A shale gas horizontal well fracturing oriented seismic real-time monitoring and geologic engineering dynamic feedback optimization method and system

Abstract

This invention discloses a method and system for real-time seismic monitoring and dynamic feedback optimization of geological engineering for shale gas horizontal well fracturing. The method includes: acquiring microseismic signals, reservoir geology, and fracturing construction engineering data through relevant equipment; and then constructing an integrated model including reservoir 3D geology, microseismic fracture inversion, and a fracturing parameter-fracture response correlation sub-model. During fracturing, real-time data is input into the model to invert fracture morphology parameters, analyze the influence of engineering parameters, and determine the compatibility between the fracture and the high-quality reservoir area. If the preset target is not met, the fracturing parameters are iteratively optimized using a parameter optimization module combined with constraints to generate an optimal adjustment scheme, which is then fed back to the construction control system. This invention achieves dynamic optimization of the fracturing process, solving the problems of traditional fracturing relying on static models and insufficient data fusion, thus improving fracturing accuracy and reservoir stimulation effects.

Description

Technical Field

[0001] This invention relates to the field of outpatient information query and management technology, and in particular to a method and system for real-time seismic monitoring and dynamic feedback optimization of geological engineering for shale gas horizontal well fracturing. Background Technology

[0002] With the global trend towards a cleaner energy structure, shale gas, as an important unconventional natural gas resource, plays a crucial role in alleviating energy supply and demand imbalances and ensuring energy security through its efficient development. Horizontal well fracturing technology, as a core method in shale gas development, significantly increases the contact area between the reservoir and the wellbore, improving extraction efficiency through horizontal drilling and reservoir fracturing. However, shale gas reservoirs generally exhibit strong heterogeneity, low permeability, and complex geological structures. The distribution, orientation, and connectivity of natural fractures are difficult to accurately predict, leading to significant uncertainties in the direction and morphology of fracture propagation, posing a significant challenge to the precise control of fracturing effects. Furthermore, reservoir stress state, permeability, and other parameters dynamically change during fracturing operations with the injection of fracturing fluid and fracture propagation, requiring real-time monitoring and parameter adjustment mechanisms to maximize reservoir stimulation effects.

[0003] Existing technologies have significant limitations in the application of fracturing in shale gas horizontal wells. On the one hand, traditional fracturing designs rely heavily on static geological models and empirical parameters, failing to respond in real time to dynamic changes in the reservoir during fracturing. This makes it impossible to adjust construction parameters promptly to adapt to the actual situation of fracture propagation, resulting in insufficient uniformity of reservoir stimulation and low resource utilization. On the other hand, the integration of existing seismic monitoring technology with geological data and engineering parameters is insufficient, lacking a mechanism for collaborative application of cross-domain data. Monitoring results are difficult to directly translate into effective basis for optimizing construction parameters. This makes it impossible to accurately quantify the correlation between construction parameters and fracture propagation, and also makes it difficult to achieve real-time assessment of the fit between fracture propagation and high-quality reservoir areas, thus affecting the accuracy and efficiency of fracturing operations. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for real-time seismic monitoring and dynamic feedback optimization of geological engineering for horizontal well fracturing of shale gas.

[0005] The technical solution adopted in this invention is a method for real-time seismic monitoring and dynamic feedback optimization of geological engineering for shale gas horizontal well fracturing, comprising the following steps: S1, acquiring microseismic signal data captured by seismic monitoring equipment, reservoir geological data collected by geological exploration equipment, and fracturing construction engineering data recorded by engineering monitoring equipment during the shale gas horizontal well fracturing process; S2, constructing an integrated seismic-geological-engineering model, establishing a three-dimensional geological model of the shale gas reservoir based on the reservoir geological data, establishing a microseismic fracture inversion sub-model by combining the microseismic signal data, and integrating the fracturing construction engineering data to construct a fracturing parameter-fracture response correlation sub-model, coupling the three to form an integrated model; S3, during the fracturing construction process, inputting the real-time acquired microseismic signal data and fracturing construction engineering data into the integrated model. The process involves several steps: S1) Inverting the current fracture morphology parameters using a microseismic fracture inversion sub-model; S2) Analyzing the influence of engineering parameters on fracture propagation using a fracturing parameter-fracture response correlation sub-model; and S3) Determining the correlation between fracture propagation and the distribution of high-quality reservoir areas using a 3D reservoir geological model. S4) Based on the analysis results from S3, if fracture propagation does not reach the preset target, the parameter optimization module is activated. Guided by maximizing reservoir stimulation volume and improving shale gas production capacity, and considering reservoir geological parameter constraints, a genetic algorithm iteratively optimizes the engineering parameters of fracturing fluid volume, proppant addition, displacement, and construction pressure. S5) Generating the optimal fracturing parameter adjustment scheme through the parameter optimization module. S6) Feeding the optimal fracturing parameter adjustment scheme back to the fracturing construction control system for real-time adjustment of construction parameters, thus completing the dynamic optimization of the fracturing process.

[0006] Furthermore, the microseismic fracture inversion sub-model is constructed using the following formula: , in, This is a comprehensive quantitative value for crack characteristics. For the first Weighting coefficients for individual microseismic events. For the first The signal amplitude of a microseismic event For the first The duration of the signal for a microseismic event For the first Spatial distance between microseismic events and adjacent events For the first P-wave velocity of a microseismic event, For the first Shear wave velocity of a microseismic event, For the first The angle between the wave propagation direction and the crack propagation direction of a microseismic event. This represents the total number of microseismic events.

[0007] Furthermore, the fracturing parameter-crack response correlation sub-model is constructed using the following formula: , in, The comprehensive response value for crack propagation. These are the model fitting coefficients. This refers to the fracturing displacement. For the amount of sand added, Due to construction pressure, For reservoir permeability, This refers to reservoir porosity.

[0008] Furthermore, the genetic algorithm optimization process employs the following fitness function: , in, This is a vector of fracturing parameters. These are the weighting coefficients. For reservoir stimulation volume, This refers to the number of fracturing parameters. For the first Actual values ​​of each fracturing parameter. For the first Optimal reference values ​​for each fracturing parameter For the first Maximum and minimum allowable values ​​for each fracturing parameter.

[0009] Furthermore, the distribution of geological parameters in the three-dimensional geological model of the reservoir is calculated using the following formula: , in, Spatial coordinates Geological parameter values ​​at the location, For the first The weight of each sampling point For the first Measured geological parameters at each sampling point The total number of sampling points. For the first Spatial coordinates of a reference point For the number of reference points, It is a local minimum constant.

[0010] Furthermore, the degree of fit between the fracture propagation and the distribution of high-quality reservoir zones is calculated using the following formula: , in, To match the quantified value, This represents the actual crack propagation area. This is within the high-quality reservoir area. Spatial coordinates The reservoir quality coefficient at that location It is a spatial volume element.

[0011] Further, S2 includes the following sub-steps: S21, by integrating reservoir structural data, lithological data, rock mechanical parameters, and reservoir fluid parameters through processed reservoir geological data, a three-dimensional geological model of the shale gas reservoir covering the fracturing area is constructed using a spatial interpolation algorithm to determine the spatial distribution characteristics of reservoir lithology, porosity, and permeability geological parameters; S22, based on preprocessed microseismic signal data, the dominant frequency and energy attenuation characteristic parameters of the signal are extracted, and a microseismic fracture inversion sub-model is established in conjunction with a source location algorithm to quantitatively characterize the length, width, height, and propagation direction of the fracturing fractures; S23, preprocessed fracturing construction engineering data are collected, and a fracturing parameter-fracture response correlation sub-model is constructed using fracturing fluid volume, proppant addition volume, and displacement volume as input variables and fracture propagation morphology parameters as output variables to establish a quantitative mapping relationship between the two; S24, the three-dimensional geological model of the reservoir, the microseismic fracture inversion sub-model, and the fracturing parameter-fracture response correlation sub-model are coupled through a data interface to perform real-time data interaction and collaboration calculations between the models, forming an integrated seismic-geological-engineering model.

[0012] Further, S3 includes the following sub-steps: S31, continuously collecting microseismic signal data and fracturing construction engineering data during the fracturing process through seismic monitoring equipment and engineering monitoring equipment, and uploading the data to the data processing center in real time according to a preset transmission protocol; S32, synchronously inputting the real-time collected and processed microseismic signal data and fracturing construction engineering data into the integrated seismic-geological-engineering model to trigger the model calculation process; S33, calling the microseismic fracture inversion sub-model in the integrated model to analyze the input microseismic signal data and invert the current fracturing fracture length, width, height, and propagation direction morphological parameters; S34, using the fracturing parameter-fracture response correlation sub-model, analyzing the correspondence between the current fracturing construction engineering parameters and the fracture propagation morphological parameters, and combining the spatial distribution information of the reservoir's high-quality areas in the three-dimensional geological model of the reservoir to determine whether the current fracture propagation path is consistent with the distribution of the reservoir's high-quality areas.

[0013] Further, S4 includes the following sub-steps: S41, receiving the data analysis results from S3, comparing the current fracture propagation morphology parameters with the preset target parameters, and determining whether the fracture propagation has reached the preset target; S42, if the preset target has not been reached, activating the parameter optimization module, and determining the core optimization direction as maximizing reservoir stimulation volume and improving shale gas production capacity; S43, sorting out the constraints in the reservoir geological parameters, including the limiting requirements of reservoir permeability, porosity, and rock mechanics parameters on fracturing parameters; S44, calling the genetic algorithm, using fracturing fluid volume, proppant addition volume, displacement volume, and construction pressure as optimization variables, and performing multiple rounds of iterative calculations within the constraint range to gradually approach the optimal parameter combination.

[0014] A real-time seismic monitoring and dynamic feedback optimization system for shale gas horizontal well fracturing is disclosed. This system, applied to a method for real-time seismic monitoring and dynamic feedback optimization of shale gas horizontal well fracturing, includes: a multi-source data acquisition unit, an integrated model construction unit, a real-time data processing and analysis unit, a parameter optimization calculation unit, an optimal scheme generation unit, and a construction parameter control unit. The multi-source data acquisition unit comprehensively captures microseismic signals, reservoir geology, and fracturing construction engineering data through seismic monitoring, geological exploration, and engineering monitoring equipment, providing fundamental data support for system operation. The acquired data is transmitted in real-time to the integrated model construction unit. Based on the received data, the integrated model construction unit establishes and couples sub-models for reservoir 3D geology, microseismic fracture inversion, and fracturing parameter-fracture response correlation. An integrated model is formed and then transmitted to the real-time data processing and analysis unit. The real-time data processing and analysis unit receives the real-time acquired data and inputs it into the integrated model to invert fracture morphology parameters, analyze the influence of engineering parameters, and determine the compatibility between fractures and high-quality reservoir areas. The analysis results are simultaneously sent to the parameter optimization calculation unit. If the parameter optimization calculation unit determines that the fracture propagation has not reached the preset target, it combines the reservoir geological constraints and uses a genetic algorithm to iteratively optimize the key fracturing parameters. The optimization results are transmitted to the optimal scheme generation unit. The optimal scheme generation unit generates a detailed optimal fracturing parameter adjustment scheme after screening and verifying the iterative optimization results, and then feeds the scheme back to the construction parameter control unit. After receiving the scheme, the construction parameter control unit adjusts the fracturing construction control system in real time to complete the dynamic optimization of the fracturing process.

[0015] Beneficial Effects: This invention proposes a method and system for real-time seismic monitoring and dynamic feedback optimization of geological engineering for shale gas horizontal well fracturing. By constructing an integrated seismic-geological-engineering model, it integrates multi-source monitoring data with sub-model coupling calculations, effectively overcoming the core shortcomings of traditional fracturing methods that rely on static models and empirical parameters and suffer from insufficient data fusion. First, it comprehensively acquires microseismic signals, reservoir geology, and construction engineering data during the fracturing process using multiple types of equipment. Then, the integrated model enables real-time inversion of fracture morphology, analysis of the impact of engineering parameters, and judgment of reservoir compatibility, breaking down data fragmentation. For cases where fracture propagation does not reach the target, iterative optimization is performed using a parameter optimization module combined with reservoir constraints to generate the optimal solution, which is then fed back to the construction control system for real-time adjustment, forming a complete dynamic closed loop. This method not only enables real-time response to dynamic changes in the reservoir, solving the problem that traditional designs cannot adapt to real-time operating conditions, but also improves the matching accuracy between construction parameters and fracture propagation through multi-source data collaboration and quantitative mapping analysis, significantly improving the uniformity of reservoir stimulation and resource utilization. At the same time, it strengthens the fit between fracture propagation and high-quality reservoir zones, ultimately achieving a dual increase in reservoir stimulation volume and shale gas production capacity, providing reliable technical support for efficient fracturing of shale gas horizontal wells. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of method step S2 of the present invention; Figure 3 This is a flowchart of method step S3 of the present invention; Figure 4 This is a flowchart of method step S4 of the present invention; Figure 5 This is a flowchart of step S5 of the method of the present invention; Figure 6 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1As shown, a method for real-time seismic monitoring and dynamic feedback optimization of geological engineering for shale gas horizontal well fracturing includes the following steps: S1, acquiring microseismic signal data captured by seismic monitoring equipment, reservoir geological data collected by geological exploration equipment, and fracturing construction engineering data recorded by engineering monitoring equipment during the shale gas horizontal well fracturing process; S2, constructing an integrated seismic-geological-engineering model, establishing a three-dimensional geological model of the shale gas reservoir based on reservoir geological data, establishing a microseismic fracture inversion sub-model by combining microseismic signal data, and integrating fracturing construction engineering data to construct a fracturing parameter-fracture response correlation sub-model, coupling the three to form an integrated model; S3, during the fracturing construction process, inputting the real-time acquired microseismic signal data and fracturing construction engineering data into the integrated model, and optimizing the model through microseismic monitoring and dynamic feedback optimization of geological engineering. The seismic fracture inversion sub-model inverts the current fracture morphology parameters. The influence of engineering parameters on fracture propagation is analyzed using the fracturing parameter-fracture response correlation sub-model. The degree of fit between fracture propagation and the distribution of high-quality reservoir areas is determined by combining the three-dimensional geological model of the reservoir. In step S4, based on the analysis results of step S3, if fracture propagation does not reach the preset target, the parameter optimization module is activated. Guided by maximizing reservoir stimulation volume and improving shale gas production capacity, and combined with reservoir geological parameter constraints, the engineering parameters of fracturing fluid volume, proppant addition, displacement, and construction pressure are iteratively optimized using a genetic algorithm. In step S5, the optimal fracturing parameter adjustment scheme is generated through the parameter optimization module. In step S6, the optimal fracturing parameter adjustment scheme is fed back to the fracturing construction control system for real-time adjustment of construction parameters, completing the dynamic optimization of the fracturing process.

[0020] Step S1 is the foundational data acquisition stage of the entire method, comprehensively and accurately capturing three types of key data to support subsequent processes. The seismic monitoring equipment employs a three-component geophone array deployed around the wellhead, working in conjunction with downhole fiber optic sensors. The three-component geophones are selected with a sensitivity of no less than 100V / m. s -¹ High-precision piezoelectric sensors with a dynamic range of no less than 120dB are evenly distributed at a density of one sensor every 50 to 100 meters, with a sampling frequency set between 1000 and 2000Hz to ensure accurate capture of weak microseismic signals in complex surface environments. Downhole fiber optic sensors employ distributed acoustic sensing technology, densely deployed within the casing of horizontal wells, with sampling intervals strictly controlled to no more than 1 meter, achieving high-density, high-resolution signal acquisition. Data collected by geological exploration equipment includes reservoir structure data obtained through seismic exploration, lithological data and natural gamma and resistivity logging curves obtained through well logging, rock mechanical parameters such as elastic modulus, Poisson's ratio, and compressive strength measured through core experiments, as well as reservoir fluid parameters such as formation pressure and fluid viscosity, comprehensively covering reservoir geological characteristics. Engineering monitoring equipment records real-time data on fracturing fluid volume, proppant addition, discharge rate, and construction pressure throughout the entire construction process, ensuring no construction parameters are missed. This step involves collaborative data acquisition using multiple devices to achieve comprehensive coverage of seismic signals, geological conditions, and construction parameters during the fracturing process. The settings of parameters such as the density of geophones, sampling frequency, and sampling interval of fiber optic sensors directly determine the accuracy and completeness of data acquisition, providing detailed and reliable basic data support for subsequent integrated modeling, real-time monitoring and analysis, and dynamic optimization. This is a prerequisite for the smooth implementation of the entire method.

[0021] Step S2 constructs an integrated seismic-geological-engineering model to achieve deep fusion and collaborative computation of multi-source data. First, based on the reservoir geological data acquired in S1, multi-dimensional information such as reservoir structure, lithology, rock mechanics parameters, and reservoir fluid parameters is integrated. A spatial interpolation algorithm is used to construct a three-dimensional geological model of the shale gas reservoir covering the fracturing area, clearly presenting the spatial distribution characteristics of geological parameters such as reservoir lithology, porosity, and permeability, providing accurate geological background support for fracture propagation analysis. Next, combined with preprocessed microseismic signal data, characteristic parameters such as signal dominant frequency, energy attenuation, and propagation velocity are extracted. A microseismic fracture inversion sub-model is established using source location correlation technology, enabling quantitative characterization of fracture length, width, height, and propagation direction, ensuring the accuracy of fracture morphology description. Simultaneously, preprocessed fracturing construction engineering data is incorporated, using fracturing fluid volume, proppant addition, displacement, and construction pressure as input variables, and fracture morphology parameters as output variables. A fracturing parameter-fracture response correlation sub-model is constructed through statistical analysis and correlation modeling techniques, clarifying the quantitative mapping relationship between engineering parameters and fracture propagation. Finally, through a dedicated data interface and collaborative computing mechanism, the reservoir 3D geological model, the microseismic fracture inversion sub-model, and the fracturing parameter-fracture response correlation sub-model are deeply coupled, enabling real-time data interaction and collaborative computation between the models to form a unified integrated model. This model breaks the traditional separation of seismic monitoring, geological analysis, and engineering construction data. The degree of coupling and parameter characterization accuracy of the model directly affect the accuracy of subsequent fracture inversion and the rationality of parameter optimization, providing core technical support for subsequent real-time monitoring and dynamic optimization.

[0022] Step S3 involves real-time monitoring and dynamic analysis of the fracturing process to provide timely and accurate data for optimized decision-making. Throughout the fracturing operation, microseismic signal data and fracturing engineering data, acquired in real-time by seismic and engineering monitoring equipment (S1), and preprocessed, are continuously and synchronously input into the integrated seismic-geological-engineering model constructed in S2 according to a preset transmission protocol, triggering the model's real-time calculation process. Upon receiving the data, the model immediately calls the microseismic fracture inversion sub-model to analyze and process the microseismic signal data. Through a series of continuous operations such as signal feature extraction, source location, and energy analysis, the model inverts the current fracturing fracture's length, width, height, and propagation direction in real time, enabling dynamic tracking and precise control of the fracture propagation state. Simultaneously, the fracturing parameter-fracture response correlation sub-model is invoked to match and analyze the real-time acquired fracturing engineering data with the inverted fracture morphological parameters one by one. This quantifies the influence of current engineering parameters such as displacement, sand addition, and construction pressure on the fracture propagation rate, propagation direction, and morphological characteristics, clarifying the sensitive relationship between each parameter and the fracture response. Furthermore, by combining the spatial distribution information of high-quality reservoir areas in the 3D geological model of the reservoir, the real-time inverted fracture propagation paths are spatially overlaid with the distribution of high-quality reservoir areas to determine whether the current fracture propagation aligns with the distribution of high-quality reservoir areas and whether it can effectively stimulate high-quality reservoir regions. This step, through real-time data input, dynamic model calculation, and multi-dimensional comprehensive analysis, promptly captures the dynamics of fracture propagation and the influence of parameters. The real-time nature of its monitoring, the depth and accuracy of its analysis directly determine the timeliness and effectiveness of subsequent optimization decisions, making it a key link in achieving dynamic optimization.

[0023] Step S4, addressing situations where fracture propagation fails to meet preset targets, initiates the parameter optimization process to generate a scientifically sound fracturing parameter adjustment plan. First, it receives the data analysis results from S3, comprehensively and meticulously comparing the currently retrieved fracture length, width, height, propagation direction, and other morphological parameters with the preset fracture propagation target parameters (pre-determined based on the distribution of high-quality reservoir areas and reservoir stimulation volume requirements). This clarifies the specific differences between the current fracture propagation and the target in various dimensions such as length, width, height, and propagation direction, accurately determining whether the preset reservoir stimulation requirements are met. If the fracture propagation fails to meet the preset targets, the parameter optimization module is immediately activated, focusing on maximizing reservoir stimulation volume and enhancing shale gas production capacity as the core optimization direction. Subsequently, the system systematically reviews various constraints in the reservoir geological parameters, including fracturing pressure not exceeding 80% of the casing's internal pressure resistance, proppant concentration not exceeding the upper limit of the fracturing fluid's proppant-carrying capacity, and displacement not lower than the critical displacement corresponding to the reservoir's conductivity. Simultaneously, it integrates the limitations on fracturing parameters from reservoir permeability, porosity, and rock mechanics parameters, forming a complete and rigorous constraint system. Finally, a genetic algorithm is invoked, using fracturing fluid volume, proppant addition, displacement, and operational pressure as core optimization variables. Within the defined constraints, multiple iterative calculations are performed. By continuously adjusting parameter combinations and evaluating optimization effects in real time, the optimal parameter combination is gradually approximated, providing solid data support for generating specific parameter adjustment schemes. This step, by clarifying optimization objectives, analyzing constraints, and employing intelligent algorithms for iterative optimization, ensures the scientific validity and feasibility of the parameter adjustment scheme. The rationality of the optimization directly impacts the extent to which reservoir stimulation effectiveness is improved.

[0024] Step S5, based on the iterative optimization results of S4, generates a complete, feasible, and directly applicable optimal fracturing parameter adjustment scheme for construction. The parameter optimization module first uses the results of multiple iterations of the genetic algorithm to precisely select fracturing parameter combinations from a massive pool of combinations that satisfy all constraints of reservoir geological parameters and maximize reservoir stimulation volume and shale gas production capacity. Parameter combinations that do not meet the constraints or have poor optimization effects are strictly excluded. Next, a comprehensive and detailed feasibility verification is conducted on the selected parameter combinations. Considering factors such as the performance parameters of the actual construction equipment, on-site construction conditions, and safe operating procedures, the operability of each parameter in actual construction is confirmed one by one to avoid situations where parameters exceed the equipment's capacity, cannot be implemented, or pose safety hazards. Subsequently, based on the verified parameter combinations, a detailed and specific optimal fracturing parameter adjustment scheme is generated, specifying the exact adjustment range for parameters such as fracturing fluid volume, proppant volume, displacement, and construction pressure. Simultaneously, the timing and sequence of parameter adjustments are precisely determined to ensure a smooth transition during the adjustment process and to avoid affecting the continuity and safety of fracturing operations. Finally, the generated optimal fracturing parameter adjustment scheme is standardized and packaged according to a preset standard format. This includes standardizing the data format, adding necessary operating instructions and precautions, and ensuring the integrity, accuracy, and transmission compatibility of the scheme data. This prepares the system for rapid and accurate feedback to the fracturing construction control system. This step, through a series of rigorous operations such as screening, verification, refinement, and packaging, forms a parameter adjustment scheme that can be directly applied to construction. The level of detail and feasibility of this scheme directly determines the success of subsequent construction adjustments.

[0025] Step S6, as a crucial closed-loop step in the entire method, enables real-time adjustment of fracturing parameters and dynamic optimization of the fracturing process. First, the optimal fracturing parameter adjustment scheme generated and packaged in S5 is fed back to the fracturing construction control system via a dedicated high-speed data transmission channel. Encryption and real-time data verification mechanisms are employed during transmission to ensure the scheme data is not lost or tampered with, and is delivered to the control system accurately and completely. Upon receiving the adjustment scheme, the fracturing construction control system immediately performs rapid analysis of each parameter, clarifying the target values, specific adjustment ranges, and precise adjustment timings for parameters such as fracturing fluid volume, proppant injection rate, displacement rate, and construction pressure. Simultaneously, the adjustment targets are compared one by one with the current construction parameters to formulate a detailed and feasible parameter adjustment execution plan. Subsequently, according to the execution plan, the control system sends precise control commands to the surface pump set, proppant injection system, pressure regulating device, and other construction equipment through a dedicated control interface, adjusting the operating parameters of each device in real time to achieve precise control of key construction parameters such as fracturing fluid injection volume, proppant injection rate, injection displacement rate, and construction pressure. During parameter adjustment, the control system monitors the operating status and parameter execution of each device in real time, and simultaneously receives the latest real-time data from seismic monitoring equipment and engineering monitoring equipment to ensure stable operation of the adjusted parameters. At the same time, the latest data is fed back to the subsequent monitoring and analysis stage, providing the latest and most accurate data support for the next round of monitoring, analysis and optimization decisions, effectively improving the accuracy and efficiency of fracturing operations, and ensuring the maximum effect of reservoir stimulation.

[0026] Preferably, the microseismic fracture inversion sub-model is constructed using the following formula: , in, This is a comprehensive quantitative value for crack characteristics. For the first Weighting coefficients for individual microseismic events. For the first The signal amplitude of a microseismic event For the first The duration of the signal for a microseismic event For the first Spatial distance between microseismic events and adjacent events For the first P-wave velocity of a microseismic event, For the first Shear wave velocity of a microseismic event, For the first The angle between the wave propagation direction and the crack propagation direction of a microseismic event. This represents the total number of microseismic events.

[0027] Specifically, the microseismic fracture inversion sub-model is based on the inherent correlation between microseismic signal characteristics and fracture geometric parameters. Analysis of a large amount of microseismic event data reveals that fracture characteristics can be comprehensively characterized by key parameters such as signal amplitude, duration, wave velocity ratio, and propagation direction angle. Therefore, these core variables are selected as model inputs. Considering the varying contributions of different microseismic events to fracture characterization, weighting coefficients are introduced to weight each variable. Two types of core influencing factors are integrated through linear superposition: one is a coupling term between the microseismic signal's own intensity and spatial distribution characteristics, obtained by dividing the product of signal amplitude and duration by the spatial distance between events, reflecting the influence of signal energy and event density on fracture morphology; the other is a product term of wave velocity ratio and the cosine of the propagation direction angle, reflecting the correlation between wave propagation characteristics and fracture propagation direction. The weighting coefficients were determined by fitting multiple sets of experimental data to ensure model accuracy. Signal amplitude and duration were extracted from the raw microseismic data using signal processing techniques. Spatial distance was calculated based on source location results. P-wave and S-wave velocities were obtained from seismic exploration data. The propagation direction angle was obtained by comparing the wave propagation path with the fracture inversion direction. The total number of microseismic events was determined based on monitoring duration and sampling frequency, typically not less than several hundred. The model's implementation involves preprocessing the microseismic signal data, extracting various parameters, and performing calculations. The resulting comprehensive quantitative value of fracture characteristics can be directly used to quantitatively describe fracture length, width, height, and propagation direction, providing accurate data support for subsequent fracture morphology analysis.

[0028] Preferably, the fracturing parameter-fracture response correlation sub-model is constructed using the following formula: , in, The comprehensive response value for crack propagation. These are the model fitting coefficients. This refers to the fracturing displacement. For the amount of sand added, Due to construction pressure, For reservoir permeability, This refers to reservoir porosity.

[0029] Specifically, the fracturing parameter-fracture response correlation sub-model is based on the nonlinear relationship between engineering parameters and fracture propagation. Statistical analysis of extensive fracturing operation data reveals that engineering parameters such as fracturing displacement, proppant loading, and operational pressure, along with geological parameters such as reservoir permeability and porosity, jointly influence fracture propagation response. Furthermore, the effects of each parameter on fracture response exhibit an exponential or product relationship. Therefore, a model combining product and exponential functions is used to construct the model. In the model, fracturing displacement, proppant loading, and operational pressure, as core engineering parameters directly affecting fracture propagation, are represented by exponential forms to characterize their nonlinear effects. Reservoir permeability and porosity, as geological constraints, are represented by exponential functions to reflect their indirect regulatory role on fracture propagation. Simultaneously, fitting coefficients are introduced to calibrate the influence weights of each parameter. The fitting coefficients are determined through multiple regression analysis to fit historical operational data, ensuring that the model accurately reflects the actual correlation patterns. Fracturing displacement, proppant loading, and operational pressure are obtained in real-time from engineering monitoring data, while reservoir permeability and porosity are determined through geological exploration and core experiment data. The implementation of this model requires the collection of preprocessed engineering and geological data, which are then substituted into the model to calculate the comprehensive response value of crack propagation. This value can quantify the combined influence of engineering and geological parameters on crack morphology, providing core support for establishing a quantitative mapping relationship between the two.

[0030] Preferably, the genetic algorithm optimization process uses the following fitness function: , in, This is a vector of fracturing parameters. These are the weighting coefficients. For reservoir stimulation volume, This refers to the number of fracturing parameters. For the first Actual values ​​of each fracturing parameter. For the first Optimal reference values ​​for each fracturing parameter For the first Maximum and minimum allowable values ​​for each fracturing parameter.

[0031] Specifically, the fitness function of the genetic algorithm focuses on the synergy between the optimization objective and constraints. Since parameter optimization requires simultaneously maximizing the reservoir stimulation volume and minimizing parameter deviations from the optimal reference value, a linear combination is used to construct the function. The reservoir stimulation volume is taken as the positive optimization objective, and the sum of squared parameter deviations is used as a penalty term. The positive objective term is highlighted by weight coefficients, while the penalty term is calculated by normalizing the degree of deviation of each parameter from the optimal reference value (divided by the difference between the maximum and minimum allowable values ​​of the parameters), summing the results, and multiplying by the penalty weight to ensure that the parameter adjustments are within a reasonable range. The weight coefficients are determined based on engineering requirements and optimization priorities, with the weight of the positive objective term typically greater than that of the penalty term. The reservoir stimulation volume is calculated based on fracture morphology parameters, with actual parameter values ​​obtained from real-time construction data. The optimal reference value is determined by combining historical best construction data and reservoir conditions. The maximum and minimum allowable values ​​are determined based on equipment performance, construction safety regulations, and reservoir constraints. The number of parameters is determined based on actual optimization needs, typically including 4-6 core parameters. The implementation of this function requires first clarifying the values ​​of each parameter, then substituting them into the function to calculate the fitness value of each parameter combination. The genetic algorithm performs selection, crossover, and mutation operations based on the fitness values, and through multiple rounds of iteration, selects the parameter combination with the best fitness, providing a quantitative basis for parameter optimization.

[0032] Preferably, the distribution of geological parameters in the three-dimensional geological model of the reservoir is calculated using the following formula: , in, Spatial coordinates Geological parameter values ​​at the location, For the first The weight of each sampling point For the first Measured geological parameters at each sampling point The total number of sampling points. For the first Spatial coordinates of a reference point For the number of reference points, It is a local minimum constant.

[0033] Specifically, the geological parameter distribution formula of the reservoir 3D geological model is based on the principle of spatial interpolation. Considering the spatial continuity of reservoir geological parameters and the discreteness of sampling points, a weighted summation method is used to construct the model. By integrating measured data from multiple sampling points and spatial distance information from reference points, the model can estimate geological parameters at any spatial coordinate. In the model, the weight coefficient of each sampling point is determined according to its influence on the target coordinates. The spatial distance term uses the ratio of Manhattan distance to maximum distance, and a minimum constant is introduced to avoid zero denominators and ensure computational stability. The sampling point weight coefficients are determined using the inverse distance weighting method or Kriging interpolation. The total number of sampling points is determined according to the exploration accuracy requirements, usually not less than several dozen. The number of reference points is determined according to the fracturing area range to ensure coverage of the entire target area. Measured values ​​of sampling points are obtained from geological exploration, well logging, and core test data. The spatial coordinates of reference points are determined according to the 3D grid division of the fracturing area. The minimum constant is a positive number much less than 1, typically between 0.001 and 0.01. The implementation of this formula requires first organizing the sampling point and reference point data, calculating the spatial distance between the target coordinates and each sampling point and reference point, substituting them into the formula to calculate the geological parameter values ​​at the target coordinates, and then constructing a complete three-dimensional geological parameter distribution field of the reservoir by calculating the three-dimensional grid of the entire fracturing area one by one, providing geological data support for the integrated model.

[0034] Preferably, the degree of fit between the fracture propagation and the distribution of high-quality reservoir zones is calculated using the following formula: , in, To match the quantified value, This represents the actual crack propagation area. This is within the high-quality reservoir area. Spatial coordinates The reservoir quality coefficient at that location It is a spatial volume element.

[0035] Specifically, the formula for the fit between fracture propagation and the distribution of high-quality reservoir areas is based on a dual consideration of spatial overlap and quality weighting. Since the fit must simultaneously reflect the degree of spatial overlap between the fracture propagation area and the high-quality reservoir area, as well as the uniformity of fracture distribution within the high-quality area, the formula is constructed using the product of two integral ratios. The first integral ratio reflects the effective coverage of fractures within the high-quality area by integrating the reservoir quality coefficient within the actual fracture propagation area and the high-quality reservoir area. The second integral ratio reflects the spatial overlap ratio by integrating the volumes of the two areas. The reservoir quality coefficient is comprehensively evaluated based on geological parameters such as reservoir lithology, porosity, and permeability, and its value ranges from 0 to 1. The range of the high-quality area is determined through geological model analysis, specifically defining areas where the reservoir quality coefficient exceeds a preset threshold. The actual fracture propagation area is determined using a microseismic fracture inversion sub-model, and the volumetric micro-elements are determined based on a three-dimensional mesh. The mesh size is determined according to the required computational accuracy, typically between 1 and 5 meters. The implementation of this formula requires first clarifying the spatial range of the two regions and the distribution of the reservoir quality coefficient. The ratio of the two integrals is calculated by numerical integration, and the product result is the quantified value of the fit metric. This value can accurately reflect the degree of matching between fracture propagation and the reservoir quality zone, providing a quantitative standard for judging the rationality of fracture propagation.

[0036] Preferred, such as Figure 2 As shown, S2 includes the following sub-steps: S21, by integrating reservoir structural data, lithological data, rock mechanical parameters, and reservoir fluid parameters through processed reservoir geological data, a three-dimensional geological model of the shale gas reservoir covering the fracturing area is constructed using a spatial interpolation algorithm to determine the spatial distribution characteristics of reservoir lithology, porosity, and permeability geological parameters; S22, based on preprocessed microseismic signal data, the dominant frequency and energy attenuation characteristic parameters of the signal are extracted, and a microseismic fracture inversion sub-model is established in conjunction with a source location algorithm to quantitatively characterize the length, width, height, and propagation direction of the fracturing fractures; S23, preprocessed fracturing construction engineering data are collected, and a fracturing parameter-fracture response correlation sub-model is constructed using fracturing fluid volume, proppant addition volume, and displacement volume as input variables and fracture propagation morphology parameters as output variables to establish a quantitative mapping relationship between the two; S24, the three-dimensional geological model of the reservoir, the microseismic fracture inversion sub-model, and the fracturing parameter-fracture response correlation sub-model are coupled through a data interface to perform real-time data interaction and collaboration calculations between the models, forming an integrated seismic-geological-engineering model.

[0037] Specifically, step S2 constructs an integrated seismic-geological-engineering model through four sub-steps. S21, using processed reservoir geological data, comprehensively integrates reservoir structural data obtained from seismic exploration, lithological data from well logging, rock mechanical parameters such as elastic modulus and Poisson's ratio measured by core experiments, and reservoir fluid parameters such as formation pressure and fluid viscosity. A spatial interpolation algorithm is used to construct a three-dimensional geological model of the shale gas reservoir covering the fracturing area, clarifying the spatial distribution characteristics of geological parameters such as reservoir lithology, porosity, and permeability, providing a precise geological basis for subsequent analysis. S22, based on preprocessed microseismic signal data, extracts characteristic parameters such as signal dominant frequency and energy attenuation, and combines this with a source location algorithm to establish a microseismic fracture inversion sub-model. This sub-model can quantitatively characterize the length, width, height, and propagation direction of fracturing fractures, with a location accuracy better than 5 meters. S23 collects pre-processed fracturing construction data, using fracturing fluid volume, proppant addition, and displacement as input variables, and fracture propagation morphology parameters as output variables, to construct a fracturing parameter-fracture response correlation sub-model and establish a quantitative mapping relationship between the two. S24 deeply couples the reservoir 3D geological model, the microseismic fracture inversion sub-model, and the fracturing parameter-fracture response correlation sub-model through a dedicated data interface, enabling real-time data interaction and collaborative computation between the models, forming an integrated seismic-geological-engineering model. This model can improve the accuracy of reservoir fracture prediction to 92% and shorten the geological model update cycle to 5 hours, providing core technical support for subsequent real-time monitoring and dynamic optimization.

[0038] Preferred, such as Figure 3 As shown, step S3 includes the following sub-steps: S31, continuously collecting microseismic signal data and fracturing construction engineering data during the fracturing process through seismic monitoring equipment and engineering monitoring equipment, and uploading the data to the data processing center in real time according to a preset transmission protocol; S32, synchronously inputting the real-time collected and processed microseismic signal data and fracturing construction engineering data into the integrated seismic-geological-engineering model to trigger the model calculation process; S33, calling the microseismic fracture inversion sub-model in the integrated model to analyze the input microseismic signal data and invert the current fracturing fracture length, width, height, and propagation direction morphological parameters; S34, using the fracturing parameter-fracture response correlation sub-model, analyzing the correspondence between the current fracturing construction engineering parameters and the fracture propagation morphological parameters, and combining the spatial distribution information of the reservoir's high-quality areas in the three-dimensional geological model of the reservoir to determine whether the current fracture propagation path is consistent with the distribution of the reservoir's high-quality areas.

[0039] Specifically, step S3 completes real-time monitoring and data analysis of the fracturing process through four sub-steps. S31 utilizes a seismic monitoring system consisting of a three-component geophone array (sampling frequency 1000-2000Hz) spaced 50-100 meters around the wellhead and downhole fiber optic sensors deployed along the inner wall of the horizontal well casing with sampling intervals no greater than 1 meter, along with engineering monitoring equipment. This system continuously collects microseismic signal data and fracturing construction data during the fracturing process and uploads the data to the data processing center in real-time according to a preset transmission protocol, ensuring a data transmission delay of no more than 1 second. S32 synchronously inputs the real-time collected and processed microseismic signal data and fracturing construction data into the integrated seismic-geological-engineering model, triggering the model calculation process with a calculation interval set to once every 30 seconds. S33 calls the microseismic fracture inversion sub-model within the integrated model to analyze the input microseismic signal data and invert the morphological parameters of the current fracturing fracture, such as its length, width, height, and propagation direction. The inversion response time is no more than 10 seconds. S34 utilizes a fracturing parameter-fracture response correlation sub-model to analyze the correspondence between current fracturing construction engineering parameters and fracture propagation morphology parameters. Combined with the spatial distribution information of high-quality reservoir areas in the three-dimensional geological model of the reservoir, it determines whether the current fracture propagation path is consistent with the distribution of high-quality reservoir areas. The judgment results are output in the form of quantitative values, providing a clear basis for subsequent optimization decisions.

[0040] Preferred, such as Figure 4 As shown, S4 includes the following sub-steps: S41, receiving the data analysis results from S3, comparing the current fracture propagation morphology parameters with the preset target parameters, and determining whether the fracture propagation has reached the preset target; S42, if the preset target has not been reached, activating the parameter optimization module, and determining the core optimization direction as maximizing reservoir stimulation volume and improving shale gas production capacity; S43, sorting out the constraints in the reservoir geological parameters, including the limiting requirements of reservoir permeability, porosity, and rock mechanics parameters on fracturing parameters; S44, calling the genetic algorithm, using fracturing fluid volume, proppant volume, discharge volume, and construction pressure as optimization variables, and performing multiple rounds of iterative calculations within the constraint range to gradually approach the optimal parameter combination.

[0041] Specifically, step S4 iteratively optimizes fracturing parameters through four sub-steps. S41 receives the data analysis results from S3 and compares the currently inverted fracture length, width, height, and propagation direction with preset target parameters (e.g., fracture length must reach at least 90% of the preset value, propagation direction must deviate from the high-quality zone by no more than 15°). It determines whether the fracture propagation has reached the preset target; this comparison and analysis process is completed within 5 seconds. S42 If the preset target is not reached, the parameter optimization module is immediately activated, clearly defining maximizing reservoir stimulation volume and improving shale gas production capacity as the core optimization directions. The target weights are set at 60% for reservoir stimulation volume and 40% for production capacity improvement. S43 reviews the constraints in the reservoir geological parameters, including fracturing pressure not exceeding 80% of the casing's internal pressure resistance, proppant concentration not exceeding the upper limit of the fracturing fluid's proppant-carrying capacity, and displacement not lower than the critical displacement corresponding to the reservoir's conductivity. Simultaneously, it integrates the constraints on fracturing parameters from reservoir permeability, porosity, and rock mechanics parameters to form a complete constraint system. S44 uses a genetic algorithm, taking fracturing fluid volume, sand addition volume, displacement volume, and construction pressure as optimization variables. It sets the number of iterations to 100 and the population size to 50, and performs multiple rounds of iterative calculations within the constraints. Each iteration takes no more than 8 seconds to gradually approach the optimal parameter combination, ensuring the scientific validity and timeliness of the optimization results.

[0042] Preferred, such as Figure 5 As shown, step S5 includes the following sub-steps: S51, the parameter optimization module selects fracturing parameter combinations that meet the constraints and can be optimized based on the iterative calculation results of the genetic algorithm; S52, the selected parameter combinations are verified for feasibility to confirm the operability of each parameter in actual construction; S53, based on the verified parameter combinations, a detailed optimal fracturing parameter adjustment scheme is generated to determine the adjustment range and timing of each parameter; S54, the optimal fracturing parameter adjustment scheme is packaged according to a preset format to ensure the integrity and transmission compatibility of the scheme data.

[0043] Specifically, step S5 generates the optimal fracturing parameter adjustment scheme through four sub-steps. S51: The parameter optimization module, based on the iterative calculation results of the genetic algorithm, selects fracturing parameter combinations from 50 individuals that meet all constraints and achieve an optimization target of over 95%. The selection process strictly adheres to quantitative indicators, eliminating unqualified combinations. S52: The feasibility of the selected parameter combinations is verified. Verification includes whether the parameters are within the rated range of the construction equipment (e.g., maximum pump displacement not exceeding 20 cubic meters per minute, maximum construction pressure not exceeding 70 MPa) and whether they comply with on-site construction safety regulations. The verification time is controlled within 10 seconds to ensure no impact on construction continuity. S53: Based on the verified parameter combinations, a detailed optimal fracturing parameter adjustment scheme is generated, specifying the adjustment range of each parameter (e.g., displacement adjustment not exceeding 20% ​​of the current value, proppant addition adjustment controlled within 10%) and the adjustment timing (e.g., initiating adjustment when the current fracturing section reaches 30% progress). The scheme details are precise down to specific numerical values. S54 encapsulates the optimal fracturing parameter adjustment scheme according to a preset standardized format, including six core contents such as parameter name, current value, target value, adjustment range, and adjustment timing, to ensure the integrity of the scheme data and transmission compatibility. The size of the encapsulated scheme data is controlled within 1KB, which facilitates rapid transmission to the construction control system.

[0044] like Figure 6As shown, a real-time seismic monitoring and dynamic feedback optimization system for shale gas horizontal well fracturing is presented. This system, applied to a method for real-time seismic monitoring and dynamic feedback optimization of shale gas horizontal well fracturing, includes: a multi-source data acquisition unit, an integrated model construction unit, a real-time data processing and analysis unit, a parameter optimization calculation unit, an optimal solution generation unit, and a construction parameter control unit. The multi-source data acquisition unit comprehensively captures microseismic signals, reservoir geology, and fracturing construction engineering data through seismic monitoring, geological exploration, and engineering monitoring equipment, providing basic data support for system operation. The acquired data is transmitted in real-time to the integrated model construction unit. Based on the received data, the integrated model construction unit establishes sub-models for reservoir 3D geology, microseismic fracture inversion, and fracturing parameter-fracture response correlation. The data is coupled to form an integrated model, which is then transmitted to the real-time data processing and analysis unit. The real-time data processing and analysis unit receives the real-time acquired data and inputs it into the integrated model, inverts fracture morphology parameters, analyzes the influence of engineering parameters, and judges the compatibility between fractures and high-quality reservoir areas. The analysis results are simultaneously sent to the parameter optimization calculation unit. If the parameter optimization calculation unit determines that the fracture propagation has not reached the preset target, it combines the reservoir geological constraints and uses a genetic algorithm to iteratively optimize the key fracturing parameters. The optimization results are transmitted to the optimal scheme generation unit. The optimal scheme generation unit generates a detailed optimal fracturing parameter adjustment scheme after screening and verifying the iterative optimization results, and then feeds the scheme back to the construction parameter control unit. After receiving the scheme, the construction parameter control unit adjusts the fracturing construction control system in real time to complete the dynamic optimization of the fracturing process.

[0045] A method and system for real-time seismic monitoring and dynamic feedback optimization of geological engineering for shale gas horizontal well fracturing is proposed. This system constructs an integrated collaborative system of seismic, geological, and engineering aspects, overcoming the limitations of data fragmentation and decision-making lag in traditional fracturing technologies. By comprehensively collecting multi-type monitoring data and integrating reservoir geology, microseismic signals, and construction engineering information, deep fusion and real-time interaction of cross-domain data are achieved, freeing fracturing decisions from reliance on static models and empirical parameters. Simultaneously, relying on a dynamic feedback mechanism and parameter optimization module, the system can capture fracture propagation status in real time, accurately analyze the correlation between construction parameters and fracture response, and quickly generate optimized schemes adapted to dynamic reservoir changes, significantly improving the accuracy and flexibility of fracturing operations.

[0046] This method addresses the problem that traditional designs cannot adapt to dynamic changes in reservoirs. By using real-time monitoring and integrated model calculations, it dynamically inverts fracture morphology parameters, promptly detects changes in reservoir stress, permeability, and other parameters, and enables real-time adjustment of construction parameters, completely eliminating reliance on static models. Addressing the issues of insufficient data integration and difficulty in transforming monitoring results into optimization criteria in existing technologies, it establishes a quantitative correlation between construction parameters and fracture propagation by constructing an integrated system with coupled multi-sub-models. Simultaneously, it introduces optimization algorithms and constraint verification to ensure that monitoring data can be directly transformed into scientifically feasible construction adjustment schemes. This significantly improves the uniformity and resource utilization of reservoir stimulation, achieving precise alignment between fracture propagation and high-quality reservoir zones.

[0047] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0048] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0049] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0051] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing, characterized in that, Includes the following steps: S1. Acquire microseismic signal data captured by seismic monitoring equipment, reservoir geological data collected by geological exploration equipment, and fracturing construction engineering data recorded by engineering monitoring equipment during the fracturing process of shale gas horizontal wells; S2. Construct an integrated seismic-geological-engineering model. Based on the reservoir geological data, establish a three-dimensional geological model of the shale gas reservoir. Combine the microseismic signal data to establish a microseismic fracture inversion sub-model. Integrate the fracturing construction engineering data to construct a fracturing parameter-fracture response correlation sub-model, coupling the three into an integrated model; S3. During fracturing construction, input the real-time acquired microseismic signal data and fracturing construction engineering data into the integrated model. Invert the current fracturing fracture morphology parameters through the microseismic fracture inversion sub-model. The influence of engineering parameters on fracture propagation is analyzed through a fracturing parameter-fracture response correlation sub-model. The correlation between fracture propagation and the distribution of high-quality reservoir areas is assessed using a 3D reservoir geological model. In step S4, based on the analysis results of step S3, if fracture propagation does not reach the preset target, the parameter optimization module is activated. Guided by maximizing reservoir stimulation volume and improving shale gas production capacity, and considering reservoir geological parameter constraints, a genetic algorithm iteratively optimizes the engineering parameters of fracturing fluid volume, proppant addition, displacement, and construction pressure. In step S5, the optimal fracturing parameter adjustment scheme is generated through the parameter optimization module. In step S6, the optimal fracturing parameter adjustment scheme is fed back to the fracturing construction control system for real-time adjustment of construction parameters, completing the dynamic optimization of the fracturing process.

2. The method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, The microseismic fracture inversion sub-model is constructed using the following formula: ， in, This is a comprehensive quantitative value for crack characteristics. For the first Weighting coefficients for individual microseismic events. For the first The signal amplitude of a microseismic event For the first The duration of the signal for a microseismic event For the first Spatial distance between microseismic events and adjacent events For the first P-wave velocity of a microseismic event, For the first Shear wave velocity of a microseismic event, For the first The angle between the wave propagation direction and the crack propagation direction of a microseismic event. This represents the total number of microseismic events.

3. The method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, The fracturing parameter-fracture response correlation sub-model is constructed using the following formula: ， in, The comprehensive response value for crack propagation. These are the model fitting coefficients. This refers to the fracturing displacement. For the amount of sand added, Due to construction pressure, For reservoir permeability, This refers to reservoir porosity.

4. The method and system for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing as described in claim 1, characterized in that, The genetic algorithm optimization process uses the following fitness function: ， in, This is a vector of fracturing parameters. These are the weighting coefficients. For reservoir stimulation volume, The number of fracturing parameters. For the first Actual values ​​of each fracturing parameter. For the first Optimal reference values ​​for each fracturing parameter For the first Maximum and minimum allowable values ​​for each fracturing parameter.

5. The method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, The distribution of geological parameters in the three-dimensional geological model of the reservoir is calculated using the following formula: ， in, Spatial coordinates Geological parameter values ​​at the location, For the first The weight of each sampling point For the first Measured geological parameters at each sampling point The total number of sampling points. For the first Spatial coordinates of a reference point For the number of reference points, It is a local minimum constant.

6. The method and system for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, The degree of fit between fracture propagation and the distribution of high-quality reservoir zones is calculated using the following formula: ， in, To match the quantified value, This represents the actual crack propagation area. This is within the high-quality reservoir area. Spatial coordinates The reservoir quality coefficient at that location It is a spatial volume element.

7. The method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, S2 includes the following sub-steps: S21, by integrating reservoir structural data, lithological data, rock mechanical parameters, and reservoir fluid parameters through processed reservoir geological data, a three-dimensional geological model of the shale gas reservoir covering the fracturing area is constructed using a spatial interpolation algorithm to determine the spatial distribution characteristics of reservoir lithology, porosity, and permeability geological parameters; S22, based on preprocessed microseismic signal data, the dominant frequency and energy attenuation characteristic parameters of the signal are extracted, and a microseismic fracture inversion sub-model is established in conjunction with a source location algorithm to quantitatively characterize the length, width, height, and propagation direction of the fracturing fractures; S23, preprocessed fracturing construction engineering data are collected, and a fracturing parameter-fracture response correlation sub-model is constructed using fracturing fluid volume, proppant addition volume, and displacement volume as input variables and fracture propagation morphology parameters as output variables to establish a quantitative mapping relationship between the two; S24, the three-dimensional geological model of the reservoir, the microseismic fracture inversion sub-model, and the fracturing parameter-fracture response correlation sub-model are coupled through a data interface to perform real-time data interaction and collaboration calculations between the models, forming an integrated seismic-geological-engineering model.

8. The method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, S3 includes the following steps: S31, continuously collecting microseismic signal data and fracturing construction engineering data during the fracturing process through seismic monitoring equipment and engineering monitoring equipment, and uploading the data to the data processing center in real time according to a preset transmission protocol; S32, synchronously inputting the real-time collected and processed microseismic signal data and fracturing construction engineering data into the integrated seismic-geological-engineering model to trigger the model calculation process; S33, calling the microseismic fracture inversion sub-model in the integrated model to analyze the input microseismic signal data and invert the current fracturing fracture length, width, height, and propagation direction morphological parameters; S34, using the fracturing parameter-fracture response correlation sub-model, analyzing the correspondence between the current fracturing construction engineering parameters and fracture propagation morphological parameters, and combining the spatial distribution information of the reservoir's high-quality areas in the three-dimensional geological model of the reservoir to determine whether the current fracture propagation path is consistent with the distribution of the reservoir's high-quality areas.

9. The method for real-time seismic monitoring and dynamic geological engineering feedback optimization for shale gas horizontal well fracturing according to claim 1, characterized in that, S4 includes the following steps: S41, receiving the data analysis results of S3, comparing the current crack propagation morphology parameters with the preset target parameters, and determining whether the crack propagation has reached the preset target; S42. If the preset target is not achieved, activate the parameter optimization module to determine the core optimization direction as maximizing reservoir stimulation volume and improving shale gas production capacity. S43. Review the constraints in reservoir geological parameters, including the limitations of reservoir permeability, porosity, and rock mechanics parameters on fracturing parameters. S44. Call the genetic algorithm to use fracturing fluid volume, sand addition volume, discharge volume, and construction pressure as optimization variables, and perform multiple rounds of iterative calculations within the constraints to gradually approach the optimal parameter combination.

10. A real-time seismic monitoring and dynamic geological engineering feedback optimization system for shale gas horizontal well fracturing, characterized in that, This system is applied to the seismic real-time monitoring and geological engineering dynamic feedback optimization method for shale gas horizontal well fracturing as described in claim 1, comprising: a multi-source data acquisition unit, an integrated model construction unit, a real-time data processing and analysis unit, a parameter optimization calculation unit, an optimal scheme generation unit, and a construction parameter control unit. The multi-source data acquisition unit comprehensively captures microseismic signals, reservoir geology, and fracturing construction engineering data through seismic monitoring, geological exploration, and engineering monitoring equipment, providing basic data support for system operation. The acquired data is transmitted to the integrated model construction unit in real time. Based on the received data, the integrated model construction unit establishes reservoir 3D geology, microseismic fracture inversion, and fracturing parameter-fracture response correlation sub-models, and couples them to form an integrated model. Subsequently, the integrated model is... The model is transmitted to the real-time data processing and analysis unit. This unit receives the real-time acquired data and inputs it into the integrated model, inverting fracture morphology parameters, analyzing the impact of engineering parameters, and determining the compatibility between the fracture and the high-quality reservoir area. The analysis results are simultaneously sent to the parameter optimization calculation unit. If the parameter optimization calculation unit determines that the fracture propagation has not reached the preset target, it combines reservoir geological constraints and uses a genetic algorithm to iteratively optimize the key fracturing parameters. The optimization results are then transmitted to the optimal solution generation unit. The optimal solution generation unit, after screening and verifying the iterative optimization results, generates a detailed optimal fracturing parameter adjustment scheme and feeds the scheme back to the construction parameter control unit. Upon receiving the scheme, the construction parameter control unit adjusts the fracturing construction control system in real time, completing the dynamic optimization of the fracturing process.

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