect key data of deep shale areas by integrating multiple sensors, providing a rich and accurate information basis for fracture prediction. The original data is pre-processed by the data processing unit, which effectively improves the quality and consistency of the data, laying a solid foundation for subsequent analysis. The prediction unit can accurately predict the possibility of natural fractures based on the processed data, improving the accuracy and reliability of the prediction. The introduction of data compression technology and redundant storage mechanism effectively saves storage space and ensures data security. The prediction system also has the functions of remote data transmission and remote control center, which can realize real-time transmission, backup storage and remote operation of data, improving the flexibility and convenience of the overall system. The remote control center can also perform quality assessment and trend prediction on the collected data, further enhancing the prediction ability and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A framework diagram of the natural fracture prediction device for deep shale. DETAILED DESCRIPTION

[0017] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0018] Reference Figure 1 As shown, a natural fracture prediction device for deep shale includes: a sensor unit, a data storage unit, a data processing unit and a prediction unit; The sensor unit plays a critical role in deep shale exploration. This unit meticulously deploys a variety of advanced sensors to collect a rich variety of data. Resistivity sensors, using specialized electromagnetic induction technology, precisely measure the resistivity of shale, providing crucial information for analyzing its internal structure. Fluid sensors, employing unique detection principles, can sensitively detect the content of various fluids within the shale. Stress sensors, on the other hand, precisely sense the stress state of the shale and capture subtle changes.

[0019] The data storage unit utilizes an advanced redundant storage mechanism, creating a sturdy fortress for data. It backs up the raw data collected by the sensors in multiple copies, ensuring data security and reliability from both hardware and software levels, effectively preventing data loss or corruption.

[0020] The data processing unit reads data from the data storage unit and performs a series of sophisticated preprocessing. For resistivity data, efficient filtering algorithms are used to remove noise. For fluid content data, specialized smoothing techniques are used to ensure greater stability and accuracy.

[0021] Based on the processed data, the prediction unit uses complex and precise statistical analysis methods to deeply explore the potential information behind the data, thereby scientifically predicting the possibility of the existence of natural fractures.

[0022] The sensor unit, comprised of a variety of advanced sensors, accurately collects rich data such as shale resistivity, fluid content, and stress state, providing comprehensive information support for exploration. The data storage unit, leveraging redundant storage mechanisms, safeguards data security at multiple levels, both hardware and software, preventing data loss or corruption and ensuring data integrity and availability. The data processing unit utilizes specialized techniques such as filtering and smoothing to denoise and optimize data, improving data quality. The prediction unit, leveraging high-quality data and employing complex statistical analysis, scientifically predicts the likelihood of natural fractures, facilitating efficient exploration and mitigating exploration risks.

[0023] The stress components measured by the stress sensors in the sensor unit include horizontal stress and vertical stress. By arranging several stress sensors at different depths, a stress measurement array is formed. The stress difference is calculated through an algorithm and used as an indicator of shale deformation and possible cracks. The calculation formula of stress difference is: , Where, represents the stress difference, and represents the horizontal stress, represents vertical stress; The data processing unit correlates the stress data with other data for analysis and calculation of strain, and combines the strain data with the fluid content data; Among them, the expression for correlation analysis between stress data and other data is: , Where, represents strain data, represents Young's modulus, represents Poisson's ratio; When combining strain data with fluid content data for analysis, the influence of fluid on shale deformation is considered, and the algorithm model expression is: , Where, represents the coupling parameter, represents the function obtained by fitting the experimental data, Indicates fluid content; Fitting functions to experimental data , by analyzing the changes in coupling parameters to predict the possible location and scale of cracks.

[0024] The measurement array formed by stress sensors accurately measures stress components at different depths and, through calculation formulas, derives stress differentials, providing key indicators for determining shale deformation and fracture potential. The data processing unit correlates stress data with other data to calculate strain, further unlocking its value. In particular, strain data is combined with fluid content data to account for the impact of fluid on shale deformation. Using specialized algorithmic model expressions, fitting functions based on experimental data, and analyzing changes in coupling parameters, this allows predictions of the potential location and size of fractures, significantly improving the accuracy and scientific nature of shale fracture detection.

[0025] The filtering process for resistivity data and the smoothing process for fluid content data specifically include: Among them, the filtering formula for resistivity data filtering is: , Where, represents the filtered signal, represents the impulse response of the filter, represents the index of the output sample, represents the index of the filter coefficient, Indicates that a delay operation is performed on the original signal. represents the original input signal sequence, represents the filter length; Among them, the smoothing formula for fluid content data smoothing is: , Where, After smoothing, The value of the data point, represents the raw fluid content data point, Indicates the smoothing window size.

[0026] The filtering formula removes noise from resistivity data and, through clever calculations on the original signal, makes the filtered signal more accurately reflect the true situation. The smoothing formula, based on the original fluid content data and processed according to a specific window size, makes the data more stable and accurate, providing a high-quality data foundation for subsequent analysis and effectively improving the usability of exploration data.

[0027] The pre-processing operations in the data processing unit also include time synchronization of different sensor data; Since the timestamps of data collected by different sensors are different, a time synchronization algorithm is used to adjust the data to the same time base: For timestamps and The time difference between the two sets of data is found by the least squares method. The expression for finding the time difference by the least squares method is: , Where, Indicates the time difference, and Indicates the two sets of data The timestamp of each data point, Indicates the number of data points to ensure temporal consistency across different data sources; The data is spatially registered. When sensors are distributed at different locations, the data at different locations are mapped to a unified spatial coordinate system based on the geological structure of the shale and the relative position information of the sensors. The coordinate transformation formula is: , , , Where, represents the original sensor coordinates, represents the coordinates after registration, Represents the transformation coefficient, ensuring the spatial continuity of the data and providing an accurate data basis for subsequent predictions.

[0028] The time synchronization algorithm uses the least squares method to accurately calculate time differences, eliminating timestamp discrepancies between data collected by different sensors and unifying the data to a common time base, making data at different times comparable. Spatial registration uses coordinate transformation formulas based on geological structure and relative position to map data from different locations to a unified coordinate system, ensuring spatial coherence. This comprehensive preprocessing provides a solid and reliable data foundation for subsequent accurate predictions.

[0029] The prediction unit uses a machine learning algorithm to predict natural fractures; The processed data is used as the input feature vector ,in Representing different characteristics, including: resistivity, fluid content, stress, and strain after pre-processing; Construct SVM classification function: , Where, represents the sample label, represents the Lagrange multiplier, represents the kernel function, represents the bias term; By training a sample set of known shale fracture data and using an optimization algorithm to solve the sum, the classification hyperplane can accurately divide the data with and without fractures, thus realizing the prediction of natural fractures in deep shale.

[0030] This method uses processed multi-category feature data, such as pre-processed resistivity, fluid content, stress, and strain, as input feature vectors. By training on a known shale fracture data set and using an optimization algorithm to solve the Lagrange multiplier and bias term, the classification hyperplane accurately separates data with and without fractures. This approach fully leverages multi-source data to achieve more accurate and efficient prediction of natural fractures in deep shale, providing a scientific basis for exploration and improving efficiency and effectiveness.

[0031] The data storage unit introduces data compression technology based on the redundant storage mechanism to save storage space; For the raw data collected by the sensor, DCT is used for compression: Based on the raw data collected by the sensor, the data sequence Perform DCT transformation, and its transformation formula is: , Where, Represents the first elements, Represents the first data elements, Indicates the length of the original data sequence, represents the kernel function of discrete cosine transform; By setting the threshold and discarding some high-frequency coefficients, data compression can be achieved; When reading data, the compressed data is subjected to inverse DCT transformation to restore the original data. The inverse transformation formula is: .

[0032] A natural fracture prediction system for deep shale, used to implement the natural fracture prediction device for deep shale, comprising: a remote data transmission module and a remote control center; The remote data transmission module uses wireless communication technology to transmit the data collected and processed by the device to the remote control center in real time; After receiving the data, the remote control center backs up and stores the data. The backup storage adopts an off-site backup strategy and stores the data in data centers in different geographical locations. The remote control center sends control instructions to the prediction device. These instructions include adjusting the sampling frequency of the sensor, adjusting the algorithm parameters of the data processing unit, and adjusting the control signal to remotely operate the entire prediction device based on the thresholds and rules set by expert experience according to the different geological conditions of the shale layer.

[0033] The remote data transmission module uses wireless communication to transmit data in real time, ensuring timely delivery of information. The remote control center utilizes an off-site backup storage strategy, significantly enhancing data security. It can accurately send diverse control commands to the prediction device, adjusting parameters based on geological conditions. This allows for flexible remote operation and comprehensively supports the efficient prediction of natural fractures in deep shale.

[0034] After receiving the data, the remote control center uses time series analysis to predict the trend of the collected data: Assume the data sequence is , the expression of the time series analysis model is: , Where, represents the autoregressive coefficient, represents the moving average coefficient, represents a white noise sequence; Determine the model parameters using the minimum information criterion algorithm and , to predict the stress data in the future, where the formula of the minimum information criterion algorithm is: , Where, is the number of parameters of the model, including The natural regression coefficients, Moving average coefficients and a constant term, is the maximum likelihood function of the model; The prediction results are combined with the current prediction model to determine in advance the potential impact of shale internal stress change trends on the formation of natural fractures.

[0035] By combining a time series analysis model with the minimum information criterion algorithm to accurately determine parameters, we can reliably predict future stress data. Combining these predictions with the current model provides forward-looking insights into stress trends within shale formations. This helps predict the potential impact of stress on natural fracture formation, providing a strong basis for decision-making in deep shale exploration, effectively reducing exploration risks, and improving the scientific and safe nature of exploration work.

[0036] The remote control center performs quality assessment on the transmitted data to ensure the reliability of the data; The completeness of the data is measured by calculating the proportion of missing data points: , Where, represents the integrity index, represents the number of missing data points, Represents the total number of data points. If the value falls below the set threshold, a data missing alert will be issued and the location of the missing data will be marked; The standard deviation is used to assess the degree of dispersion of data: , Where, represents the standard deviation, Indicates the actual measurement value, indicates the average value, which is determined based on sensor accuracy and historical data Error range, if If the error range is exceeded, the accuracy of the data is considered to be in doubt and needs to be remeasured or corrected.

[0037] By calculating integrity indicators, we can accurately measure data integrity, promptly detect missing data, issue alerts, and mark locations, thus avoiding analytical bias caused by missing data. Using standard deviation to assess data dispersion and combining sensor accuracy and historical data to determine the error range effectively identifies data of questionable accuracy. This dual guarantee ensures data reliability, lays a solid foundation for subsequent analysis and prediction, and enhances the accuracy and scientific nature of natural fracture prediction.

[0038] After receiving data from the remote data transmission module, the remote control center standardizes the data collected by different sensor types. Because different sensors measure different physical quantities, the data dimensions vary, significantly impacting the accuracy of data analysis. Standardization aligns all data types into specific data intervals, effectively eliminating deviations caused by varying dimensions.

[0039] The remote control center uses principal component analysis (PCA) to reduce the dimensionality of the standardized data. Complex exploration data contains a large amount of redundant information. PCA can deeply explore data characteristics and identify the dominant principal components. This dimensionality reduction not only simplifies data processing and improves computational efficiency, but also prevents interference from excessive minor factors that could affect the analysis of core data, providing a higher-quality data foundation for subsequent accurate prediction of natural fractures in deep shale formations.

[0040] The process of using the present invention is as follows: The deep shale natural fracture prediction device is activated, and the various sensors in the device are activated to begin comprehensively collecting key data from the deep shale area, including but not limited to resistivity, fluid content, and stress state. This data is transmitted in real time to a data processing unit for preprocessing operations such as filtering, smoothing, time synchronization, and spatial registration to ensure data quality and consistency.

[0041] The processed data is fed into the prediction unit, which uses statistical analysis and machine learning algorithms to predict natural fractures in deep shale formations. The prediction results are displayed on the system interface for user reference.

[0042] Users also use the remote control center to remotely operate the device and manage data, including real-time data transmission, backup storage, quality assessment, and trend prediction. The entire process is efficient and accurate, significantly improving the accuracy and efficiency of natural fracture prediction in deep shale formations.

[0043] In summary, the advantages of the present invention include: It integrates multiple sensors to comprehensively collect key deep shale data, laying a solid information foundation for fracture prediction. The data processing unit preprocesses raw data to improve data quality and consistency. The prediction unit accurately predicts the likelihood of natural fractures based on the processed data, enhancing prediction accuracy and reliability. Furthermore, data compression technology and redundant storage mechanisms save space and ensure data security. Furthermore, remote data transmission and a remote control center enable real-time data transmission, backup, and remote operation, enhancing system flexibility and convenience. The remote control center can also assess data quality and predict trends, further enhancing system practicality.

[0044] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A natural fracture prediction device for deep shale, characterized in that: include: sensor unit, data storage unit, data processing unit and prediction unit; The sensor unit is used to deploy a variety of sensors in a deep shale area to collect different types of data, including but not limited to: a resistivity sensor for measuring shale resistivity, a fluid sensor for detecting fluid content in shale, and a stress sensor for sensing shale stress state; The data storage unit is used to store the raw data collected by the sensor and adopts a redundant storage mechanism to ensure the security and reliability of the data; The data processing unit reads data from the data storage unit and performs pre-processing on the data, including filtering the resistivity data and smoothing the fluid content data; The prediction unit uses statistical analysis methods based on the processed data to predict the possibility of natural fractures.

2. The natural fracture prediction device for deep shale according to claim 1, characterized in that: The stress components measured by the stress sensors in the sensor unit include horizontal stress and vertical stress. By arranging several stress sensors at different depths, a stress measurement array is formed. The stress difference is calculated through an algorithm and used as an indicator of shale deformation and possible cracks. The calculation formula of stress difference is: ， Where, represents the stress difference, and represents horizontal stress, represents vertical stress; The data processing unit correlates the stress data with other data for analysis and calculation of strain, and combines the strain data with the fluid content data; Among them, the expression for correlation analysis between stress data and other data is: ， Where, represents strain data, represents Young's modulus, represents Poisson's ratio; When combining strain data with fluid content data for analysis, the influence of fluid on shale deformation is considered, and the algorithm model expression is: ， Where, represents the coupling parameter, represents the function obtained by fitting the experimental data, Indicates fluid content; Fitting functions to experimental data , by analyzing the changes in coupling parameters to predict the possible location and scale of cracks.

3. The natural fracture prediction device for deep shale according to claim 1, characterized in that: The filtering process for resistivity data and the smoothing process for fluid content data are specifically include: Among them, the filtering formula for resistivity data filtering is: ， Where, represents the filtered signal, represents the impulse response of the filter, represents the index of the output sample, represents the index of the filter coefficient, Indicates that a delay operation is performed on the original signal. represents the original input signal sequence, represents the filter length; Among them, the smoothing formula for fluid content data smoothing is: ， Where, After smoothing, The value of the data point, represents the raw fluid content data point, Indicates the smoothing window size.

4. The natural fracture prediction device for deep shale according to claim 1, characterized in that: The pre-processing operations in the data processing unit also include time synchronization of different sensor data; Since the timestamps of data collected by different sensors are different, a time synchronization algorithm is used to adjust the data to the same time base: For timestamps and The time difference between the two sets of data is found by the least squares method. The expression for finding the time difference by the least squares method is: ， Where, Indicates the time difference, and Indicates the two sets of data The timestamp of each data point, Indicates the number of data points to ensure temporal consistency across different data sources; The data is spatially registered. When sensors are distributed at different locations, the data at different locations are mapped to a unified spatial coordinate system based on the geological structure of the shale and the relative position information of the sensors. The coordinate transformation formula is: ， ， ， Where, represents the original sensor coordinates, represents the coordinates after registration, Represents the transformation coefficient, ensuring the spatial continuity of the data and providing an accurate data basis for subsequent predictions.

5. The natural fracture prediction device for deep shale according to claim 1, characterized in that: The prediction unit uses a machine learning algorithm to predict natural fractures; The processed data is used as the input feature vector ,in Representing different characteristics, including: resistivity, fluid content, stress, and strain after pre-processing; Construct SVM classification function: ， Where, represents the sample label, represents the Lagrange multiplier, represents the kernel function, represents the bias term; By training a sample set of known shale fracture data and using an optimization algorithm to solve and, the classification hyperplane can accurately divide the data with and without fractures, thus realizing the prediction of natural fractures in deep shale.

6. The natural fracture prediction device for deep shale according to claim 1, characterized in that: The data storage unit introduces data compression technology based on the redundant storage mechanism to save storage space; For the raw data collected by the sensor, DCT is used for compression: Based on the raw data collected by the sensor, the data sequence Perform DCT transformation, and its transformation formula is: ， Where, Represents the first elements, Represents the first data elements, Indicates the length of the original data sequence, represents the kernel function of discrete cosine transform; By setting the threshold and discarding some high-frequency coefficients, data compression can be achieved; When reading data, the compressed data is subjected to inverse DCT transformation to restore the original data. The inverse transformation formula is: 。 7. A natural fracture prediction system for deep shale, characterized in that: A device for predicting natural fractures in deep shale according to any one of claims 1 to 6, comprising: a remote data transmission module and a remote control center; The remote data transmission module uses wireless communication technology to transmit the data collected and processed by the device to the remote control center in real time; After receiving the data, the remote control center backs up and stores the data. The backup storage adopts an off-site backup strategy and stores the data in data centers in different geographical locations. The remote control center sends control instructions to the prediction device. These instructions include adjusting the sampling frequency of the sensor, adjusting the algorithm parameters of the data processing unit, and adjusting the control signal to remotely operate the entire prediction device based on the thresholds and rules set by expert experience according to the different geological conditions of the shale layer.

8. A natural fracture prediction system for deep shale according to claim 7, characterized in that: After receiving the data, the remote control center uses time series analysis to predict the trend of the collected data: Assume the data sequence is , the expression of the time series analysis model is: ， Where, represents the autoregressive coefficient, represents the moving average coefficient, represents a white noise sequence; Determine the model parameters using the minimum information criterion algorithm and , to predict the stress data in the future, where the formula of the minimum information criterion algorithm is: ， Where, is the number of parameters of the model, including The natural regression coefficients, Moving average coefficients and a constant term, is the maximum likelihood function of the model; The prediction results are combined with the current prediction model to determine in advance the potential impact of shale internal stress change trends on the formation of natural fractures.

9. The natural fracture prediction system for deep shale according to claim 7, characterized in that: The remote control center performs quality assessment on the transmitted data to ensure the reliability of the data; The completeness of the data is measured by calculating the proportion of missing data points: ， Where, represents the integrity index, represents the number of missing data points, Represents the total number of data points. If the value falls below the set threshold, a data missing alert will be issued and the location of the missing data will be marked; The standard deviation is used to assess the degree of dispersion of data: ， Where, represents the standard deviation, Indicates the actual measured value, Represents the mean value, determined based on sensor accuracy and historical data Error range, if If the error range is exceeded, the accuracy of the data is considered to be in doubt and needs to be remeasured or corrected.

10. The natural fracture prediction system for deep shale according to claim 7, characterized in that: After receiving data from the remote data transmission module, the remote control center standardizes the data collected by different types of sensors, unifies various types of data into a specific data range, eliminates the deviation caused by different dimensions of the data, and uses the principal component analysis method to reduce the dimension of the standardized data to find the principal component that plays a dominant role in the data.

Images