Method for evaluating effectiveness of artificial fracturing fracture parameters of horizontal well

Abstract

The invention relates to the technical field of hydraulic fracturing, in particular to a method for evaluating effectiveness of fracture parameters of artificial fracturing of a horizontal well. The method comprises the following steps that information collection based on ground process parameters and underground dynamic parameters is conducted on the horizontal well artificial fracturing construction process, and fracturing working condition data are obtained; according to the fracturing working condition data, the fracture expansion of the horizontal well is monitored in real time, digital conversion processing is conducted, and real-time fracture monitoring data is generated; performing micro-seismic signal analysis on the real-time crack monitoring data, and performing crack network space distribution calculation to generate crack network distribution data; performing reservoir stress field dynamic evolution analysis according to the fracture network distribution data to generate stress field distribution data; and carrying out propping agent migration trajectory tracking according to the stress field distribution data to generate propping agent distribution data. According to the method, the reservoir stress field dynamic evolution analysis and the proppant migration mechanical model are combined, and the effectiveness of the fracture parameters can be evaluated more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic fracturing, and particularly to an evaluation method for the effectiveness of artificial fracturing fracture parameters in horizontal wells. Background Art

[0002] A horizontal well is a commonly used well type design in the process of oil and gas exploitation. It has a higher recovery rate compared to traditional vertical wells, especially in the exploitation of unconventional oil and gas reservoirs (such as shale gas, tight sandstone, etc.). The well section of a horizontal well not only penetrates the formation vertically but also turns and extends along the horizontal direction of the oil and gas layer after reaching the target reservoir. Its main purpose is to maximize the contact area between the wellbore and the oil and gas reservoir, thereby improving the recovery rate of oil and gas.

[0003] Artificial fracturing of horizontal wells is an exploitation technology used in combination with horizontal well drilling technology and is widely used in the development of unconventional oil and gas reservoirs (such as shale gas, tight sandstone, etc.). Through hydraulic fracturing technology, high-pressure fluid is injected into the horizontal well section, forcing the rock formation to generate fractures. Proppants (such as sand) are injected into the fractures to keep the fractures open, thereby improving the fluidity of oil and gas and increasing production. This technology can significantly improve the recovery rate of low-permeability reservoirs and is one of the key technologies in modern oil and gas exploitation.

[0004] However, in the past, fracturing construction lacked real-time monitoring means for downhole fracture propagation, and the monitoring means were often lagging, making it impossible to obtain accurate data during the construction process; it was difficult for past fracturing technologies to track the migration path of proppants, resulting in unclear fracture conductivity; traditional evaluation methods could not effectively combine the spatial distribution of the fracture network with the trajectory of proppant migration, resulting in inaccurate evaluation of fracture conductivity and productivity contribution. Summary of the Invention

[0005] Based on this, it is necessary for the present invention to provide an evaluation method for the effectiveness of artificial fracturing fracture parameters in horizontal wells to solve at least one of the above technical problems.

[0006] To achieve the above object, an evaluation method for the effectiveness of artificial fracturing fracture parameters in horizontal wells includes the following steps: Step S1: Collect information based on surface process parameters and downhole dynamic parameters during the artificial fracturing construction process of a horizontal well to obtain fracturing working condition data; monitor the fracture propagation of the horizontal well in real time according to the fracturing working condition data, and perform digital conversion processing to generate real-time fracture monitoring data; Step S2: Conduct microseismic signal analysis on the real-time fracture monitoring data, calculate the spatial distribution of the fracture network, and generate fracture network distribution data; conduct dynamic evolution analysis of the reservoir stress field based on the fracture network distribution data to generate stress field distribution data; track the migration trajectory of proppants based on the stress field distribution data to generate proppant distribution data; Step S3: Evaluate the conductivity of the proppant distribution data to generate fracture conductivity data; analyze the modified volume of the fracture network distribution data to generate reservoir modified volume data; conduct parameter sensitivity tests on the fracture conductivity data based on the reservoir modified volume data to generate fracture parameter optimization data; Step S4: Identify the multi-stage fracturing area based on the real-time fracture monitoring data to obtain layered modification area data; perform fracture interference mapping on the stress field distribution data and the layered modification area data, and conduct effectiveness evaluation to generate fracture parameter effectiveness data; conduct productivity contribution analysis on the layered modification area data based on the fracture conductivity data and the fracture parameter optimization data to obtain productivity evaluation data; Step S5: Use the productivity evaluation data and the fracture parameter effectiveness data to conduct parameter evaluation to generate comprehensive fracture parameter evaluation data.

[0007] Through the information collection of surface process parameters and downhole dynamic parameters, the present invention can comprehensively capture the complex working condition data occurring during the artificial fracturing process of horizontal wells. This step ensures the real-time monitoring of fracture propagation behavior, and generates real-time fracture monitoring data through digital conversion processing, enabling the visualization of the dynamic changes in fracture propagation. This helps to evaluate the propagation state of fractures in real time and provides accurate basic data for subsequent analysis, improving the timeliness and accuracy of fracture monitoring. The real-time fracture monitoring data combined with microseismic signal analysis can accurately calculate the spatial distribution of the fracture network and generate fracture network distribution data. This enables engineers to master the spatial layout of fracture propagation and provides data support for the subsequent analysis of the reservoir stress field. In addition, through the dynamic evolution analysis of the reservoir stress field, stress field distribution data is generated, further providing a basis for tracking the migration trajectory of proppants and ensuring the effective distribution of proppants in fractures. This step can improve the prediction accuracy of fracture propagation and proppant distribution through a series of parameter calculations. The evaluation of the conductivity of proppant distribution data can accurately predict the conductivity performance of fractures, thereby evaluating the fluid flow capacity of the fracture network for oil and gas. At the same time, the reservoir stimulation volume analysis makes the correlation between the stimulated area of fractures and the reservoir volume clearer. Finally, combining the fracture conductivity data for parameter sensitivity testing can optimize the fracture parameters, improve the accuracy of fracture design, and enhance the accuracy of fracture treatment effect evaluation. The entire step can improve the overall evaluation ability of reservoir stimulation effects. By identifying the multi-stage fracturing areas through real-time fracture monitoring data, the stimulated areas of different intervals in the reservoir can be accurately located, making the fracture design more precise. At the same time, the fracture interference mapping combined with the stress field distribution data enables the evaluation of the interference effect between fractures, generating fracture parameter effectiveness data, providing a basis for further optimization of fracture design. Combining the fracture conductivity data with the fracture parameter optimization data for productivity contribution analysis helps to clarify the contribution of different fracture areas to the overall productivity and improve the accuracy of productivity evaluation. Using the productivity evaluation data and fracture parameter effectiveness data for comprehensive parameter evaluation and generating comprehensive fracture parameter evaluation data can comprehensively evaluate the effects of fractures during the entire fracturing process and their contributions to productivity. This provides a scientific basis for optimizing fracture design and fracturing technology, ensuring the reduction of resource waste and the improvement of the economic benefits of fracturing projects while maximizing productivity. Description of the Drawings

[0008] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings: Figure 1 It is a schematic flow chart of the steps of the method for evaluating the effectiveness of artificial fracturing fracture parameters in horizontal wells of the present invention; Figure 2 For Figure 1 a detailed schematic flow chart of step S1 in Figure 3 For Figure 1 the detailed step flow schematic diagram of step S2 in Specific implementation manner

[0009] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0010] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0012] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an evaluation method for the effectiveness of horizontal well artificial fracture parameters, and the method includes the following steps: Step S1: Collect information based on surface process parameters and downhole dynamic parameters during the horizontal well artificial fracturing construction process to obtain fracturing working condition data; monitor the horizontal well fracture propagation in real time according to the fracturing working condition data, and perform digital conversion processing to generate real-time fracture monitoring data; Step S2: Analyze the microseismic signals of the real-time fracture monitoring data, calculate the spatial distribution of the fracture network, and generate fracture network distribution data; analyze the dynamic evolution of the reservoir stress field according to the fracture network distribution data to generate stress field distribution data; track the migration trajectory of the proppant according to the stress field distribution data to generate proppant distribution data; Step S3: Evaluate the conductivity of the proppant distribution data to generate fracture conductivity data; analyze the modified volume of the fracture network distribution data to generate reservoir modified volume data; perform parameter sensitivity tests on the fracture conductivity data based on the reservoir modified volume data to generate optimized fracture parameter data; Step S4: Identify the multi-stage fracturing area based on the real-time fracture monitoring data to obtain stratified modification area data; map the fracture interference between the stress field distribution data and the stratified modification area data and perform effectiveness evaluation to generate fracture parameter effectiveness data; analyze the productivity contribution of the stratified modification area data based on the fracture conductivity data and the optimized fracture parameter data to obtain productivity evaluation data; Step S5: Use the productivity evaluation data and the fracture parameter effectiveness data to perform parameter evaluation to generate comprehensive fracture parameter evaluation data.

[0013] In the embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow of the method for evaluating the effectiveness of horizontal well artificial fracturing fracture parameters of the present invention. In this example, the method for evaluating the effectiveness of horizontal well artificial fracturing fracture parameters includes the following steps: Step S1: Collect information on the horizontal well artificial fracturing construction process based on surface process parameters and downhole dynamic parameters to obtain fracturing working condition data; perform real-time monitoring on the horizontal well fracture propagation according to the fracturing working condition data and perform digital conversion processing to generate real-time fracture monitoring data; In the embodiment of the present invention, during the horizontal well artificial fracturing construction process, information is collected based on surface process parameters and downhole dynamic parameters. The surface process parameters include fracturing fluid injection rate, fracturing pressure, proppant concentration, etc.; the downhole dynamic parameters are collected through a downhole fiber optic temperature measurement system, a downhole pressure sensor array, and an acoustic detection system, including wellbore temperature distribution, sectional pressure data, and acoustic signal data. After all the collected data is time-synchronized and calibrated, fracturing working condition data is obtained. Based on this data, a real-time fracture propagation monitoring system is used, combined with downhole microseismic signals, to track the fracture propagation in real time. Through digital conversion, the fracture propagation situation is converted into real-time fracture monitoring data. In a specific application scenario, the fracturing fluid injection rate is set to 20 m³ / min, the proppant concentration is controlled at 0.5 kg / L, and the real-time monitoring frequency is 10 times per second, and finally high-precision fracture propagation data is generated.

[0014] Step S2: Analyze the microseismic signals of the real-time fracture monitoring data and calculate the spatial distribution of the fracture network to generate fracture network distribution data; perform dynamic evolution analysis of the reservoir stress field based on the fracture network distribution data to generate stress field distribution data; track the proppant migration trajectory based on the stress field distribution data to generate proppant distribution data; According to the real-time fracture monitoring data generated in step S1 of the embodiments of the present invention, first, microseismic signals are analyzed, the effective waveforms in the microseismic signals are extracted, and noise interference is removed through spectral analysis to obtain the source location. A source distribution model is constructed using three-dimensional space coordinates, and source location calculations are performed to generate the spatial distribution data of the fracture network. Then, based on the spatial distribution of the fracture network, an initial model of the reservoir stress field is constructed, and spatio-temporal evolution analysis is carried out. Considering the heterogeneity of the reservoir, the dynamic response of the stress field is simulated to generate stress field distribution data. Next, based on the stress field distribution data, the flow-solid coupling model is used to simulate and track the migration trajectory of the proppant to ensure the effective placement of the proppant, and finally, the distribution data of the proppant is obtained. In practical applications, the error of source location is less than 10 meters, and the time step of the stress field dynamic simulation is set to 1 hour.

[0015] Step S3: Evaluate the conductivity of the proppant distribution data to generate fracture conductivity data; analyze the modified volume of the fracture network distribution data to generate reservoir modified volume data; perform parameter sensitivity tests on the fracture conductivity data according to the reservoir modified volume data to generate fracture parameter optimization data; Based on the proppant distribution data generated in step S2 of the embodiments of the present invention, first, the fracture conductivity is evaluated. By analyzing the spatial distribution characteristics of the proppant, calculating its residence and embedding depths in the fracture, and combining with the fracture surface morphology, a fracture conductivity model is established to obtain the fracture conductivity data. At the same time, using the fracture network distribution data, a fracture network volume analysis model is constructed to calculate the reservoir modified volume. After the modified volume analysis is completed, the modified volume data is used for parameter sensitivity tests of the fracture conductivity to identify the influence of different fracture parameters on the conductivity, so as to optimize the fracture parameters and generate fracture parameter optimization data. In actual construction, the spatio-temporal resolution of the fracture conductivity evaluation is once every 30 minutes, and the fracture parameter sensitivity tests cover key parameters such as conductivity, fracture length, and fracture width.

[0016] Step S4: Identify the multi-stage fracturing area according to the real-time fracture monitoring data to obtain the stratified modification area data; perform fracture interference mapping on the stress field distribution data and the stratified modification area data, and conduct effectiveness evaluation to generate fracture parameter effectiveness data; perform productivity contribution analysis on the stratified modification area data according to the fracture conductivity data and the fracture parameter optimization data to obtain productivity evaluation data; Based on the real-time crack monitoring data, the embodiment of the present invention determines the transformation areas of different layers through a multi-stage fracturing area identification algorithm and generates stratified transformation area data. Then, the stress field distribution data generated in step S2 is spatially mapped and superimposed with the stratified transformation area data to analyze the crack interference between different layers. The crack interference mapping model is used to evaluate the interference intensity between cracks to obtain crack parameter effectiveness data. Finally, combining the crack conductivity data and the crack parameter optimization data, a productivity contribution analysis is carried out to identify the contributions of different stratified transformation areas to the overall productivity and generate productivity evaluation data. In actual operation, the resolution of the layer identification algorithm is 10 meters, the time step of the crack interference intensity evaluation is 2 hours, and the productivity contribution rate calculation covers the permeability and pressure changes of each layer.

[0017] Step S5: Use the productivity evaluation data and the crack parameter effectiveness data to perform parameter evaluation and generate comprehensive crack parameter evaluation data.

[0018] Based on the productivity evaluation data and the crack parameter effectiveness data obtained in step S4, the embodiment of the present invention first conducts a comprehensive evaluation of the crack parameters by comparing and analyzing the effects of different crack parameters on the overall productivity, identifies the crack parameter combinations that contribute more to the productivity, and generates comprehensive crack parameter evaluation data. During the evaluation process, factors such as crack conductivity, reservoir stimulation volume, and crack interference intensity are comprehensively considered, and finally the optimal crack parameter scheme is obtained. In practical applications, the finite element analysis method is used for the crack parameter evaluation model, the calculation time for crack parameter optimization is 24 hours, and the accuracy of the finally determined crack parameters is controlled within ±5%.

[0019] Through the information collection of surface process parameters and downhole dynamic parameters, the present invention can comprehensively capture the complex working condition data occurring during the artificial fracturing process of horizontal wells. This step ensures the real-time monitoring of the fracture propagation behavior, and generates real-time fracture monitoring data through digital conversion processing, enabling the visualization of the dynamic changes in fracture propagation. This helps to evaluate the propagation state of the fracture in real time, and provides accurate basic data for subsequent analysis, improving the timeliness and accuracy of fracture monitoring. The real-time fracture monitoring data combined with microseismic signal analysis can accurately calculate the spatial distribution of the fracture network, generating fracture network distribution data. This enables engineers to master the spatial layout of fracture propagation, providing data support for the subsequent analysis of the reservoir stress field. In addition, through the dynamic evolution analysis of the reservoir stress field, stress field distribution data is generated, further providing a basis for tracking the migration trajectory of proppants and ensuring the effective distribution of proppants in the fractures. This step can improve the prediction accuracy of fracture propagation and proppant distribution through a series of parameter calculations. The evaluation of the conductivity of the proppant distribution data can accurately predict the conductivity performance of the fracture, thereby evaluating the flow capacity of the fracture network for oil and gas. At the same time, the reservoir stimulation volume analysis makes the correlation between the stimulated area of the fracture and the reservoir volume clearer. Finally, combining the fracture conductivity data for parameter sensitivity testing can optimize the fracture parameters, improving the accuracy of fracturing design and the accuracy of fracturing effect evaluation. The entire step can improve the overall evaluation ability of reservoir stimulation effects. By identifying the multi-stage fracturing areas through real-time fracture monitoring data, the stimulated areas of different intervals in the reservoir can be accurately located, making the fracturing design more accurate. At the same time, the fracture interference mapping combined with the stress field distribution data enables the evaluation of the interference effect between fractures, generating fracture parameter effectiveness data, providing a basis for further optimization of the fracturing design. Combining the fracture conductivity data with the fracture parameter optimization data for productivity contribution analysis helps to clarify the contribution of different fracture areas to the overall productivity, improving the accuracy of productivity evaluation. Using the productivity evaluation data and fracture parameter effectiveness data for comprehensive parameter evaluation, generating comprehensive fracture parameter evaluation data, can comprehensively evaluate the effect of fractures during the entire fracturing process and their contribution to productivity. This provides a scientific basis for optimizing fracture design and fracturing technology, ensuring that under the premise of maximizing productivity, resource waste is reduced and the economic benefits of fracturing projects are improved.

[0020] Preferably, step S1 includes the following steps: Step S11: Collect parameters based on the injection of fracturing fluid, the addition of proppants, and the performance of fracturing fluid during the artificial fracturing construction process of horizontal wells, so as to obtain comprehensive surface process data; Step S12: Collect downhole dynamic parameters of the horizontal well to obtain downhole dynamic comprehensive data. Specifically, the downhole dynamic parameter collection is to collect wellbore temperature distribution data through a downhole fiber optic temperature measurement system, collect sectional pressure data through a downhole pressure sensor array, collect acoustic signal data through an acoustic detection system, and combine the temperature distribution data, sectional pressure data, and acoustic signal data into downhole dynamic comprehensive data; Step S13: Perform time synchronization calibration on the surface process comprehensive data and the downhole dynamic comprehensive data to obtain fracturing condition data; Step S14: Identify the fracture initiation pressure based on the fracturing condition data and track the fracture propagation in real time to obtain fracture propagation tracking data; Step S15: Digitally encode the fracture propagation tracking data, perform spatial discretization processing, and establish a grid data structure to obtain grid monitoring data; Step S16: Standardize the grid monitoring data to generate real-time fracture monitoring data.

[0021] As an embodiment of the present invention, refer to Figure 2 shown in Figure 1 is a detailed step flow schematic diagram of step S1 in Step S11: Collect parameters based on the injection of fracturing fluid, addition of proppant, and properties of fracturing fluid during the artificial fracturing construction process of the horizontal well to obtain surface process comprehensive data; In the embodiment of the present invention during the artificial fracturing construction process of the horizontal well, first, parameters such as the injection rate, pressure, and volume of the fracturing fluid are collected in real time through the fracturing fluid injection equipment. Then, the proppant injection control system is used to record the injection time, injection concentration, and total injection volume of the proppant. At the same time, a fracturing fluid property monitor is used to monitor the viscosity, density, and proppant suspension performance of the fracturing fluid in real time. All the collected parameters will be automatically summarized into the surface process comprehensive data management system. In practical applications, the fracturing fluid injection rate is set to 15 m³ / min, the proppant concentration is 0.3 kg / L, and the fracturing fluid viscosity is controlled within the range of 50 - 70 cP to ensure the continuity and stability of the fracturing construction.

[0022] Step S12: Collect downhole dynamic parameters of the horizontal well to obtain downhole dynamic comprehensive data. Specifically, the downhole dynamic parameter collection is to collect wellbore temperature distribution data through a downhole fiber optic temperature measurement system, collect sectional pressure data through a downhole pressure sensor array, collect acoustic signal data through an acoustic detection system, and combine the temperature distribution data, sectional pressure data, and acoustic signal data into downhole dynamic comprehensive data; In the embodiments of the present invention, in order to obtain downhole dynamic parameters, first, sensors are arranged along the wellbore through a downhole optical fiber temperature measurement system to collect the longitudinal distribution data of the wellbore temperature in real time. Secondly, pressure sensors are installed in each fracturing stage to record the pressure change curves of each stage, ensuring that the data accurately covers the entire fracturing construction process. Finally, an acoustic wave detection system is used to monitor acoustic wave signals, which can reflect the fracture propagation behavior and the placement of proppants. All the temperature distribution data, stage pressure data, and acoustic wave signal data are integrated through a data processing platform to generate downhole dynamic comprehensive data. In an actual scenario, the resolution of the temperature sensor is 0.1 °C, the sampling frequency of the pressure sensor is 10 Hz, and the sampling frequency of the acoustic wave detection is 1 kHz.

[0023] Step S13: Perform time synchronization calibration on the ground process comprehensive data and the downhole dynamic comprehensive data to obtain fracturing condition data; In the embodiments of the present invention, the ground process comprehensive data obtained in step S11 and the downhole dynamic comprehensive data in step S12 are subjected to time synchronization calibration. First, the time stamp technology is used to align the time axes of the two data sources to ensure that all data records are accurately matched. During the time synchronization process, problems such as data acquisition delay and sampling frequency asynchronization need to be addressed. Through an interpolation algorithm, the sampling frequency is unified and the delay is compensated to ensure the integrity and consistency of the data. Finally, the calibrated data generates fracturing condition data for subsequent analysis. In practical applications, the time synchronization accuracy is controlled within the second level range to ensure that the downhole dynamic data and the ground process data synchronously reflect the construction status.

[0024] Step S14: Identify the fracture initiation pressure based on the fracturing condition data and track the fracture propagation in real time to obtain fracture propagation tracking data; In the embodiments of the present invention, based on the fracturing condition data generated in step S13, first, the fracture initiation pressure is calculated using a reservoir engineering model. By analyzing the pressure change curve, the initiation point is determined and verified in combination with the reflection characteristics of the downhole acoustic wave signals. After the fracture initiates, the fracture propagation is tracked in real time. Using microseismic monitoring technology combined with the pressure change of the fracturing fluid, the dynamic data of the fracture propagation is obtained, and parameters such as the azimuth, length, and width of the fracture propagation are recorded. In actual operation, the determination error of the initiation pressure is less than 0.5 MPa, and the time interval for fracture propagation tracking is recorded every 5 minutes to ensure accuracy and real-time performance.

[0025] Step S15: Digitally encode the fracture propagation tracking data and perform spatial discretization processing to establish a grid data structure to obtain grid monitoring data; The embodiment of the present invention digitally encodes the fracture extension tracking data, first converts it into discrete data points in chronological order, and discretizes the spatial coordinates of each data point. Using a three-dimensional grid generation algorithm, the fracture extension data is divided and encoded according to the grid cells of the three-dimensional space of the well to generate a gridded data structure. In practical applications, the size of the grid cell is controlled within a resolution range of 1 meter × 1 meter × 1 meter to ensure an accurate description of the spatial distribution of the fracture. After the data is discretized, all relevant information on the fracture extension will be mapped to the grid cells for subsequent numerical analysis and simulation processing.

[0026] Step S16: Standardize the gridded monitoring data to generate real-time crack monitoring data.

[0027] The embodiment of the present invention performs standardization processing on the gridded monitoring data generated in step S15. First, the data in each grid unit is normalized to ensure that the data of different physical quantities can be uniformly compared. Then, a data cleaning algorithm is used to remove abnormal data points, and difference filling is performed to ensure the integrity and consistency of the data. After standardization, the data format is unified, and real-time crack monitoring data is generated for subsequent analysis. In practical applications, the normalization range used in the standardization process is 0 to 1, and the data cleaning rule is set as data points that exceed the mean value ±3 times the standard deviation will be eliminated.

[0028] By comprehensively collecting the parameters of fracturing fluid injection, proppant addition, and fracturing fluid properties, the present invention can accurately capture the comprehensive data of the surface process. This provides a complete surface process background for subsequent analysis, ensures that the operation data of each link is recorded, and helps improve the transparency and traceability of the entire fracturing process. In addition, the acquisition of comprehensive surface process data can provide an accurate basis for optimizing fracturing design and real-time adjustment of fracturing strategies, enhancing the effectiveness and precision of fracturing operations. The acquisition of downhole dynamic parameters includes key parameters such as temperature, pressure, and acoustic signals, comprehensively reflecting the actual conditions downhole. The combined application of an optical fiber temperature measurement system, a pressure sensor array, and an acoustic detection system can monitor the dynamic changes of downhole fractures in multiple dimensions. Integrating these data into comprehensive downhole dynamic data provides a clear description of the complex downhole environment. The advantage of this step is that through the collaborative work of multiple sensors, the real-time development state of downhole fractures can be accurately grasped, providing a data basis for further analysis of fracture propagation. The time synchronization and calibration of comprehensive surface process data and comprehensive downhole dynamic data ensure the matching of surface and downhole data. The core of this step is to synchronize the data from the two sources through precise calibration in the time dimension, so that the fracturing condition data can better reflect the downhole changes during the actual construction process. This synchronization process can effectively reduce data deviation and provide more reliable basic data for subsequent fracture propagation tracking and evaluation. Identifying the fracture initiation pressure based on the fracturing condition data can accurately identify when and where the fracture starts to initiate, thus guiding the injection strategy of fracturing fluid and the addition strategy of proppant during the construction process. Real-time tracking of fracture propagation data helps dynamically adjust the fracturing plan to ensure that each link in the fracture propagation process is effectively controlled. This step can significantly improve the real-time monitoring accuracy of fracture propagation and reduce the risk of out-of-control fracture propagation. By digitally encoding and spatially discretizing the fracture propagation tracking data, the complex data of fracture propagation can be gridified to establish a unified grid data structure. This structured data makes the monitoring of fracture propagation more systematic and intuitive, facilitating subsequent data analysis and display. The gridified monitoring data helps improve the efficiency and accuracy of data processing, making subsequent standardized processing smoother. Standardizing the gridified monitoring data ensures the consistency of different data sources and forms. This step helps eliminate the deviation between data, making the finally generated real-time fracture monitoring data more reliable and comparable. The real-time fracture monitoring data provides a stable and standardized basis for the overall evaluation of fracture propagation, facilitating further analysis and decision-making. This processing process improves the accuracy of fracture propagation monitoring and makes the control of the fracturing process more intelligent and refined.

[0029] Preferably, step S2 includes the following steps: Step S21: Extract the microseismic signal waveforms from the real-time crack monitoring data, and eliminate the noise interference through spectrum analysis to obtain the processed microseismic signal data; Step S22: Establish a source distribution model based on three-dimensional space coordinates according to the processed microseismic signal data, and perform source location calculation to obtain the source location data; Step S23: Identify the crack propagation direction based on the real-time crack monitoring data and the source location data, and perform topological structure analysis on the crack network connectivity to obtain the crack network distribution data; Step S24: Reconstruct the in-situ stress field for the crack network distribution data, and establish a stress superposition model based on crack interference to obtain the initial stress field data; Step S25: Perform spatio-temporal evolution analysis of the reservoir stress field based on the initial stress field data considering formation heterogeneity, and conduct dynamic response simulation of the stress field to obtain the stress field distribution data; Step S26: Construct a proppant transport mechanics model based on the stress field distribution data, and simulate the proppant migration path based on fluid-solid coupling calculation according to the proppant transport mechanics model, and perform real-time position tracking to generate the proppant distribution data.

[0030] As an embodiment of the present invention, refer to Figure 3 shown in Figure 1 is a detailed step flow diagram of step S2 in Step S21: Extract the microseismic signal waveforms from the real-time crack monitoring data, and eliminate the noise interference through spectrum analysis to obtain the processed microseismic signal data; In the embodiment of the present invention, the microseismic signal waveforms are extracted from the real-time crack monitoring data. First, the microseismic signals in the monitoring data are preprocessed through a filter to extract the effective microseismic waveforms. Then, spectrum analysis technology is used to perform frequency domain conversion on the extracted waveforms and analyze their frequency distribution characteristics. By setting a frequency threshold, the noise signals are removed to effectively eliminate the noise interference and obtain clearer processed microseismic signal data. In practical applications, the usually set frequency threshold range is between 5Hz - 50Hz to exclude the influence of environmental noise and ensure the accuracy of the microseismic signals.

[0031] Step S22: Establish a source distribution model based on three-dimensional space coordinates according to the processed microseismic signal data, and perform source location calculation to obtain the source location data; In the embodiment of the present invention, based on the microseismic signal processing data obtained in step S21, a source distribution model based on three-dimensional space coordinates is used to accurately locate the source position. First, the preliminary position of the source is determined by using the source time difference and the spatial arrangement of the sensors, and then the source is refined by an inversion algorithm to correct the error and obtain the final source location data. In actual operation, the spatial error of the source location is controlled within 10 meters, and the grid resolution of the source distribution model is 5 m × 5 m × 5 m to ensure the accuracy of the source position.

[0032] Step S23: Identify the crack propagation direction based on the real-time crack monitoring data and the source location data, and perform a topological structure analysis on the crack network connectivity to obtain the crack network distribution data; In the embodiment of the present invention, by combining the real-time crack monitoring data and the source location data, a pattern recognition algorithm is used to analyze the crack propagation direction, and by calculating parameters such as the crack propagation speed and angle, the main crack propagation direction is identified. Then, a topological structure analysis method is used to model the crack network, analyze the connectivity between cracks, and construct a topological structure diagram of the crack network. In practical applications, the connectivity rate of the crack network topological structure needs to reach more than 85% to ensure the effective description of the crack network and the accuracy of the connectivity analysis.

[0033] Step S24: Reconstruct the in-situ stress field for the crack network distribution data, establish a stress superposition model based on crack interference, and thus obtain the initial stress field data; In the embodiment of the present invention, based on the crack network distribution data generated in step S23, a numerical simulation method is used to reconstruct the in-situ stress field. First, a stress superposition model based on crack interference is established according to the spatial position, size and connectivity of the cracks. By considering the stress conduction and influence of the crack network on the surrounding formation, the stress change of each crack area is calculated to obtain the initial stress field data. In practical applications, the calculation accuracy of the model is set to one unit per 5 meters to ensure that the details of the in-situ stress field reconstruction are fine enough to accurately reflect the influence of cracks on the stress field.

[0034] Step S25: Perform a spatio-temporal evolution analysis of the reservoir stress field based on the initial stress field data considering formation heterogeneity, and conduct a dynamic response simulation of the stress field to obtain the stress field distribution data; In the embodiments of the present invention, the initial stress field data is utilized to conduct spatio-temporal evolution analysis on the reservoir stress field. First, based on the heterogeneity characteristics of the formation, a dynamic model of the reservoir stress field is constructed to simulate the stress changes in the reservoir at different time periods. Then, through the finite element analysis method, the dynamic response of the reservoir stress field is simulated to obtain the distribution data of the stress field. In actual operation, the time step of the spatio-temporal evolution analysis is set to 1 hour, and the spatial unit resolution is 10 meters to ensure the accurate simulation of the evolution process of the reservoir stress field.

[0035] Step S26: Based on the stress field distribution data, a mechanical model for proppant migration is constructed, and the proppant migration path simulation based on fluid-solid coupling calculation is carried out for real-time position tracking, thereby generating proppant distribution data.

[0036] In the embodiments of the present invention, based on the stress field distribution data generated in step S25, a mechanical model for proppant migration is established. This model takes into account the stress field changes in the reservoir, fluid pressure, and the physical properties of the proppant to calculate the migration path of the proppant in the fracture. Using the fluid-solid coupling calculation method, the real-time migration path of the proppant is simulated, and the real-time position of the proppant is tracked in combination with the actual fracturing construction parameters to generate proppant distribution data. In practical applications, the migration speed of the proppant is controlled within 0.1 - 1 m / minute, and the error of real-time position tracking is less than 5 meters to ensure the accuracy of the proppant distribution data.

[0037] Through microseismic signal waveform extraction and spectral analysis, the present invention can effectively distinguish noise interference from effective signals, ensuring that the extracted microseismic signals are more accurate and reliable. The elimination of noise interference in this step can improve the resolution of microseismic monitoring, making the processed microseismic signal data clearer and more accurate, thereby providing a high-quality data basis for subsequent crack propagation and seismic source location, and enhancing the reliability of the overall analysis. By using the processed microseismic signal data to establish a seismic source distribution model based on three-dimensional space coordinates and through seismic source location calculation, the seismic source positions of crack propagation can be accurately identified. This provides intuitive geographical location information for the judgment of crack propagation direction and the analysis of crack network connectivity, making the monitoring more three-dimensional and refined, contributing to the comprehensive understanding of the dynamic process of crack propagation, and improving the evaluation accuracy of crack network connectivity. The identification of crack propagation direction and the topological structure analysis of crack network connectivity help to construct a spatial distribution map of the crack network and reveal the trend and network structure of the cracks. Through this analysis, the connectivity of the crack network can be accurately judged, potential production layer connection paths can be identified, thereby providing a scientific basis for optimizing the fracturing construction plan and subsequent reservoir transformation, and improving the effectiveness and production efficiency of the crack network. The crack network distribution data is used to reconstruct the in-situ stress field and establish a stress superposition model based on crack interference, which can accurately reflect the influence of crack propagation on the reservoir stress field. The establishment of the stress superposition model can better understand the stress changes during crack propagation, lay a foundation for subsequent analysis of the evolution of the reservoir stress field, enable the effective evaluation and monitoring of the interference effect of cracks, and enhance the effect of reservoir stress management. The spatio-temporal evolution analysis of the reservoir stress field based on the initial stress field data can comprehensively reveal the variation law of the reservoir stress with time and space. Especially when considering the formation heterogeneity factors, it can more accurately simulate the dynamic response process of the reservoir. The dynamic response simulation of the stress field can predict the stress change trend of the reservoir in the future for a period of time, thereby providing reliable stress field distribution data for the design of proppant migration paths and the optimization of reservoir transformation. The proppant migration mechanical model constructed based on the stress field distribution data can simulate the migration path of proppants in the crack network and track the migration process of proppants in real time through fluid-solid coupling calculation. This real-time tracking of proppant positions can ensure the reasonable distribution of proppants, improve their conductivity in the cracks, and effectively prevent the deposition or deviation of proppants, thereby ensuring the maximization of fracturing effect and enhancing the recovery rate of the reservoir.

[0038] Preferably, step S22 includes the following steps: Step S221: Extract the P-wave and S-wave first arrival times from the processed microseismic signal data based on a waveform recognition algorithm, thereby obtaining wave arrival time data; In the embodiment of the present invention, waveform recognition is performed on the microseismic signal processing data. The waveform recognition algorithm based on adaptive filtering and template matching is adopted to extract the arrival times of P-waves and S-waves. First, the signal is preprocessed to filter out high-frequency noise and environmental interference, and then the short-time Fourier transform (STFT) is used to analyze the spectral changes to locate the arrival points of P-waves and S-waves. The arrival time is determined by combining the manually adjusted threshold to generate accurate wave arrival time data. In practical applications, the error of the arrival times of P-waves and S-waves should generally be controlled within 0.1 millisecond to ensure the accuracy of the wave arrival time data.

[0039] Step S222: Establish a velocity model based on the travel time inversion equations and the ray tracing method according to the wave arrival time data to obtain the velocity field distribution data; In the embodiment of the present invention, according to the wave arrival time data in step S221, the travel time inversion equations are constructed by using the ray tracing method. First, a three-dimensional initial velocity model is established according to the actual formation conditions. Through the simulation of the ray propagation paths in different geological units and combined with the travel time data, the model is inverted to correct the velocity field. In the inversion process, the steepest path algorithm is used to calculate the wave propagation path to correct the non-uniformity of the velocity model, and finally the accurate velocity field distribution data is obtained. In actual operation, the resolution of the velocity field distribution is set to 5 meters, and the inversion accuracy of each iteration is 0.01 km / s to ensure the reliability of the velocity field model.

[0040] Step S223: Perform source parameter inversion based on the velocity field distribution data, and perform least squares optimization calculation on the source location to obtain the initial positioning result data; In the embodiment of the present invention, the velocity field distribution data is used to perform the inversion calculation of the source parameters. By fitting parameters such as the source location, source mechanism, and source strength, and using the optimization algorithm based on the least squares method, the source location is calculated and corrected to obtain the initial positioning result data. In the specific calculation process, first, multiple groups of ground and downhole sensor data are selected, and the relationship between the wave arrival time and the source location is used for nonlinear optimization, with the error controlled within 1 meter to ensure the accuracy of the source parameter inversion.

[0041] Step S224: Analyze the positioning error of the initial positioning result data to obtain the positioning accuracy evaluation data, where the positioning error analysis is specifically to establish an error ellipse evaluation model based on the covariance matrix and use the error ellipse evaluation model to evaluate the positioning accuracy of the initial positioning result data; In the embodiments of the present invention, positioning error analysis is performed on the initial positioning result data, and an error ellipse evaluation model based on the covariance matrix is adopted. This model calculates the positioning errors in different directions according to the spatial distribution of sensors, signal quality, and the change of the source depth during the source positioning process, and analyzes the positioning accuracy through the major and minor axes of the error ellipse. In practical applications, the major axis of the error ellipse does not exceed 20 meters, and the minor axis is controlled within 5 meters to ensure the rationality and effectiveness of the positioning accuracy evaluation data.

[0042] Step S225: Perform error correction on the initial positioning result data according to the positioning accuracy evaluation data, and use the double-difference positioning algorithm to perform precise positioning calculation to obtain corrected positioning data; In the embodiments of the present invention, error correction is performed on the initial positioning result data based on the positioning accuracy evaluation data, and the double-difference positioning algorithm is adopted for further optimization. By calculating the arrival time differences between multiple seismic sources, common errors are eliminated, and precise relative positioning correction is performed. After optimization, the absolute positions of each seismic source are recalculated to obtain corrected positioning data with higher accuracy. In practical applications, the error range of the double-difference positioning algorithm is usually less than 2 meters to ensure that the final positioning accuracy of the seismic source reaches the highest standard.

[0043] Step S226: Perform three-dimensional space mapping on the corrected positioning data, establish a seismic source distribution model based on the geographical coordinate system, and perform spatial clustering analysis to obtain seismic source positioning data.

[0044] In the embodiments of the present invention, the corrected seismic source positioning data is subjected to three-dimensional space mapping to establish a seismic source distribution model based on the geographical coordinate system. This model matches the spatial positions of the seismic sources with the actual geographical coordinates to generate a three-dimensional seismic source distribution map, and analyzes the distribution characteristics and connectivity of the seismic sources through a spatial clustering algorithm. In the application scenario, the radius threshold set by the clustering algorithm is 10 meters, which is used to identify seismic source clusters and analyze the spatial distribution law of microseismic events, and finally obtain accurate seismic source positioning data.

[0045] Through the extraction of the arrival times of P-waves and S-waves based on the waveform recognition algorithm, the present invention can accurately capture the arrival time points of microseismic events, thereby obtaining more accurate wave arrival time data. The application of this algorithm can effectively improve the accuracy of seismic source location. Especially in complex strata, by accurately extracting the arrival times of P-waves and S-waves, it helps to provide a reliable time data basis for subsequent seismic source parameter inversion and seismic source location calculation. According to the wave arrival time data, a velocity model based on the travel time inversion equation set and ray tracing method is established, which can effectively calculate the velocity field distribution of the strata. This step, through the accurate inversion of the formation velocity, can ensure the accuracy of the wave propagation velocity in seismic source location, thereby improving the overall accuracy of microseismic event location and providing key velocity field data support for subsequent seismic source parameter inversion and location calculation. Based on the velocity field distribution data, seismic source parameter inversion is carried out, and the seismic source location is calculated through least squares optimization, and a relatively accurate initial location result can be obtained. The optimization calculation of the least squares method can reduce the possible errors in the wave propagation process, ensure that the preliminary calculation result of the seismic source location has a high accuracy, and thus provide reliable initial data for subsequent location error analysis. Through the establishment of an error ellipse evaluation model of the covariance matrix, the location error analysis can accurately quantify the error range in the initial location result. Using the error ellipse evaluation model, the accuracy of seismic source location can be effectively evaluated, the possible error directions and ranges can be clarified, and a scientific basis can be provided for subsequent location error correction. The improvement of location accuracy can significantly improve the accuracy of judging the fracture propagation direction. According to the location accuracy evaluation data, the initial location result is corrected for errors, and the double-difference location algorithm is used to further optimize the location accuracy. The double-difference location algorithm can significantly improve the location accuracy of the seismic source by eliminating systematic errors and path errors, ensuring that the final corrected location data is more accurate. This precise location calculation is crucial for the efficiency of the microseismic monitoring system and helps to accurately judge the fracture propagation dynamics. The corrected location data is mapped in three-dimensional space, and a seismic source distribution model is established in combination with the geographic coordinate system, which can visually display the spatial distribution of seismic sources. At the same time, through spatial clustering analysis, the aggregation areas of seismic sources can be identified, and the connectivity and distribution characteristics of the fracture network can be judged. The finally obtained seismic source location data can provide a more accurate spatial layout basis for reservoir stimulation optimization, ensuring the maximization of fracturing effects.

[0046] Preferably, step S25 includes the following steps: Step S251: Based on the inhomogeneity characteristics of the formation, the physical parameters of the reservoir are collected based on the rock elastic modulus, Poisson's ratio, rock compressive strength, and porosity distribution to generate reservoir physical property parameter data; In the embodiments of the present invention, based on the heterogeneity characteristics of the reservoir, physical parameters of the reservoir are collected. First, using acoustic logging data and core analysis data, the elastic modulus, Poisson's ratio, and compressive strength of the rock are calculated respectively, and porosity distribution data is obtained through nuclear magnetic resonance logging. For the elastic modulus and Poisson's ratio, the equivalent medium theory is used to interpolate and calculate different geological layers to ensure the rationality of the spatial distribution of the parameters. The porosity distribution data is supplemented by comparing logging curves and calibrating through core experiments. Finally, the generated reservoir physical property parameter data includes the elastic modulus, Poisson's ratio, compressive strength, and porosity data of each layer, with a resolution of 1 meter.

[0047] Step S252: Classify the formation heterogeneity according to the reservoir physical property parameter data and the fracture network distribution data, construct a multi-level stress field model, and generate basic data of stress field heterogeneity; In the embodiments of the present invention, the formation heterogeneity is classified according to the reservoir physical property parameter data and the fracture network distribution data. The specific method is as follows: First, according to the spatial variation of the physical properties, the reservoir is stratified, and the hierarchical clustering algorithm is used to divide the regions with similar physical properties into different heterogeneous units. Then, the geometric distribution of the fracture network is superimposed and analyzed with these heterogeneous units to establish a multi-level stress field model. Through numerical simulation, the stress transfer characteristics of each layer are calculated, and finally, the basic data of stress field heterogeneity is generated. This data mainly includes the stress field change trend of different layers and the spatial distribution of local stress concentration areas.

[0048] Step S253: Conduct a stress field superposition analysis based on the basic data of stress field heterogeneity and the initial stress field data, and calculate the local stress concentration area to obtain stress concentration data; In the embodiments of the present invention, a stress field superposition analysis is conducted based on the basic data of stress field heterogeneity and the initial stress field data. The finite element analysis (FEA) method is used to calculate the superposition of local stresses in the stress fields of different layers, especially focusing on the stress concentration phenomena at the fracture endpoints and proppant accumulation areas. Through the superposition calculation of the stress distribution, the local stress concentration areas are determined, and stress concentration data is generated. In practical applications, a grid division of 1 meter × 1 meter is adopted to conduct a refined analysis of the local stress changes, and the accuracy of the stress concentration data can reach 0.1 MPa.

[0049] Step S254: Conduct a spatio-temporal evolution simulation of the stress concentration data based on the influence of in-situ stress changes, fracture propagation, and proppant migration on the stress field to generate stress field dynamic evolution simulation data; In the embodiments of the present invention, spatio-temporal evolution simulation is carried out on local stress concentration data, and the simulation content includes the influence of in-situ stress change, fracture propagation, and proppant migration on the stress field. The simulation method uses a fluid-solid coupling model, combines the real-time monitoring data of fracture propagation and proppant, and simulates the migration path of proppant in the stress field and its reaction force on fracture propagation. The step size of the spatio-temporal evolution simulation is set to 1 second to ensure that the dynamic changes of fracture propagation and proppant migration can be captured in real time, and dynamic evolution simulation data of the stress field is generated.

[0050] Step S255: Gradually iteratively correct the stress field according to the dynamic evolution simulation data of the stress field, so as to obtain stress field response data; In the embodiments of the present invention, the stress field is gradually iteratively corrected according to the dynamic evolution simulation data of the stress field. First, by comparing the simulation data with the actual monitored fracture propagation situation, various parameters in the stress field model are corrected, such as the local formation stiffness and fracture conductivity. Through multiple iterative calculations, the accuracy of each correction is controlled within 0.01 MPa to ensure that the stress field model can more accurately reflect the real stress distribution inside the reservoir, and finally stress field response data is obtained.

[0051] Step S256: Conduct a comparative analysis of the stress field response data and the real-time fracture monitoring data based on the characteristics of formation heterogeneity, so as to obtain stress field distribution data.

[0052] In the embodiments of the present invention, a comparative analysis of the stress field response data and the real-time fracture monitoring data is carried out based on the characteristics of formation heterogeneity. By comparing the fracture propagation trajectory in the real-time fracture monitoring data with the fracture position change in the stress field response data, the influence of different heterogeneous units on fracture propagation is analyzed. For the phenomena of fracture propagation offset, blockage, or acceleration, statistical analysis methods are used for attribution, and finally accurate stress field distribution data is obtained.

[0053] By collecting physical parameter data such as the rock elastic modulus, Poisson's ratio, rock compressive strength, and porosity of the reservoir, reservoir physical property parameter data is generated, which can comprehensively reflect the heterogeneity characteristics of the reservoir. The effect of this step is to accurately obtain the physical property parameters of the reservoir, providing detailed formation basic data for subsequent stress field analysis, ensuring that the stress field model can fully consider the actual heterogeneity of the reservoir, and improving the accuracy of the analysis. Based on the reservoir physical property parameters and fracture network distribution data, the formation heterogeneity is classified and processed, and a multi-level stress field model is constructed, which can refine the stress distribution of the formation. By considering the heterogeneity characteristics of the formation, this step effectively solves the problem that the traditional stress field model cannot accurately describe the stress distribution under complex geological conditions. The generated stress field heterogeneity basic data can more realistically reflect the actual working conditions, helping to improve the rationality and effectiveness of the fracturing design. Based on the stress field heterogeneity basic data and the initial stress field data, stress superposition analysis is carried out to identify local stress concentration areas. This step can effectively predict the high stress areas that may appear during the fracture propagation process, providing a more accurate reference basis for optimizing the fracture conductivity, reducing the fracture closure problem caused by stress concentration, and ensuring the stability of the fracturing effect. By analyzing the influence of the stress field change, fracture propagation, and proppant migration in the stress concentration area through spatio-temporal evolution simulation, the change of the stress field can be dynamically monitored. This simulation can effectively predict the evolution trend of the fracture network at different times and spaces, helping to optimize the migration path of the proppant and the fracture propagation behavior, thus maximizing the reservoir stimulation effect. According to the stress field dynamic evolution simulation data, step-by-step iterative correction can more accurately capture the dynamic change trend of the stress field, and finally obtain more realistic stress field response data. The process of step-by-step iterative correction can reduce the error in model calculation, improve the accuracy of reservoir stress field prediction, and ensure the effectiveness and feasibility of the fracturing design. By comparing and analyzing the stress field response data with the real-time fracture monitoring data based on the formation heterogeneity characteristics, the stress field distribution result can be effectively corrected. Through the feedback of real-time data, the stress field distribution is dynamically adjusted to ensure that the stress distribution during the fracturing process is consistent with the actual situation, maximizing the fracture conductivity and reservoir stimulation effect, and improving the accuracy of productivity evaluation.

[0054] Preferably, step S3 includes the following steps: Step S31: Analyze the spatial distribution characteristics of the proppant distribution data based on the proppant placement concentration, and conduct an analysis of the proppant residence degree, so as to obtain proppant residence data; In the embodiment of the present invention, when analyzing the proppant distribution data, first, a distribution model based on the fracture width and height is used, combined with the proppant concentration data in the injection stage, to establish the spatial distribution characteristics of proppant placement. By comparing the proppant distributions at different time nodes, the spatial interpolation method is used to evaluate the residence degree of the proppant, and the residence degree is calculated through the decay curve of the proppant concentration over time. This process generates proppant residence data based on the relationship between the proppant density and the fracture volume, analyzes the proppant retention effect in the fracture and its distribution law in different intervals.

[0055] Step S32: Calculate the proppant embedment depth based on the fracture surface morphology according to the proppant residence data, and conduct a spatio-temporal evolution analysis based on the fracture conductivity, and extract the distribution characteristics of the conductivity ability, so as to generate the fracture conductivity data; In the embodiment of the present invention, according to the proppant residence data, the roughness of the fracture surface is characterized by using the fracture morphology scan data, and a fracture embedment depth model is established. The proppant embedment depth is calculated through the fracture surface friction coefficient, the rock compressive strength and the proppant particle diameter. Subsequently, based on the proppant embedment depth and the fracture conductivity, a spatio-temporal evolution analysis of the conductivity ability is conducted. This analysis simulates the change of the conductivity ability under different pressure conditions through numerical simulation methods, and finally generates the fracture conductivity data, indicating the fluid conductivity performance of the fracture at different time periods.

[0056] Step S33: Establish a volume analysis model based on the multi-scale fracture network according to the fracture network distribution data, and calculate the size of the stimulation volume, so as to obtain the volume calculation data; In the embodiment of the present invention, according to the fracture network distribution data, a volume analysis model based on the multi-scale fracture is established by using the finite element mesh generation technology. This model combines the spatial extensibility of the fracture, and estimates the fracture volume through the discrete fracture network method (DFN). According to the porosity, width and length of the fractures at different scales, the volume of a single fracture is calculated. By using the method of spatial superposition, the volumes of all fractures are comprehensively calculated to obtain the data of the overall stimulation volume, ensuring the evaluation of the effective stimulation volume of each area.

[0057] Step S34: Evaluate the effective stimulation volume based on the fracture connectivity for the volume calculation data, so as to generate the reservoir stimulation volume data; In the embodiment of the present invention, the volume calculation data in step S33 is used to evaluate the reservoir stimulation volume based on the fracture connectivity. Through the connectivity analysis, the effective fracture volume is calculated, that is, the effective volume actually used to stimulate the reservoir during the fracturing process. When analyzing, the connectivity and fluid transmission efficiency between different intervals of the reservoir are considered, combined with the topological structure of the fracture network, and the high connectivity areas are screened out to generate the final reservoir stimulation volume data, reflecting the actual effect of reservoir stimulation.

[0058] Step S35: Conduct sensitivity analysis of fracturing parameters based on reservoir stimulation volume data and fracture conductivity data, and calculate the response characteristics of fracturing parameters, so as to generate optimized fracture parameter data.

[0059] In the embodiment of the present invention, based on the reservoir stimulation volume data and fracture conductivity data, the sensitivity analysis of fracturing parameters is carried out by using the response surface method. The analysis content includes the influence of parameters such as fracture width, proppant concentration, injection pressure, etc. on the reservoir stimulation effect. By calculating the response characteristics of the results within the range of each parameter change, the influence weights of the parameters on fracture conductivity and stimulation volume are determined by using multiple regression analysis, so as to obtain optimized fracture parameter data to guide the optimization and adjustment of subsequent fracturing process parameters.

[0060] By analyzing the spatial distribution characteristics and residence degree of proppant distribution data, the present invention can accurately evaluate the distribution status of proppant in fractures. The generation of proppant residence data helps to understand the retention and embedding of proppant in fractures, so as to ensure that the fractures remain open and improve the fracture conductivity. The effect of this step is to optimize the utilization efficiency of proppant, avoid excessive or insufficient use of proppant, and ensure the effective use of resources. Calculating the embedding depth of proppant based on proppant residence data and conducting spatio-temporal evolution analysis can deeply understand the embedding and expansion of proppant on the fracture surface. The extraction of spatio-temporal characteristics of this fracture conductivity helps to improve the evaluation accuracy of fracture conductivity performance, ensure that the fractures have good conductivity in the long term, and thus improve the reservoir stimulation effect. Establishing a volume analysis model of multi-scale fracture network according to fracture network distribution data can comprehensively and accurately calculate the size of reservoir stimulation volume. The volume calculation data provides a scientific basis for the evaluation of reservoir stimulation volume, helps to judge the actual stimulation effect of fracturing operations, and ensures the maximized utilization of the effective stimulation area of the reservoir. By evaluating the connectivity of fractures and the effectiveness of stimulation volume, the actual stimulation effect of the reservoir can be identified. The reservoir stimulation volume data can reflect the areas with stronger fracture connectivity and better stimulation effect, avoid over-stimulation of ineffective areas, and thus improve the overall efficiency and effect of fracturing operations. Conducting sensitivity analysis of fracturing parameters based on reservoir stimulation volume data and fracture conductivity data can find the optimal combination of fracturing parameters. The effect of this step is to accurately adjust fracturing operation parameters, optimize fracture conductivity performance, maximize the productivity contribution of the reservoir, and thus improve the economic and technical effects of fracturing operations.

[0061] Preferably, step S4 includes the following steps: Step S41: Conduct multi-segment identification analysis of real-time fracture monitoring data based on the spatial distribution of fracturing segments, so as to obtain segment identification data; In the embodiment of the present invention, when performing multi - layer section identification and analysis, first, the fracturing response signals of each section are analyzed through real - time fracture monitoring data, and the positions of different fracturing sections are identified by using spatial distribution characteristics. By matching the dynamic parameters of the fracturing sections with their spatial distribution characteristics, the fracturing section distribution of each layer section is identified, thereby obtaining layer section identification data. This process usually uses fracturing data obtained by surface and downhole sensors, and combines spatial clustering algorithms to perform segmented analysis on the data to ensure the accuracy of layer section identification.

[0062] Step S42: Divide the transformation area based on the fracturing process parameters according to the layer section identification data, and define the spatial boundary of the transformation area, thereby obtaining stratified transformation area data; In the embodiment of the present invention, according to the layer section identification data, combined with fracturing process parameters (such as injection volume, pressure, and proppant concentration), the transformation area of each identified fracturing layer section is divided. The spatial boundary is delimited based on the geological horizons, fracture network distribution, and changes in the fracturing stress field, and spatial interpolation techniques are applied to accurately delimit the boundary of each transformation area. The finally generated stratified transformation area data can clearly define the specific scope of the transformation for subsequent evaluation and optimization operations.

[0063] Step S43: Perform spatial mapping and superposition of the stress field distribution data and the stratified transformation area data, and perform fracture interference mapping, thereby obtaining fracture interference mapping data; In the embodiment of the present invention, the stress field distribution data and the stratified transformation area data are subjected to spatial mapping and superposition, and fracture interference mapping is performed through the interaction between the topological structure of the fracture network and the changes in the stress field. By using the mutual influence characteristics of fracture interference, through finite element simulation technology, the stress superposition effect between each fracture is analyzed to generate fracture interference mapping data. This process helps to identify the interaction relationship between fractures, thereby better optimizing the fracturing design.

[0064] Step S44: Evaluate the effectiveness of fracture parameters based on fracture conductivity for the fracture interference mapping data, and quantify the degree of fracture interference, thereby generating fracture parameter effectiveness data; In the embodiment of the present invention, according to the fracture interference mapping data, the conductivity of each fracture segment and its response to fracture interference are evaluated. By quantitatively evaluating the degree of fracture interference and combining conductivity analysis, the effectiveness of fracture parameters is evaluated. The specific operation uses a conductivity model and numerical calculations of fracture interference effects to evaluate the effectiveness of fractures and generate fracture parameter effectiveness data. These data provide a basis for further optimizing fracturing parameters.

[0065] Step S45: Calculate the productivity contribution based on flow potential for the stratified transformation area data according to the fracture conductivity data, thereby obtaining productivity distribution data; In an embodiment of the present invention, based on the fracture conductivity data, the reservoir stimulation conditions of each region in the zonal stimulation area data are utilized to analyze the flow potential of each region, and its contribution to the overall production capacity is calculated. Through fluid flow simulation and potential analysis tools, the contribution of each stimulated interval to production is estimated, thereby generating production capacity distribution data. This process is used to quantify the production capacity contributions of different stimulated regions to assist in refined production capacity assessment.

[0066] Step S46: Evaluate the stimulation effect on the production capacity distribution data and the fracture parameter optimization data, and conduct an analysis of the production capacity contribution rate to obtain production capacity evaluation data.

[0067] In an embodiment of the present invention, based on the production capacity distribution data and the fracture parameter optimization data, the overall stimulation effect of fracturing is evaluated. Through the analysis of the production capacity contribution rate, the actual production capacity contribution of each stimulated interval is calculated, and the statistical analysis method is used to analyze the correlation between the production capacity contribution and the fracture parameters to determine the optimal fracture parameter combination. The finally generated production capacity evaluation data can clearly show the actual effect of the fracturing stimulation and provide guidance for subsequent fracturing optimization.

[0068] Through the multi - segment identification and analysis of real - time fracture monitoring data, the present invention can accurately identify the spatial distribution characteristics of different fracturing segments. The acquisition of such segment - identification data helps with the subsequent division of the transformation area, provides a basis for targeted fracturing optimization, and ensures the effective evaluation of fracture network formation and flow performance under different geological conditions. Dividing the transformation area and defining the spatial boundary according to the segment - identification data can scientifically and reasonably divide the transformation areas of different fracturing segments. This step ensures the pertinence and effectiveness of the transformation operation, reduces resource waste, improves the efficiency of the fracturing operation, and provides a clear spatial scope for subsequent analysis. Spatially mapping and overlaying the stress - field distribution data with the stratified transformation - area data and performing fracture interference mapping can comprehensively analyze the mutual influence between different fracturing segments. The result of this step helps to identify the interference situation between fractures and provides a basis for the reasonable configuration of fractures, thereby improving the effectiveness of the fracturing operation. By evaluating the effectiveness of fracture conductivity and quantifying the degree of interference on the fracture interference mapping data, the present invention can deeply understand the conductivity performance of each fracture and its performance under different conditions. This step can provide specific data support to help adjust and optimize fracture parameters and improve the overall fracturing effect. Calculating the productivity contribution of the stratified transformation area based on the fracture conductivity data can accurately evaluate the production potential of each transformation area. The result of this step not only helps to evaluate the economic benefits of the fracturing operation but also provides data support for subsequent mining decisions to ensure the priority development of high - productivity areas. Evaluating the overall effect of the fracturing operation and analyzing the productivity contribution rate on the productivity distribution data and fracture - parameter optimization data can comprehensively evaluate the overall effect of the fracturing operation. This step integrates the influence of different parameters, provides a clear productivity evaluation data, helps to summarize experience, optimize future operation plans, and enhance the continuous improvement ability of the fracturing operation.

[0069] Preferably, step S41 includes the following steps: Step S411: Classify the real - time fracture monitoring data based on the spatial position of the fracturing segment to obtain the fracturing - segment position data; In the embodiment of the present invention, the real - time fracture monitoring data is classified. First, the data is preliminarily screened according to the spatial position of the fracturing segment (such as well - hole depth, coordinate system, etc.). Using the spatial coordinate information and with the help of Geographic Information System (GIS) technology, the monitoring data is classified according to the corresponding fracturing segments. Specifically, the threshold - segmentation method is adopted. According to the depth range and spatial coordinates of the fracturing segment, the fracture monitoring data is assigned to different fracturing segments, thereby obtaining clear fracturing - segment position data for subsequent analysis and processing.

[0070] Step S412: Perform segment division based on formation physical property parameters according to the fracturing - segment position data to obtain formation - segment data; In the embodiments of the present invention, based on the fracture section position data, formation physical property parameters (such as porosity, permeability, elastic modulus, etc.) are segmented. First, the corresponding physical property parameters are extracted from the geological model, and then these physical property parameters are matched with the fracture section position data obtained in the previous step. By formulating appropriate classification criteria (such as thresholds of physical property parameters), the K-means clustering algorithm is used to segment the sections to ensure that the physical property characteristics of each section have a certain similarity. The finally generated formation segmentation data provides a basis for subsequent activity analysis.

[0071] Step S413: Perform layer segment activity analysis on the formation segmentation data based on the microseismic signal intensity, and extract layer segment response characteristics, so as to obtain layer segment activity data; In the embodiments of the present invention, for the formation segmentation data, the activity of each layer segment is analyzed in combination with the microseismic signal intensity. The specific method is to extract the microseismic signals corresponding to each layer segment and calculate the characteristic values such as the intensity and frequency of the signals. By using statistical analysis methods (such as variance analysis), the microseismic signal intensity of each layer segment is evaluated to identify active layer segments and inactive layer segments. Further extract layer segment response characteristics, such as signal duration and waveform changes, etc., to generate layer segment activity data, thereby revealing the activity status and response characteristics of each layer segment.

[0072] Step S414: Perform layer segment feature quantification analysis based on the layer segment activity data, so as to obtain layer segment feature data; In the embodiments of the present invention, according to the layer segment activity data, the characteristics of each layer segment are quantitatively analyzed. The quantitative analysis includes calculating the activity value, response intensity and relative activity of each layer segment. The standardization method is used to normalize these characteristics for more detailed comparison. Then, dimensionality reduction techniques such as principal component analysis (PCA) are applied to condense the layer segment characteristics into several groups of important characteristic indicators, and finally form layer segment feature data with high representativeness to provide support for subsequent spatial distribution calculation.

[0073] Step S415: Perform multi-layer segment spatial distribution calculation on the layer segment feature data based on the spatial clustering algorithm, and perform matching processing based on the layer segment characteristics with the real-time fracture monitoring data, so as to obtain layer segment identification data.

[0074] In the embodiments of the present invention, multi-layer segment spatial distribution calculation is performed on the layer segment feature data, and the spatial clustering algorithm (such as DBSCAN or hierarchical clustering) is used to analyze the layer segment characteristics. First, the layer segment feature data is combined with the real-time fracture monitoring data to form a comprehensive data set. Then, through clustering analysis, the layer segments with similar characteristics are identified and their spatial distribution is determined. Finally, based on the matching processing between the layer segment characteristics and the fracture segments, layer segment identification data is generated to help effectively identify the characteristics of different fracture segments and their mutual relationships, providing a basis for subsequent fracturing optimization.

[0075] Through the data classification processing of the spatial positions of the fracturing stages on the real-time fracture monitoring data, the present invention can accurately identify and locate the positions of each fracturing stage in the formation. This step ensures that the data used in subsequent analysis is based on the real spatial positions, improves the accuracy and reliability of the data, and lays a foundation for subsequent interval division and evaluation. According to the fracturing stage position data, the interval division based on the formation physical property parameters can scientifically segment the formation, making the physical properties of each interval clear. This process helps to understand the physical property differences between different intervals, provides an important basis for subsequent fracturing design, and can optimize the fracture propagation effect. The interval activity analysis based on the microseismic signal intensity for the formation segment data helps to evaluate the response of each interval during the fracturing process. This analysis can not only identify the active intervals, but also provide data support for the interval response characteristics, thus providing important information for subsequent fracture network optimization. According to the interval activity data, the interval characteristic quantification analysis can represent the active state of the interval in a quantitative manner, facilitating the comparison and analysis of the performance of different intervals. This step enables the intuitive identification and selection of intervals with higher activity in practical applications, thereby optimizing the fracturing plan and improving the overall production efficiency. By performing the multi-interval spatial distribution calculation based on the spatial clustering algorithm on the interval characteristic data and combining with the real-time fracture monitoring data for feature matching, the relationships and spatial distribution characteristics between each interval can be accurately identified. This process improves the accuracy of interval identification, can effectively support decision-making analysis, makes the subsequent fracturing design more in line with the actual geological conditions, and thus achieves higher operation efficiency and economic benefits.

[0076] Preferably, step S43 includes the following steps: Step S431: Perform spatial discretization processing on the stress field distribution data based on the stress field intensity to obtain stress field grid data; In the embodiment of the present invention, when performing spatial discretization processing on the stress field distribution data, it is first necessary to numerically process the stress field intensity. The specific method is to use the finite element analysis method to convert the stress field data into a grid form. Select an appropriate grid size (for example, each grid is 10 meters × 10 meters), and establish a grid structure in the entire monitoring area. Then, interpolate the stress values of each grid node as the weighted average of its surrounding nodes to ensure that each grid unit can reflect the local stress field intensity, and finally generate stress field grid data, which provides a discretization basis for subsequent analysis.

[0077] Step S432: Perform numerical mapping conversion based on the spatial coordinates according to the layered transformation area data to obtain transformation area grid data; perform overlay analysis based on the spatial coordinates on the stress field grid data and the transformation area grid data to obtain spatial overlay data; In the embodiment of the present invention, according to the data of the hierarchical transformation area, numerical mapping conversion of spatial coordinates is performed, and interpolation algorithms (such as spline interpolation or Kriging interpolation) are used to convert the spatial characteristics of the transformation area into grid data. By analyzing attributes such as the geometric shape, area, and volume of the transformation area, an appropriate mapping accuracy is set to construct the grid data of the transformation area. Then, the stress field grid data is superimposed and analyzed with the transformation area grid data, and the spatial superposition method is used to map the stress field data onto the transformation area grid to generate spatial superposition data for further stress interference analysis.

[0078] Step S433: Identify the interaction of cracks based on stress interference according to the spatial superposition data, so as to obtain crack interference data; In the embodiment of the present invention, the interaction between cracks is identified according to the spatial superposition data. First, a threshold of stress interference is set. According to the change of stress values in the spatial superposition data, a clustering analysis method (such as K-means clustering) is applied to classify the crack regions with similar interference characteristics. Then, the relative positions and stress values of these cracks are analyzed to identify the interaction relationship between the cracks affected by stress interference, and finally crack interference data is obtained, providing a basis for subsequent interference analysis.

[0079] Step S434: Calculate the interference range based on stress field reconstruction for the crack interference data, and perform quantitative analysis of the interference intensity, so as to obtain interference range data; In the embodiment of the present invention, the interference range of the stress interference data is calculated. First, a calculation method for the interference range needs to be defined. By establishing a stress transfer model, the interference range received by each crack is calculated, and the finite element method is used to simulate the propagation of stress between cracks. According to the interference intensity, an appropriate radius (such as 5 meters) is set as the interference influence range, the interference intensity of each crack is analyzed, and the stress interference relationship between the cracks is quantified. Finally, interference range data is obtained to provide support for the subsequent propagation model.

[0080] Step S435: Establish an interference propagation model based on the crack network topology structure according to the interference range data, and perform dynamic response analysis of the stress field, so as to obtain interference propagation data; In the embodiment of the present invention, an interference propagation model of the crack network topology structure is established according to the interference range data. Using graph theory methods, a topology graph containing all cracks is constructed, where the nodes represent cracks and the edges represent the stress influence of the interaction. Applying dynamic simulation technology, the dynamic response of the stress field is analyzed, and time-domain or frequency-domain analysis methods are used to evaluate the timeliness of interference propagation. Finally, interference propagation data is obtained to help understand the stress distribution and transfer law in the crack network.

[0081] Step S436: Conduct spatial mapping analysis on the interference propagation data based on fracture connectivity, and establish a fracture interference mapping relationship, thereby obtaining mapping relationship data; In the embodiment of the present invention, spatial mapping analysis is conducted on the interference propagation data. First, the fracture connectivity characteristics need to be defined. According to the geometric shape and distribution characteristics of the fractures, spatial analysis methods (such as Voronoi diagrams) are applied to establish the interference mapping relationship. The interference propagation data is paired with the fracture network to generate the interference mapping relationship, and the distribution characteristics of the interference in space are intuitively displayed through heat maps or three-dimensional visualization tools. Finally, the mapping relationship data is obtained, providing a basis for subsequent spatio-temporal feature verification.

[0082] Step S437: Conduct spatio-temporal evolution feature verification analysis on the mapping relationship data and the real-time fracture monitoring data, and conduct mapping accuracy evaluation, thereby generating fracture interference mapping data.

[0083] In the embodiment of the present invention, spatio-temporal evolution feature verification is conducted on the mapping relationship data and the real-time fracture monitoring data. First, time series analysis needs to be conducted on the real-time fracture monitoring data to identify the fracture activity conditions within a specific time period. The dynamic time warping (DTW) algorithm is applied to compare the mapping relationship data with the real-time data to evaluate the mapping accuracy. The accuracy and effectiveness of the mapping relationship are quantified through regression analysis methods. Finally, the fracture interference mapping data is generated to help improve the ability to monitor and predict fracture dynamics.

[0084] The present invention performs spatial discretization processing of stress field distribution data based on stress field intensity, which can convert complex stress field information into grid data that is easy to analyze. This processing method not only improves the operability of the data, but also provides fine spatial distribution information for subsequent analysis and modeling, enabling the changes in the stress field to be accurately quantified. Performing numerical mapping conversion according to the data of the layered transformation area and conducting superposition analysis with the stress field grid data helps to establish the specific position and influence range of the transformation area in the stress field. This step provides a basis for subsequent interference identification through the mapping of spatial coordinates, enabling intuitive comparison of the stress states and transformation effects in different regions. Identifying the crack interaction of stress interference through spatial superposition data can identify the mutual influence between different cracks during the fracturing process. This analysis can reveal the complexity and non-linear characteristics of crack propagation, providing an important basis for optimizing the crack network design and transformation strategy. Calculating the interference range and quantifying the interference intensity of the crack interference data helps to determine the influence range and degree of stress interference on crack behavior. The results of this analysis are crucial for evaluating the stability and flow capacity of the cracks, helping to formulate a more precise fracturing operation plan to ensure safety and efficiency. Establishing an interference propagation model based on the interference range data and conducting dynamic response analysis of the stress field can help understand how stress interference propagates over time and space during the fracturing process. This model can effectively predict the influence of interference on crack propagation, thus providing support for real-time adjustment of fracturing parameters and strategies. Through spatial mapping analysis of the interference propagation data, establishing the mapping relationship of crack interference can clarify the mutual influence and connection between different cracks. This mapping relationship is crucial for understanding the evolution of the crack network and the fluid flow path, providing reliable data support for subsequent decision-making analysis and optimization design. Conducting spatio-temporal evolution characteristic verification analysis on the mapping relationship data and the real-time crack monitoring data helps to verify the accuracy and reliability of the mapping model. This process not only improves the accuracy of interference mapping, but also can monitor the behavior changes of cracks in real time, providing a solid foundation for timely adjustment of the fracturing plan and optimization of operation efficiency.

[0085] Preferably, step S45 includes the following steps: Step S451: Conduct spatial distribution analysis of the fracture conductivity data based on flow characteristics and perform flow potential evaluation to obtain flow potential data; In the embodiments of the present invention, for the spatial distribution analysis of fracture conductivity data, it is first necessary to use spatial interpolation methods (such as Kriging interpolation or inverse distance weighted interpolation) to convert the conductivity data into a continuous spatial distribution map. By visualizing the data in three-dimensional space, regions with strong and weak conductivity can be identified. Then, a flow potential assessment is carried out. An assessment criterion (such as the ratio of flow capacity to fracture density) is set, and the flow potential of each region is calculated. By classifying and labeling the flow potential data, the flow potential data is finally obtained, providing a basis for subsequent seepage capacity analysis.

[0086] Step S452: Perform seepage capacity analysis based on permeability according to the data of the layered transformation area to obtain seepage characteristic data; In the embodiments of the present invention, for the seepage capacity analysis of permeability according to the data of the layered transformation area, it is first necessary to collect and process the permeability data in this area, including the experimental results of rock samples. Applying Darcy's law, the seepage capacity of different layers is calculated using permeability and pressure difference. During this process, specific fluid properties (such as fluid viscosity and density) are set, and combined with the actual geological conditions of the area, the seepage characteristic data of each layer is obtained, and finally the seepage capacity distribution of each layer is generated.

[0087] Step S453: Perform a numerical simulation based on fluid-solid coupling according to the flow potential data and the seepage characteristic data, and conduct production capacity prediction, so as to obtain production capacity prediction data; In the embodiments of the present invention, using the flow potential data and the seepage characteristic data, a numerical simulation of fluid-solid coupling is carried out. First, a fluid mechanics model is constructed, combined with the finite element method (FEM) or computational fluid dynamics (CFD) method, and boundary conditions and initial conditions are set. By setting different flow scenarios (such as different injection pressures and flow rates), dynamic simulation calculations are carried out. Finally, based on the simulation results, production capacity prediction is carried out, and statistical methods are used to analyze the fluid flow path and flow rate changes to obtain production capacity prediction data, providing a basis for subsequent production capacity decomposition.

[0088] Step S454: Perform production capacity decomposition based on the contributions of multiple layers according to the production capacity prediction data, and identify flow channels based on the connectivity of the fracture network, so as to obtain flow region data; In the embodiments of the present invention, for production capacity decomposition according to the production capacity prediction data, it is first necessary to set an analysis method for the contributions of multiple layers. According to the production data and prediction results of different layers, the weighted average method is applied to decompose the contributions of each layer and identify the flow channels of each layer. Using fracture network connectivity analysis, graph theory methods are applied to evaluate the flow channels between each layer to understand the main flow paths and local obstacles, and finally the flow region data is obtained.

[0089] Step S455: Perform dynamic response analysis based on pressure conduction according to the flow area data, so as to obtain pressure response data; In the embodiment of the present invention, dynamic response analysis of pressure conduction is performed according to the flow area data. A numerical simulation method is adopted to establish a pressure propagation model, set fluid flow conditions and formation characteristics, and simulate the pressure transfer process between layers. The dynamic time domain analysis method is used to monitor the pressure changes at different time points and evaluate their response characteristics. Finally, pressure response data is obtained, providing a basis for subsequent productivity contribution analysis.

[0090] Step S456: Extract the spatio-temporal distribution characteristics based on productivity contribution from the pressure response data, and perform matching verification based on dynamic response with the real-time fracture monitoring data, so as to generate productivity distribution data.

[0091] In the embodiment of the present invention, spatio-temporal distribution characteristics are extracted from the pressure response data. The time series analysis method is used to identify the main change trends and characteristics of the pressure response. Combining with the real-time fracture monitoring data, through dynamic response matching verification, a verification standard is set (such as the correlation analysis of pressure change and productivity change), comparing the pressure response with the flow area data, confirming the impact of pressure change on productivity, and finally generating productivity distribution data to support subsequent production decisions.

[0092] Through the spatial distribution analysis of flow characteristics on fracture conductivity data, the present invention can identify the flow potential of different regions. This analysis provides a basis for evaluating the overall flow performance of the reservoir, helps to formulate reasonable production strategies and optimize the fracturing design, and improves the utilization efficiency of resources. According to the data of the layered transformation area, the seepage capacity analysis of permeability can effectively identify the flow characteristics of different intervals. This step provides support for understanding the internal flow mechanism of the reservoir and lays a foundation for subsequent fluid flow and fracturing effect analysis, which helps to optimize the interval selection. Combining the flow potential data and the seepage characteristic data for the numerical simulation of fluid-solid coupling can simulate the interaction between the fluid and the rock and accurately predict the production capacity. This simulation provides strong data support for production capacity analysis and ensures that a high production rate and efficiency can be obtained in actual production. Based on the production capacity prediction data, the production capacity decomposition can clarify the contributions of different intervals and fracture networks to the overall production capacity. This analysis helps to identify the key flow channels and optimize the resource production strategy to ensure efficient production even under complex geological conditions. Through the dynamic response analysis based on pressure conduction on the flow area data, the propagation of pressure inside the reservoir can be evaluated. This analysis provides a basis for understanding the pressure changes during the fluid flow process and can better guide real-time monitoring and decision-making. Extracting the spatio-temporal distribution characteristics of the pressure response data and matching and verifying them with the real-time fracture monitoring data can confirm the accuracy of the production capacity distribution. This process provides a direct basis for evaluating the fracturing effect and reservoir performance, ensures quick adjustments in the dynamically changing production environment, and improves the overall production benefit.

[0093] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0094] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. An evaluation method for the effectiveness of artificial fracturing fracture parameters in horizontal wells, characterized in that Including the following steps: Step S1: Collect information on the horizontal well artificial fracturing construction process based on surface process parameters and downhole dynamic parameters to obtain fracturing condition data; Monitor the fracture propagation of the horizontal well in real time according to the fracturing condition data, and perform digital conversion processing to generate real-time fracture monitoring data; Step S2: Analyze the microseismic signals of the real-time fracture monitoring data, calculate the spatial distribution of the fracture network, and generate fracture network distribution data; Analyze the dynamic evolution of the reservoir stress field according to the fracture network distribution data to generate stress field distribution data; Track the migration trajectory of the proppant according to the stress field distribution data to generate proppant distribution data; Step S3: Evaluate the conductivity of the proppant distribution data to generate fracture conductivity data; Analyze the modified volume of the fracture network distribution data to generate reservoir modified volume data; Conduct a parameter sensitivity test on the fracture conductivity data according to the reservoir modified volume data to generate fracture parameter optimization data; Step S4: Identify the multi-stage fracturing area according to the real-time fracture monitoring data to obtain layered modification area data; Map the fracture interference between the stress field distribution data and the layered modification area data, and conduct an effectiveness evaluation to generate fracture parameter effectiveness data; Analyze the productivity contribution of the layered modification area data according to the fracture conductivity data and the fracture parameter optimization data to obtain productivity evaluation data; Step S5: Use the productivity evaluation data and the fracture parameter effectiveness data to conduct a parameter evaluation to generate comprehensive fracture parameter evaluation data.

2. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect parameters based on fracturing fluid injection, proppant addition, and fracturing fluid performance during the horizontal well artificial fracturing construction process to obtain comprehensive surface process data; Step S12: Collect downhole dynamic parameters of the horizontal well to obtain comprehensive downhole dynamic data. Specifically, the downhole dynamic parameter collection is to collect the wellbore temperature distribution data through the downhole fiber optic temperature measurement system, collect the segmented pressure data through the downhole pressure sensor array, collect the acoustic signal data through the acoustic detection system, and combine the temperature distribution data, segmented pressure data, and acoustic signal data into comprehensive downhole dynamic data; Step S13: Perform time synchronization calibration on the comprehensive surface process data and the comprehensive downhole dynamic data to obtain fracturing condition data; Step S14: Identify the fracture initiation pressure according to the fracturing condition data and track the fracture propagation in real time to obtain fracture propagation tracking data; Step S15: Perform digital coding on the fracture propagation tracking data, conduct spatial discretization processing, and establish a grid data structure to obtain grid monitoring data; Step S16: Standardize the grid monitoring data to generate real-time fracture monitoring data.

3. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 2, wherein Step S2 includes the following steps: Step S21: Extract the microseismic signal waveform of the real-time fracture monitoring data, and eliminate the noise interference through spectrum analysis to obtain microseismic signal processing data; Step S22: Establish a source distribution model based on three-dimensional space coordinates according to the processed microseismic signal data, and perform source location calculation to obtain source location data; Step S23: Identify the crack propagation direction based on the real-time crack monitoring data and the source location data, and perform topological structure analysis on the crack network connectivity to obtain crack network distribution data; Step S24: Reconstruct the in-situ stress field for the crack network distribution data, and establish a stress superposition model based on crack interference to obtain the initial stress field data; Step S25: Conduct spatio-temporal evolution analysis of the reservoir stress field based on the initial stress field data considering formation heterogeneity, and perform dynamic response simulation of the stress field to obtain stress field distribution data; Step S26: Construct a proppant transport mechanics model based on the stress field distribution data, and simulate the proppant migration path based on fluid-solid coupling calculation according to the proppant transport mechanics model, and perform real-time position tracking to generate proppant distribution data.

4. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 3, characterized in that, Step S22 includes the following steps: Step S221: Extract the P-wave and S-wave arrival times of the processed microseismic signal data based on the waveform recognition algorithm to obtain arrival time data; Step S222: Establish a velocity model based on the travel-time inversion equations and ray tracing method according to the arrival time data to obtain velocity field distribution data; Step S223: Invert the source parameters based on the velocity field distribution data, and perform least squares optimization calculation on the source location to obtain the initial location result data; Step S224: Conduct location error analysis on the initial location result data to obtain location accuracy evaluation data, where the location error analysis is specifically to establish an error ellipse evaluation model based on the covariance matrix and use the error ellipse evaluation model to evaluate the location accuracy of the initial location result data; Step S225: Correct the error of the initial location result data according to the location accuracy evaluation data, and perform precise location calculation using the double-difference location algorithm to obtain corrected location data; Step S226: Map the corrected location data to three-dimensional space, establish a source distribution model based on the geographic coordinate system, and perform spatial clustering analysis to obtain source location data.

5. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 4, wherein Step S25 includes the following steps: Step S251: Collect the reservoir physical parameters based on the heterogeneity characteristics of the formation, including the rock elastic modulus, Poisson's ratio, rock compressive strength, and porosity distribution, to generate reservoir physical property parameter data; Step S252: Classify the formation heterogeneity according to the reservoir physical property parameter data and the crack network distribution data, and construct a multi-level stress field model to generate basic stress field heterogeneity data; Step S253: Conduct stress field superposition analysis based on the basic stress field heterogeneity data and the initial stress field data, and calculate the local stress concentration area to obtain stress concentration data; Step S254: Perform spatio-temporal evolution simulation of the stress concentration data considering the influence of in-situ stress change, crack propagation, and proppant migration on the stress field to generate stress field dynamic evolution simulation data; Step S255: Gradually iterate and correct the stress field according to the stress field dynamic evolution simulation data to obtain stress field response data; Step S256: Compare and analyze the stress field response data with the real-time fracture monitoring data based on the formation heterogeneity characteristics to obtain stress field distribution data.

6. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 5, characterized in that, Step S3 includes the following steps: Step S31: Analyze the spatial distribution characteristics of the proppant distribution data based on the proppant placement concentration, and analyze the proppant residence degree to obtain proppant residence data; Step S32: Calculate the proppant embedding depth based on the fracture surface morphology according to the proppant residence data, conduct spatio-temporal evolution analysis based on the fracture conductivity, and extract the distribution characteristics of the conductivity ability to generate fracture conductivity data; Step S33: Establish a volume analysis model based on the multi-scale fracture network according to the fracture network distribution data, and calculate the size of the stimulated volume to obtain volume calculation data; Step S34: Evaluate the effective stimulated volume based on the fracture connectivity of the volume calculation data to generate reservoir stimulated volume data; Step S35: Conduct sensitivity analysis of the fracturing parameters based on the reservoir stimulated volume data and the fracture conductivity data, and calculate the response characteristics of the fracturing parameters to generate fracture parameter optimization data.

7. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 6, characterized in that, Step S4 includes the following steps: Step S41: Conduct multi-segment identification analysis of the real-time fracture monitoring data based on the spatial distribution of the fracturing sections to obtain section identification data; Step S42: Divide the stimulated area based on the fracturing process parameters according to the section identification data, and define the spatial boundary of the stimulated area to obtain stratified stimulated area data; Step S43: Perform spatial mapping superposition of the stress field distribution data and the stratified stimulated area data, and conduct fracture interference mapping to obtain fracture interference mapping data; Step S44: Evaluate the effectiveness of the fracture parameters based on the fracture conductivity of the fracture interference mapping data, and quantify the fracture interference degree to generate fracture parameter effectiveness data; Step S45: Calculate the production contribution based on the flow potential of the stratified stimulated area data according to the fracture conductivity data to obtain production distribution data; Step S46: Evaluate the stimulation effect of the production distribution data and the fracture parameter optimization data, and conduct production contribution rate analysis to obtain production evaluation data.

8. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 7, characterized in that Step S41 includes the following steps: Step S411: Classify the real-time fracture monitoring data based on the spatial position of the fracturing sections to obtain fracturing section position data; Step S412: Divide the sections based on the formation physical property parameters according to the fracturing section position data to obtain formation section data; Step S413: Analyze the section activity based on the microseismic signal intensity of the formation section data, and extract the section response characteristics to obtain section activity data; Step S414: Conduct quantitative analysis of the section characteristics according to the section activity data to obtain section characteristic data; Step S415: Calculate the multi-segment spatial distribution of the segment feature data based on a spatial clustering algorithm, and perform matching processing based on segment features with the real-time fracture monitoring data, so as to obtain segment identification data.

9. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 8, characterized in that, Step S43 includes the following steps: Step S431: Perform spatial discretization processing on the stress field distribution data based on the stress field intensity, so as to obtain stress field grid data; Step S432: Perform numerical mapping conversion based on spatial coordinates according to the layered transformation area data, so as to obtain transformation area grid data; perform superposition analysis based on spatial coordinates on the stress field grid data and the transformation area grid data, so as to obtain spatial superposition data; Step S433: Identify the fracture interaction based on stress interference according to the spatial superposition data, so as to obtain fracture interference data; Step S434: Calculate the interference range of the fracture interference data based on stress field reconstruction, and perform interference intensity quantification analysis, so as to obtain interference range data; Step S435: Establish an interference propagation model based on the fracture network topology structure according to the interference range data, and perform stress field dynamic response analysis, so as to obtain interference propagation data; Step S436: Perform spatial mapping analysis based on fracture connectivity on the interference propagation data, and establish a fracture interference mapping relationship, so as to obtain mapping relationship data; Step S437: Perform spatio-temporal evolution feature verification analysis on the mapping relationship data and the real-time fracture monitoring data, and perform mapping accuracy evaluation, so as to generate fracture interference mapping data.

10. The evaluation method for the effectiveness of horizontal well artificial fracturing fracture parameters according to claim 9, characterized in that Step S45 includes the following steps: Step S451: Perform spatial distribution analysis of the fracture conductivity data based on flow characteristics, and perform flow potential evaluation, so as to obtain flow potential data; Step S452: Perform seepage capacity analysis based on permeability according to the layered transformation area data to obtain seepage characteristic data; Step S453: Perform numerical simulation based on fluid-solid coupling according to the flow potential data and the seepage characteristic data, and perform production capacity prediction, so as to obtain production capacity prediction data; Step S454: Decompose the production capacity based on the contribution of multiple segments according to the production capacity prediction data, and identify the flow channels based on the connectivity of the fracture network, so as to obtain flow area data; Step S455: Perform dynamic response analysis based on pressure conduction according to the flow area data, so as to obtain pressure response data; Step S456: Extract the spatio-temporal distribution characteristics based on the production capacity contribution of the pressure response data, and perform matching verification based on the dynamic response with the real-time fracture monitoring data, so as to generate production capacity distribution data.

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