Fracturing control method and system for slotting equipment, computer equipment and computer program

Abstract

The invention relates to a fracturing control method for slotting equipment. The fracturing control method comprises the steps that initial slotting parameters are determined according to stratum characteristic information, oil and gas distribution information and preset yield increase demand information of a target reservoir; constructing a slotting simulation model according to a preset slotting fracturing data set, the stratum characteristic information and the oil and gas distribution information; according to the initial slotting parameters, slotting equipment is controlled to operate, and key parameter information of a fracturing point is obtained; iterating the slotting simulation model according to the key parameter information of the fracturing point; performing fracturing simulation on the initial slotting parameters according to the optimized slotting simulation model to obtain slotting fracturing simulation parameters, and determining a plurality of target slotting fracturing points according to the slotting fracturing simulation parameters; according to the slotting and fracturing simulation parameters and the multiple target slotting and fracturing points, simulation calibration is conducted on the initial slotting parameters to obtain slotting correction parameters, and fracturing control is conducted on slotting equipment. The defect that the fracturing effect is poor due to the fact that complex and changeable geological conditions of the high-temperature reservoir are difficult to adapt is overcome.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of reservoir fracturing, in particular to a fracturing control method and system of a slotted device, a computer device and a computer program. BACKGROUND

[0002] With the continuous growth of global energy demand and the gradual depletion of traditional oil resources, the field of oil and gas exploration and development is facing unprecedented challenges. High-temperature reservoirs are of great concern due to their complex geological conditions and abundant oil and gas resources. With the continuous development of petroleum engineering technology, slotted fracturing technology has become one of the important means to improve the production of oil and gas fields. However, the development of reservoirs in high-temperature environments faces many technical challenges, especially in the key step of slotted fracturing. Due to high formation temperature, high rock hardness, low permeability, and complex and variable mechanical properties, traditional fracturing methods often fail to adapt to the special conditions of high-temperature reservoirs, resulting in low development efficiency of oil and gas and even potential safety risks.

[0003] Therefore, in the current analysis of electric slotted multi-point fracturing of high-temperature reservoirs, it is difficult to adapt to the complex and variable geological conditions of high-temperature reservoirs and extreme high-temperature environments, resulting in poor fracturing results and poor safety and controllability. SUMMARY

[0004] The present disclosure provides a fracturing control method and device of a slotted device, a computer device, a storage medium and a computer program. By using technical means such as constructing a slotted simulation model of the target reservoir, the technical problems of the existing electric slotted multi-point fracturing analysis, such as the difficulty in adapting to the complex and variable geological conditions of high-temperature reservoirs and extreme high-temperature environments, resulting in poor fracturing results and poor safety and controllability, are solved, achieving the technical effects of improving fracturing results and reducing safety risks in the fracturing process.

[0005] In a first aspect, the present disclosure provides a fracturing control method of a slotted device, wherein a sensor for collecting key parameter information of a fracturing point is arranged on the slotted device, and the method comprises:

[0006] According to the formation characteristic information and the oil and gas distribution information of the target reservoir and the preset stimulation demand information, the initial slotted parameters of the slotted device are determined through a preset slotted parameter analysis model;

[0007] According to the preset slotted fracturing data set, the formation characteristic information and the oil and gas distribution information of the target reservoir, a simulation model is constructed to build a slotted simulation model of the target reservoir;

[0008] The fracturing equipment is controlled to perform fracturing operations based on the initial fracturing parameters, and key parameter information of the fracturing point is acquired through the sensor during the fracturing control process.

[0009] The fracturing point key parameter information is used to iteratively optimize the fracturing simulation model to obtain the optimized fracturing simulation model.

[0010] Based on the optimized fracturing simulation model, the initial fracturing parameters are subjected to fracturing simulation to obtain fracturing simulation parameters, and multiple target fracturing points of the target reservoir are determined based on the fracturing simulation parameters.

[0011] Based on the fracturing simulation parameters and the multiple target fracturing points, the initial fracturing parameters are simulated and calibrated to obtain fracturing correction parameters, and the fracturing control of the fracturing equipment is performed based on the fracturing correction parameters.

[0012] In some embodiments, determining the initial slotting parameters of the slotting equipment based on the formation characteristics information and oil and gas distribution information of the target reservoir, as well as preset production enhancement demand information, through a preset slotting parameter analysis model includes:

[0013] A slotting data space is generated based on preset data of the slotting equipment in fracturing operations, and the slotting data space is divided and identified to determine the input data and output data.

[0014] The input and output data are trained using a deep learning network structure to construct the preset kerf parameter analysis model;

[0015] The initial fracture parameters are determined by analyzing the formation characteristics, oil and gas distribution, and preset production increase demand information using the preset fracture parameter analysis model.

[0016] In some embodiments, the step of performing simulation modeling based on a preset fracture fracturing dataset of the target reservoir, the formation characteristic information, and the hydrocarbon distribution information to construct a fracture simulation model of the target reservoir includes:

[0017] The formation characteristics and oil and gas distribution information are spatially modeled using three-dimensional modeling to construct a spatial model of the target reservoir.

[0018] Preprocess the preset fracture fracturing dataset of the target reservoir to obtain the standard fracture fracturing dataset of the target reservoir.

[0019] The spatial model is used as the constant simulation parameter for the fracturing, and the standard fracturing dataset for the fracturing is used as the momentum simulation parameter for the fracturing. Based on the constant simulation parameter and the momentum simulation parameter, the fracturing simulation is performed to construct the fracturing simulation model of the target reservoir.

[0020] In some embodiments, the step of iteratively optimizing the fracturing simulation model based on the key parameter information of the fracturing point to obtain an optimized fracturing simulation model includes:

[0021] Based on the fracturing simulation model, the initial fracturing parameters are simulated to obtain the initial fracturing simulation parameters. The difference between the initial fracturing simulation parameters and the key parameter information of the fracturing point is used as the target optimization parameter variable.

[0022] Extract the parameters of the kerf simulation model to obtain the simulation model parameters;

[0023] The simulation model parameters are iteratively optimized based on the target optimization parameter variables to obtain optimized simulation model parameters. The kerf simulation model is then optimized and configured based on the optimized simulation model parameters to obtain the optimized kerf simulation model.

[0024] In some embodiments, the step of performing fracturing simulation on the initial fracturing parameters according to the optimized fracturing simulation model to obtain fracturing simulation parameters, and determining multiple target fracturing points of the target reservoir based on the fracturing simulation parameters, includes:

[0025] Stress concentration analysis and crack propagation analysis were performed on the simulated parameters of the slotted fracturing to obtain the corresponding set of stress concentration zones and set of crack propagation characteristics.

[0026] Based on the set of stress concentration zones and the set of crack propagation characteristics, fracturing points are identified to determine the set of potential slotted fracturing points.

[0027] Based on preset risk feasibility factors, risk assessment is performed on each potential slotted fracturing point in the set of potential slotted fracturing points in sequence to obtain a set of risk coefficients for slotted fracturing points.

[0028] Based on the set of risk coefficients for the slotted fracturing points, the plurality of target slotted fracturing points are determined from the set of potential slotted fracturing points using preset screening conditions.

[0029] In some embodiments, the step of simulating and calibrating the initial fracturing parameters to obtain fracturing correction parameters based on the fracturing simulation parameters and the plurality of target fracturing points includes:

[0030] Based on the optimized slotting simulation model, the slotting fracturing simulation parameters and the multiple target slotting fracturing points are simulated to obtain the slotting fracturing simulation results.

[0031] Permeability analysis was performed on the simulated fracture to obtain fracture permeability parameters;

[0032] Perform a production enhancement prediction operation on the fracture permeability parameters to obtain reservoir production enhancement prediction information;

[0033] Based on the comparison between the reservoir production enhancement prediction information and the preset production enhancement demand information, a target optimization production enhancement effect factor is determined. The initial slotting parameters are then calibrated and optimized based on the target optimization production enhancement effect factor to obtain the slotting correction parameters.

[0034] In some embodiments, the step of calibrating and optimizing the initial slotting parameters according to the target optimized yield-increasing effect factor to obtain the slotting correction parameters includes:

[0035] Based on the stated objectives, optimize the production increase effect factors and formulate parameter strategy optimization rules;

[0036] The initial kerf parameters are optimized, expanded, and updated according to the parameter strategy optimization rules to obtain multiple updated kerf parameters.

[0037] The optimized kerf simulation model is used to simulate and optimize the multiple kerf update parameters to obtain the kerf correction parameters.

[0038] Secondly, this disclosure provides a fracturing control system, wherein the fracturing equipment is equipped with sensors for collecting key parameter information of the fracturing point, the system comprising:

[0039] The initial slotting parameter determination module is used to determine the initial slotting parameters of the slotting equipment based on the formation characteristics information and oil and gas distribution information of the target reservoir, as well as the preset production enhancement demand information, through a preset slotting parameter analysis model.

[0040] The fracturing simulation model construction module is used to perform simulation modeling based on the preset fracturing dataset of the target reservoir, the formation characteristic information, and the oil and gas distribution information to construct the fracturing simulation model of the target reservoir.

[0041] The fracturing point key parameter information acquisition module is used to control the fracturing equipment to perform fracturing operation according to the initial fracturing parameters, and to acquire fracturing point key parameter information through the sensor during the fracturing control process;

[0042] The slotting simulation model optimization module is used to iteratively optimize the slotting simulation model based on the key parameter information of the fracturing point to obtain the optimized slotting simulation model.

[0043] The target fracturing point determination module is used to perform fracturing simulation on the initial fracturing parameters according to the optimized fracturing simulation model, obtain the fracturing simulation parameters, and determine multiple target fracturing points of the target reservoir according to the fracturing simulation parameters.

[0044] The control module is used to simulate and calibrate the initial kerf parameters to obtain kerf correction parameters based on the kerf fracturing simulation parameters and the plurality of target kerf fracturing points, and to perform fracturing control on the kerf equipment based on the kerf correction parameters.

[0045] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0046] Fourthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0047] This disclosure solves the technical problems of existing electric fracturing analysis methods, such as constructing a fracturing simulation model of the target reservoir and simulation, which are difficult to adapt to the complex and variable geological conditions and extreme high-temperature environment of high-temperature reservoirs, resulting in poor fracturing effect and poor safety and controllability. It achieves the technical effect of improving fracturing effect and reducing safety risks in the fracturing process. Attached Figure Description

[0048] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0049] Figure 1 This is a schematic flowchart illustrating a fracturing control method for a slotting device provided in an embodiment of this disclosure.

[0050] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0054] Example One

[0055] This embodiment discloses a fracturing control method for a slotting device, which can be used to control the operation of a slotting device (e.g., an electric slotting device), wherein a sensor for collecting key parameter information of the fracturing point is installed on the slotting device.

[0056] Figure 1 This is a schematic flowchart illustrating a fracturing control method for a slotting device provided in an embodiment of this disclosure. Figure 1 As shown, the fracturing control method for the slotting equipment provided in this embodiment includes the following steps:

[0057] Step 110: Based on the formation characteristics and oil and gas distribution information of the target reservoir and the preset production increase demand information, determine the initial cutting parameters of the cutting equipment through the preset cutting parameter analysis model.

[0058] Optionally, the target reservoir in this embodiment may be a target high-temperature reservoir.

[0059] The formation characteristics information for the target high-temperature reservoir specifically includes rock type and hardness, formation temperature, formation pressure, and formation permeability. Specifically, the type and hardness of the reservoir rocks determine the cutting force and penetration power required by the fracturing equipment; high temperatures affect equipment performance and material durability, necessitating the selection of suitable high-temperature resistant materials and cooling systems; understanding formation pressure helps determine the injection pressure and flow rate of fracturing fluid; low-permeability reservoirs require more effective fracture propagation to improve recovery. Oil and gas distribution information includes oil and gas saturation, reservoir thickness and continuity, and oil and gas migration patterns. Specifically, understanding the distribution and saturation of oil and gas in the reservoir can identify priority areas for fracturing; reservoir thickness and continuity affect the design length and distribution of fractures; understanding the migration patterns of oil and gas in the formation helps optimize the direction and location of fractures. Pre-set production enhancement demand information can include production enhancement targets and economic benefit analysis. Production enhancement targets clarify specific goals and plans for production enhancement, such as improving recovery rate and increasing production; economic benefit analysis considers the economic benefits of different production enhancement schemes, including investment costs, operating costs, and expected returns. Based on formation characteristics, oil and gas distribution information, and pre-set production enhancement requirements, the parameters of the electric fracturing equipment are analyzed to determine the initial fracturing parameters. Specifically, this includes determining the appropriate cutting speed based on rock hardness and formation temperature; determining the required fracture depth and width based on formation characteristics and oil and gas distribution; determining the optimal cutting direction based on oil and gas migration patterns and formation structure; and determining fracturing fluid parameters (such as injection pressure, flow rate, and type) based on formation pressure and permeability. Finally, the initial fracturing parameters are determined, including the number, location, length, width, and direction of the fracturing lines.

[0060] In some embodiments, determining the initial slotting parameters of the slotting equipment based on the formation characteristics information and oil and gas distribution information of the target reservoir, as well as preset production enhancement demand information, through a preset slotting parameter analysis model includes:

[0061] A slotting data space is generated based on preset data of the slotting equipment in fracturing operations, and the slotting data space is divided and identified to determine the input data and output data.

[0062] The input and output data are trained using a deep learning network structure to construct the preset kerf parameter analysis model;

[0063] The initial fracture parameters are determined by analyzing the formation characteristics, oil and gas distribution, and preset production increase demand information using the preset fracture parameter analysis model.

[0064] Optionally, a slotting data mining operation can be performed on the slotting equipment to obtain a slotting data space. Data mining primarily refers to extracting and organizing the data generated by the electric slotting equipment during fracturing operations (i.e., preset data, the specific data to be included can be determined according to actual needs) to form a structured and meaningful data set, i.e., the slotting data space. The slotting data space includes slotting data and various key parameter information from the historical operation of this model of electric slotting equipment, such as cutting speed, cutting depth, cutting direction, formation temperature, formation stress, and fracture propagation direction.

[0065] The input data includes formation characteristics of high-temperature reservoirs, oil and gas distribution information, and preset production enhancement requirements; the output data includes equipment slotting parameters.

[0066] The fracturing data within the fracturing data space is divided and labeled to obtain input data and output data. Specifically, the input data mainly includes formation characteristic information of high-temperature reservoirs, oil and gas distribution information, and preset production enhancement demand information. The output data is the equipment fracturing parameters obtained based on the input data through simulation models or data analysis. These are key parameters that guide the electric fracturing equipment in fracturing operations, including cutting speed, cutting depth, cutting direction, and cutting spacing, which directly affect the formation and propagation of fractures, and thus affect the fracturing effect and oil and gas recovery rate.

[0067] Optionally, a pre-defined kerf parameter analysis model is constructed based on a convolutional neural network (CNN), which determines multiple convolutional layers, pooling layers, and fully connected layers. The input and output data are analyzed and trained, that is, the input data is used as the input of the pre-defined kerf parameter analysis model, and the predicted value of the output data is calculated through forward propagation. This process is repeated until the pre-defined kerf parameter analysis model converges or reaches a preset number of training rounds, and finally the pre-defined kerf parameter analysis model is obtained.

[0068] Furthermore, formation characteristic information, oil and gas distribution information, and pre-set production enhancement demand information are used as input data and fed into the constructed pre-set fracturing parameter analysis model. Through its internal complex network structure and algorithm, the model performs parameter parsing and calculation. Then, based on the characteristics of the input data and combined with its learned formation response laws and fracturing effect prediction capabilities, the pre-set fracturing parameter analysis model outputs a set of initial fracturing parameters, which may include key parameters such as cutting speed, cutting depth, cutting direction, and cutting spacing. These parameters will directly affect the effectiveness and efficiency of fracturing operations.

[0069] Step 120: Perform simulation modeling based on the preset fracture fracturing dataset of the target reservoir, the formation characteristic information, and the oil and gas distribution information to construct the fracture simulation model of the target reservoir.

[0070] The pre-set fracture fracturing dataset includes seismic data, downhole logging data, core analysis data, and field fracturing experiment data related to high-temperature reservoirs. Specifically, seismic data provides information on the geological structure and fracture distribution of the reservoir; downhole logging data includes parameters such as rock physical properties, porosity, and permeability; core analysis data provides detailed physical and chemical properties of the rock; and field fracturing experiment data records parameters such as pressure, flow rate, and fracture morphology during the fracturing process.

[0071] In some embodiments, the step of performing simulation modeling based on a preset fracture fracturing dataset of the target reservoir, the formation characteristic information, and the hydrocarbon distribution information to construct a fracture simulation model of the target reservoir includes:

[0072] The formation characteristics and oil and gas distribution information are spatially modeled using three-dimensional modeling to construct a spatial model of the target reservoir.

[0073] Preprocess the preset fracture fracturing dataset of the target reservoir to obtain the standard fracture fracturing dataset of the target reservoir.

[0074] The spatial model is used as the constant simulation parameter for the fracturing, and the standard fracturing dataset for the fracturing is used as the momentum simulation parameter for the fracturing. Based on the constant simulation parameter and the momentum simulation parameter, the fracturing simulation is performed to construct the fracturing simulation model of the target reservoir.

[0075] Optionally, a spatial model of the target reservoir (which may also be referred to as a high-temperature reservoir spatial model in this embodiment) is constructed using 3D modeling technology. This model considers physical processes such as rock deformation, fluid seepage, and fracture propagation. Then, based on formation characteristics and oil and gas distribution information, initial and boundary conditions are set for the model. Specifically, a pre-set fracture fracturing dataset of the reservoir is used as input parameters for setting and verifying the simulation model's parameters. Based on the high-temperature reservoir spatial model, the operation of an electric fracturing device is simulated, including parameters such as cutting speed, cutting depth, and cutting direction. The effects of the high-temperature environment on rocks and fluids, such as thermal expansion of rocks and thermal conduction of fluids, are considered. The fracture propagation process is simulated, including parameters such as fracture length, width, and morphology. By comparing the simulation results with the actual fracturing effect, the model parameters are continuously adjusted to improve the model's accuracy, ultimately constructing a fracture simulation model of the target reservoir (which may also be referred to as a high-temperature reservoir fracture simulation model in this embodiment).

[0076] Using the high-temperature reservoir spatial model as the constant simulation parameter for fractures means that the static information such as the geological structure, rock type, and oil and gas distribution of the high-temperature reservoir is fixed during the simulation process and will not change with time or fracturing operations. Using the reservoir fracture standard fracturing dataset as the fracture momentum simulation parameter describes the dynamically changing parameters during fracturing, such as fracturing pressure, fracturing fluid flow rate, and fracturing time. These dynamic parameters will affect the propagation, shape, and connectivity of fractures, and thus affect the recovery rate of oil and gas.

[0077] The high-temperature reservoir spatial model (fracturing constant simulation parameters) is then used as the initial geological structure for simulation. Initial boundary conditions and initial states are set, such as the initial stress state and initial fracture state of the formation. Based on the reservoir fracturing standard dataset (fracturing momentum simulation parameters), dynamic parameters are set for the simulation, including fracturing pressure, fracturing fluid flow rate, and fracturing time. Specific parameters for fracturing operations are set according to the fracturing objectives (e.g., improving oil and gas recovery) and formation characteristics, such as the location of the fracturing well, the fracturing layer, and the fracturing method. Then, fracturing simulation is performed based on the set initial conditions and parameters, specifically simulating the expansion, shape, and connectivity of fractures during fracturing, as well as the impact of fractures on oil and gas flow. The simulation results will include the geometry of the fractures, the flow state of the fluid within the fractures, and the impact of fractures on formation stress and permeability. The simulation results are analyzed to evaluate the effectiveness and efficiency of the fracturing operation. Based on the simulation results, the high-temperature reservoir fracturing simulation model is optimized.

[0078] Step 130: Control the fracturing equipment to perform fracturing operation according to the initial fracturing parameters, and obtain key parameter information of the fracturing point through the sensor during the fracturing control process.

[0079] It should be noted that multiple sensors may be used to collect key parameters at the fracturing point. Specifically, the key parameters collected may include: formation temperature, formation stress, fracture propagation direction, and other key parameters; the corresponding sensors may be temperature sensors, pressure sensors, and strain sensors, respectively.

[0080] The electric fracturing equipment operates based on pre-collected formation characteristic information, oil and gas distribution information, and pre-set production enhancement demand information. After parameter analysis, initial fracturing parameters are determined to achieve precise fracturing and cracking. Sensor monitoring devices are installed on the electric fracturing equipment to acquire key parameter information during the fracturing process in real time. These may include temperature sensors, pressure sensors, strain sensors, etc., used to monitor key parameters such as formation temperature, formation stress, and fracture propagation direction. The installation location and number of sensor monitoring devices need to be determined based on specific fracturing operation requirements and equipment structure. By monitoring the fracturing control process through sensor monitoring devices, key parameter information at the fracturing points can be obtained. Key parameters of the fracturing point include formation temperature, formation stress, and fracture propagation direction. Specifically, temperature sensors monitor the formation temperature near the fracturing point in real time to ensure the normal operation of the equipment in a high-temperature environment and prevent equipment damage or safety risks caused by excessive temperature. Pressure or strain sensors monitor changes in formation stress around the fracturing point to understand the stress distribution and variation during fracture propagation, providing real-time feedback for fracturing operations. By integrating data from multiple sensors, combined with high-temperature reservoir space models and numerical simulation results, the fracture propagation direction can be inferred, which helps optimize fracturing schemes, improve fracture propagation effects, and enhance oil and gas recovery rates.

[0081] Step 140: Iteratively optimize the fracturing simulation model based on the key parameter information of the fracturing point to obtain the optimized fracturing simulation model.

[0082] Optionally, during the iterative optimization in this step, the high-temperature reservoir fracturing simulation model can be corrected and optimized based on the actual formation stress and crack propagation direction data. The simulation training can be repeated multiple times until a more accurate crack propagation prediction result is obtained, and finally the optimized fracturing simulation model, namely the high-temperature reservoir fracturing optimized simulation model, is obtained.

[0083] In some embodiments, the step of iteratively optimizing the fracturing simulation model based on the key parameter information of the fracturing point to obtain an optimized fracturing simulation model includes:

[0084] Based on the fracturing simulation model, the initial fracturing parameters are simulated to obtain the initial fracturing simulation parameters. The difference between the initial fracturing simulation parameters and the key parameter information of the fracturing point is used as the target optimization parameter variable.

[0085] Extract the parameters of the kerf simulation model to obtain the simulation model parameters;

[0086] The simulation model parameters are iteratively optimized based on the target optimization parameter variables to obtain optimized simulation model parameters. The kerf simulation model is then optimized and configured based on the optimized simulation model parameters to obtain the optimized kerf simulation model.

[0087] The initial fracturing simulation parameters are compared with the key parameters of the fracturing point item by item, and the differences between them are calculated. From the calculated differences, the key differences that have a significant impact on the fracturing effect are identified. These differences may be key factors affecting fracture morphology, fracture conductivity, fracturing fluid distribution, etc. The identified key differences are used as target optimization parameter variables, which will become the objects that need to be adjusted and optimized in the subsequent optimization process.

[0088] High-temperature reservoir fracture simulation model parameters refer to various numerical and physical quantities used to describe and simulate the characteristics of fracture morphology, fluid flow, heat transfer process, etc. in high-temperature reservoirs, such as reservoir temperature, pressure, rock type, fracture network characteristics (such as size, density, orientation, aperture, etc.), fluid properties, etc.

[0089] Simulations are performed using initial parameters, and the model's output results are observed, such as fracture propagation, fluid flow paths, and temperature distribution. The simulation results are compared with actual data or expected targets. If the simulation results do not meet the requirements, the simulation model parameters are adjusted based on the target optimization parameter variables. After adjusting the parameters, simulations are performed again, and the results are evaluated. Iterative optimization is carried out until the simulation results meet the preset requirements, resulting in a set of parameter values ​​that can improve the performance of the simulation model, i.e., simulation model optimization parameters. These may include adjusted fracture network configuration, fluid flow characteristic parameters, heat transfer efficiency parameters, etc. The simulation model optimization parameters are used to configure the high-temperature reservoir fracture simulation model, i.e., the optimized parameter values ​​are applied to the model to obtain the high-temperature reservoir fracture optimization simulation model.

[0090] Step 150: Perform fracturing simulation on the initial fracturing parameters according to the optimized fracturing simulation model to obtain fracturing simulation parameters, and determine multiple target fracturing points of the target reservoir according to the fracturing simulation parameters.

[0091] Initial fracturing parameters are input into the high-temperature reservoir fracturing optimization simulation model for fracturing simulation. This simulation simulates the formation, propagation, and connectivity of fractures during actual fracturing, as well as the flow and distribution of fracturing fluid within the fractures. Through simulation, fracturing simulation parameters such as fracture length, width, morphology, and distribution are obtained. Based on these parameters, combined with formation characteristics, oil and gas distribution, and production enhancement requirements, multiple target fracturing points are determined. The selection of target fracturing points should be based on maximizing the connectivity of the fracture network, improving oil and gas recovery, and enhancing economic benefits. The optimal combination of target fracturing points is determined through searches using genetic algorithms or particle swarm optimization algorithms, ultimately identifying multiple target fracturing points.

[0092] In some embodiments, the step of performing fracturing simulation on the initial fracturing parameters according to the optimized fracturing simulation model to obtain fracturing simulation parameters, and determining multiple target fracturing points of the target reservoir based on the fracturing simulation parameters, includes:

[0093] Stress concentration analysis and crack propagation analysis were performed on the simulated parameters of the slotted fracturing to obtain the corresponding set of stress concentration zones and set of crack propagation characteristics.

[0094] Based on the set of stress concentration zones and the set of crack propagation characteristics, fracturing points are identified to determine the set of potential slotted fracturing points.

[0095] Based on preset risk feasibility factors, risk assessment is performed on each potential slotted fracturing point in the set of potential slotted fracturing points in sequence to obtain a set of risk coefficients for slotted fracturing points.

[0096] Based on the set of risk coefficients for the slotted fracturing points, the plurality of target slotted fracturing points are determined from the set of potential slotted fracturing points using preset screening conditions.

[0097] The preset risk feasibility factors include: construction difficulty, technical feasibility, construction cost, and safety risks.

[0098] Optionally, the following techniques can be used to obtain the corresponding set of stress concentration zones and set of fracture propagation characteristics: During fracturing, stress concentration refers to the phenomenon of increased stress in local areas of rock due to discontinuities within the rock (such as natural fractures, bedding, mineral grain boundaries, etc.) or external forces (such as fracturing fluid pressure). Analyzing stress concentration in the fracturing simulation parameters involves analyzing the stress changes in the rock during fracturing, paying particular attention to stress concentration at weak surfaces such as natural fractures and bedding. These areas are usually the preferred paths for fracture propagation. Through simulation results, areas with significant stress concentration in the rock are identified, forming a set of stress concentration zones. For example, stress concentration is likely to occur at the intersection of fractures and the end of natural fractures, where the fracture width shows discontinuities. The larger the fracture intersection angle and the longer the natural fracture, the greater the stress concentration. The smaller the value, the more obvious the crack discontinuity phenomenon. Crack propagation refers to the process during hydraulic fracturing where the internal stress of the rock exceeds its tensile strength, causing the rock to break and propagate cracks in a certain direction. Analyzing crack propagation in fracturing simulation parameters involves analyzing the entire process of crack propagation from initiation to propagation under the pressure of fracturing fluid. Special attention is paid to the characteristics of crack propagation, such as direction, speed, and morphology, as well as the interaction between cracks and weak surfaces such as natural cracks and bedding. Through simulation results, the characteristics of crack propagation, such as propagation speed, propagation direction, and crack morphology, are obtained, forming a set of crack propagation characteristics. For example, by controlling the length, width, orientation, and shape of pre-fabricated cracks, the propagation law of cracks in the rock mass can be revealed. The development of natural cracks in coal and rock can cause multiple pressure fluctuations during the propagation of hydraulic cracks, and the crack extension path can be affected by micro-cracks and change direction.

[0099] Optionally, when determining the set of potential fractured fracturing points, the following technical means can be adopted: Analyze the set of stress concentration zones and the set of fracture propagation characteristics, and prioritize the selection of areas in the set of stress concentration zones as fracturing points, as these fracturing points are more likely to generate fracture propagation. Within the stress concentration zone, based on the analysis results of the set of fracture propagation characteristics, select areas whose fracture propagation direction, velocity, and other characteristics are conducive to oil and gas recovery and fracture connectivity as fracturing points, while avoiding the selection of areas with geological anomalies, unstable formations, or obstructed fracture propagation as fracturing points, thus determining the set of potential fractured fracturing points, that is, the set of fracturing points with greater fracture propagation potential and production enhancement effect.

[0100] Optionally, when obtaining the risk coefficient set for fractured fracturing points, the following technical means can be adopted: Construction difficulty is assessed by evaluating the impact of geological conditions, formation stability, rock hardness, and other factors at potential fractured fracturing points on construction difficulty; technical feasibility refers to analyzing whether existing technical capabilities and equipment can meet the needs of fracturing operations at potential fractured fracturing points, including considering the cutting-edge nature, applicability, and reliability of fracturing technology, and assessing technical feasibility; construction cost is calculated by determining the costs required for fracturing operations at potential fractured fracturing points, including equipment rental, material procurement, labor costs, etc.; safety risk... Risk assessment involves analyzing potential safety risks at fracturing points, such as equipment failure, personnel injury, and environmental pollution. This includes evaluating the probability and potential impact of safety risks, determining the risk level, and outlining corresponding countermeasures. For each potential fracturing point, a quantitative assessment is conducted based on factors such as construction difficulty, technical feasibility, construction cost, and safety risks. Taking all factors into account, methods such as weighted average or multi-factor assessment are used to calculate the risk coefficient for each potential fracturing point. These risk coefficients are then compiled and summarized to form a set of fracturing point risk coefficients.

[0101] Optionally, when determining multiple target fracturing points, the following technical means can be adopted: the set of potential fracturing points is screened according to the set screening criteria (i.e., preset screening conditions). For example, a threshold for risk coefficient is set, and only potential fracturing points with risk coefficients lower than the threshold will be considered. At the same time, the influence of factors such as construction cost and technical feasibility is considered. Specifically, potential fracturing points with risk coefficients exceeding the threshold are excluded. Among the remaining potential fracturing points, further screening is carried out based on factors such as construction cost and technical feasibility, and finally multiple target fracturing points are determined.

[0102] Step 160: Based on the fracturing simulation parameters and the multiple target fracturing points, simulate and calibrate the initial fracturing parameters to obtain fracturing correction parameters, and perform fracturing control on the fracturing equipment based on the fracturing correction parameters.

[0103] Optionally, the simulated fracturing parameters are compared and analyzed with the target fracturing point to evaluate the effect of the initial fracturing parameters in the actual fracturing process. If there is a discrepancy between the simulation results and the target effect, the initial fracturing parameters are simulated and calibrated using optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.). The goal is to find a new set of fracturing parameters (i.e., fracturing correction parameters) that can more accurately match the target fracturing point in the simulation model while meeting the production enhancement requirements. Specifically, after simulation calibration, the fracturing correction parameters are determined based on the output results of the simulation model. These parameters may include new cutting speeds, cutting depths, cutting directions, etc. The fracturing correction parameters are optimized parameters designed to improve the fracturing effect, ensure that the fracture can expand as expected, and match the target fracturing point. Then, the fracturing correction parameters are input into the control system of the electric fracturing equipment to control the fracturing operation. During the fracturing process, key parameter information such as formation temperature, formation stress, and fracture propagation direction are monitored in real time by sensor monitoring devices installed on the electric fracturing equipment. Finally, based on the monitored data, the fracturing operation is dynamically adjusted and optimized in real time to ensure that the fracturing process is carried out in accordance with the predetermined cut correction parameters and to achieve the expected fracturing effect.

[0104] In some embodiments, the step of simulating and calibrating the initial fracturing parameters to obtain fracturing correction parameters based on the fracturing simulation parameters and the plurality of target fracturing points includes:

[0105] Based on the optimized slotting simulation model, the slotting fracturing simulation parameters and the multiple target slotting fracturing points are simulated to obtain the slotting fracturing simulation results.

[0106] Permeability analysis was performed on the simulated fracture to obtain fracture permeability parameters;

[0107] Perform a production enhancement prediction operation on the fracture permeability parameters to obtain reservoir production enhancement prediction information;

[0108] Based on the comparison between the reservoir production enhancement prediction information and the preset production enhancement demand information, a target optimization production enhancement effect factor is determined. The initial slotting parameters are then calibrated and optimized based on the target optimization production enhancement effect factor to obtain the slotting correction parameters.

[0109] Optionally, when obtaining the simulation results of fracture fracturing, the following technical means can be adopted: input the fracture fracturing simulation parameters and the selected target fracture fracturing point into the high-temperature reservoir fracture optimization simulation model, start the simulation program, and the model will simulate the fracture propagation process in the reservoir, the fluid flow, and the temperature distribution in the reservoir according to the input parameters and conditions, and obtain the simulation results, i.e., the fracture fracturing simulation results, which may include the fracture propagation morphology, the complexity of the fracture network, the fluid flow path and velocity, the temperature distribution in the reservoir, etc.

[0110] Optionally, the following techniques can be used when performing permeability analysis: Permeability analysis is mainly used to evaluate and optimize the connectivity and permeability of fractures. Fracture connectivity describes the degree of connection between different fractures in a fracture network, determining the fluid flow capacity within the fracture network. Permeability refers to the ability of rock to allow fluid to pass through under a certain pressure difference; it is a parameter characterizing the ability of soil or rock itself to conduct liquids. Specifically, fracture images are marked with connected regions to identify different fracture connected regions. By calculating parameters such as the area, perimeter, length, and width of the circumscribed rectangle of each connected region, the connectivity of the fractures can be evaluated. For example, indicators such as rectangularity and circularity are used to measure fracture connectivity. The closer the rectangularity is to 1, the closer the shape of the fracture connected region is to a rectangle, and the better the connectivity; the smaller the circularity, the more complex the boundary of the fracture connected region, and the potentially poorer the fracture connectivity.

[0111] Optionally, when obtaining reservoir production enhancement prediction information, the following technical means can be adopted: combine fracture permeability parameters to construct a production enhancement prediction model, and then obtain reservoir production enhancement prediction information, which may include the expected production growth (predicting the production growth in the future period based on fracture permeability parameters and other relevant factors), the effective period of production enhancement (predicting the duration of the production enhancement effect), and the spatial distribution of production enhancement (predicting the spatial distribution of the production enhancement effect in the reservoir, i.e., which areas have greater production enhancement potential), etc.

[0112] In some embodiments, the step of calibrating and optimizing the initial slotting parameters according to the target optimized yield-increasing effect factor to obtain the slotting correction parameters includes:

[0113] Based on the stated objectives, optimize the production increase effect factors and formulate parameter strategy optimization rules;

[0114] The initial kerf parameters are optimized, expanded, and updated according to the parameter strategy optimization rules to obtain multiple updated kerf parameters.

[0115] The optimized kerf simulation model is used to simulate and optimize the multiple kerf update parameters to obtain the kerf correction parameters.

[0116] By comparing reservoir production enhancement prediction information with preset production enhancement demand information, the target production enhancement effect factors that need to be optimized are determined. Based on the target production enhancement effect factors, the initial fracture parameters are calibrated and optimized. During the optimization process, various factors such as reservoir geological conditions, rock properties, and fluid characteristics are comprehensively considered to ensure the rationality and effectiveness of the optimization results. After calibration and optimization, a new set of fracture parameters, namely the fracture correction parameters, is obtained, which can better meet the production enhancement demand compared with the initial parameters and is expected to improve fracture connectivity and permeability.

[0117] Optionally, when formulating parameter strategy optimization rules, the following technical means can be adopted: Based on the target optimization production enhancement effect factor, formulate specific parameter adjustment strategies and optimization rules. For example, determine which slot parameters (such as depth, width, spacing, angle, etc.) have a greater impact on the production enhancement effect and need to be adjusted in detail; set the optimization range and adjustment step size of each parameter according to the value or range of the production enhancement effect factor; and determine the upper and lower limits of the parameters and possible constraints, taking into account the limitations of reservoir geological conditions and engineering conditions.

[0118] Optionally, when performing optimization and expansion updates, the following technical means can be adopted: according to the established parameter strategy optimization rules, the initial kerf parameters are adjusted and expanded to generate multiple possible combinations of kerf update parameters, which may include different parameter values ​​such as depth, width, spacing and angle, thus obtaining multiple kerf update parameters.

[0119] Optionally, when performing simulation optimization, the following technical means can be adopted: use a high-temperature reservoir fracture optimization simulation model to simulate and test multiple fracture update parameters, evaluate the production increase effect under each parameter combination, simulate key indicators such as fracture propagation morphology, fracture network connectivity, and permeability, and calculate the corresponding production increase prediction value. The fracture parameter combination with the best production increase effect is taken as the final fracture correction parameter.

[0120] Example Two

[0121] Based on the above embodiments, this embodiment provides a fracturing control system for a slotting device. The system includes:

[0122] The initial slotting parameter determination module is used to determine the initial slotting parameters of the slotting equipment based on the formation characteristics information and oil and gas distribution information of the target reservoir, as well as the preset production enhancement demand information, through a preset slotting parameter analysis model.

[0123] The fracturing simulation model construction module is used to perform simulation modeling based on the preset fracturing dataset of the target reservoir, the formation characteristic information, and the oil and gas distribution information to construct the fracturing simulation model of the target reservoir.

[0124] The fracturing point key parameter information acquisition module is used to control the fracturing equipment to perform fracturing operation according to the initial fracturing parameters, and to acquire fracturing point key parameter information through the sensor during the fracturing control process;

[0125] The slotting simulation model optimization module is used to iteratively optimize the slotting simulation model based on the key parameter information of the fracturing point to obtain the optimized slotting simulation model.

[0126] The target fracturing point determination module is used to perform fracturing simulation on the initial fracturing parameters according to the optimized fracturing simulation model, obtain the fracturing simulation parameters, and determine multiple target fracturing points of the target reservoir according to the fracturing simulation parameters.

[0127] The control module is used to simulate and calibrate the initial kerf parameters to obtain kerf correction parameters based on the kerf fracturing simulation parameters and the plurality of target kerf fracturing points, and to perform fracturing control on the kerf equipment based on the kerf correction parameters.

[0128] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computing devices, either centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. Furthermore, in some cases, the steps shown or described can be performed in a different order than presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module.

[0129] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of each module in the fracturing control system of the cutting equipment can be referred to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0130] Example Three

[0131] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0132] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0133] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0134] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.

[0135] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0136] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0137] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0138] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0139] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0140] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0141] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0142] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A fracturing control method for a slotting device, characterized in that, The fracturing equipment is equipped with sensors for collecting key parameter information at the fracturing point, and the method includes: Based on the formation characteristics and oil and gas distribution information of the target reservoir, as well as the preset production increase demand information, the initial cutting parameters of the cutting equipment are determined by the preset cutting parameter analysis model. Simulation modeling is performed based on the preset fracture fracturing dataset of the target reservoir, the formation characteristic information, and the oil and gas distribution information to construct the fracture simulation model of the target reservoir. The fracturing equipment is controlled to perform fracturing operations based on the initial fracturing parameters, and key parameter information of the fracturing point is acquired through the sensor during the fracturing control process. The fracturing point key parameter information is used to iteratively optimize the fracturing simulation model to obtain the optimized fracturing simulation model. Based on the optimized fracturing simulation model, the initial fracturing parameters are subjected to fracturing simulation to obtain fracturing simulation parameters, and multiple target fracturing points of the target reservoir are determined based on the fracturing simulation parameters. Based on the fracturing simulation parameters and the multiple target fracturing points, the initial fracturing parameters are simulated and calibrated to obtain fracturing correction parameters, and the fracturing control of the fracturing equipment is performed based on the fracturing correction parameters.

2. The method according to claim 1, characterized in that, The process of determining the initial slotting parameters of the slotting equipment based on the formation characteristics and oil and gas distribution information of the target reservoir, as well as the preset production enhancement demand information, through a preset slotting parameter analysis model includes: A slotting data space is generated based on preset data of the slotting equipment in fracturing operations, and the slotting data space is divided and identified to determine the input data and output data. The input and output data are trained using a deep learning network structure to construct the preset kerf parameter analysis model; The initial fracture parameters are determined by analyzing the formation characteristics, oil and gas distribution, and preset production increase demand information using the preset fracture parameter analysis model.

3. The method according to claim 1, characterized in that, The step of performing simulation modeling based on the preset fracture fracturing dataset of the target reservoir, the formation characteristic information, and the oil and gas distribution information to construct a fracture simulation model of the target reservoir includes: The formation characteristics and oil and gas distribution information are spatially modeled using three-dimensional modeling to construct a spatial model of the target reservoir. Preprocess the preset fracture fracturing dataset of the target reservoir to obtain the standard fracture fracturing dataset of the target reservoir. The spatial model is used as the constant simulation parameter for the fracturing, and the standard fracturing dataset for the fracturing is used as the momentum simulation parameter for the fracturing. Based on the constant simulation parameter and the momentum simulation parameter, the fracturing simulation is performed to construct the fracturing simulation model of the target reservoir.

4. The method according to claim 1, characterized in that, The step of iteratively optimizing the fracturing simulation model based on the key parameter information of the fracturing point to obtain an optimized fracturing simulation model includes: Based on the fracturing simulation model, the initial fracturing parameters are simulated to obtain the initial fracturing simulation parameters. The difference between the initial fracturing simulation parameters and the key parameter information of the fracturing point is used as the target optimization parameter variable. Extract the parameters of the kerf simulation model to obtain the simulation model parameters; The simulation model parameters are iteratively optimized based on the target optimization parameter variables to obtain optimized simulation model parameters. The kerf simulation model is then optimized and configured based on the optimized simulation model parameters to obtain the optimized kerf simulation model.

5. The method according to claim 1, characterized in that, The step of performing fracturing simulation on the initial fracturing parameters based on the optimized fracturing simulation model to obtain fracturing simulation parameters, and determining multiple target fracturing points of the target reservoir based on the fracturing simulation parameters, includes: Stress concentration analysis and crack propagation analysis were performed on the simulated parameters of the slotted fracturing to obtain the corresponding set of stress concentration zones and set of crack propagation characteristics. Based on the set of stress concentration zones and the set of crack propagation characteristics, fracturing points are identified to determine the set of potential slotted fracturing points. Based on preset risk feasibility factors, risk assessment is performed on each potential slotted fracturing point in the set of potential slotted fracturing points in sequence to obtain a set of risk coefficients for slotted fracturing points. Based on the set of risk coefficients for the slotted fracturing points, the plurality of target slotted fracturing points are determined from the set of potential slotted fracturing points using preset screening conditions.

6. The method according to claim 1, characterized in that, The step of calibrating the initial fracturing parameters based on the fracturing simulation parameters and the multiple target fracturing points to obtain fracturing correction parameters includes: Based on the optimized slotting simulation model, the slotting fracturing simulation parameters and the multiple target slotting fracturing points are simulated to obtain the slotting fracturing simulation results. Permeability analysis was performed on the simulated fracture to obtain fracture permeability parameters; Perform a production enhancement prediction operation on the fracture permeability parameters to obtain reservoir production enhancement prediction information; Based on the comparison between the reservoir production enhancement prediction information and the preset production enhancement demand information, a target optimization production enhancement effect factor is determined. The initial slotting parameters are then calibrated and optimized based on the target optimization production enhancement effect factor to obtain the slotting correction parameters.

7. The method according to claim 6, characterized in that, The step of calibrating and optimizing the initial slotting parameters based on the target optimized yield-increasing effect factor to obtain the slotting correction parameters includes: Based on the stated objectives, optimize the production increase effect factors and formulate parameter strategy optimization rules; The initial kerf parameters are optimized, expanded, and updated according to the parameter strategy optimization rules to obtain multiple updated kerf parameters. The optimized kerf simulation model is used to simulate and optimize the multiple kerf update parameters to obtain the kerf correction parameters.

8. A fracturing control system for a slotting device, characterized in that, The fracturing equipment is equipped with sensors for collecting key parameter information at the fracturing point, and the system includes: The initial slotting parameter determination module is used to determine the initial slotting parameters of the slotting equipment based on the formation characteristics information and oil and gas distribution information of the target reservoir, as well as the preset production enhancement demand information, through a preset slotting parameter analysis model. The fracturing simulation model construction module is used to perform simulation modeling based on the preset fracturing dataset of the target reservoir, the formation characteristic information, and the oil and gas distribution information, so as to construct the fracturing simulation model of the target reservoir. The fracturing point key parameter information acquisition module is used to control the fracturing equipment to perform fracturing operation according to the initial fracturing parameters, and to acquire fracturing point key parameter information through the sensor during the fracturing control process; The slotting simulation model optimization module is used to iteratively optimize the slotting simulation model based on the key parameter information of the fracturing point to obtain the optimized slotting simulation model. The target fracturing point determination module is used to perform fracturing simulation on the initial fracturing parameters according to the optimized fracturing simulation model, obtain the fracturing simulation parameters, and determine multiple target fracturing points of the target reservoir according to the fracturing simulation parameters. The control module is used to simulate and calibrate the initial kerf parameters to obtain kerf correction parameters based on the kerf fracturing simulation parameters and the plurality of target kerf fracturing points, and to perform fracturing control on the kerf equipment based on the kerf correction parameters.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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