Blending control method, device and equipment for dry powder fracturing fluid of oil field and medium

Abstract

The invention provides a mixing control method, device and equipment for oil field dry powder fracturing fluid and a medium. The method comprises the steps that mixing process data are collected in real time; establishing a virtual model based on the internal structure of the target equipment; performing dynamic simulation on fluid flow in the liquid preparation tank by using fluid dynamics, and constructing a real-time fluid motion model; based on a chemical kinetics principle, establishing a chemical kinetics model of dry powder dissolution, cross-linking reaction and gel breaking reaction, and fusing material characteristic parameters into the chemical kinetics model; inputting data of the mixing process into the virtual model, performing dynamic calibration on the virtual model through a real-time fluid motion model and a chemical kinetic model, dynamically adjusting water quality data, a material ratio and stirring conditions, and performing multi-scene real-time simulation analysis on the mixing process; and executing corresponding dry powder fracturing fluid blending control on the target equipment according to the blending simulation data. The fracturing fluid mixing quality and efficiency can be improved, and the development efficiency of unconventional oil and gas reservoirs can be improved in an auxiliary mode.

Description

Technical Field

[0001] This application belongs to the field of data processing, and particularly relates to a mixing control method, device, equipment and medium for oilfield dry powder fracturing fluid. Background Technique

[0002] Oilfield dry powder fracturing fluid is a special liquid system used in the fracturing operation of oil and gas exploitation. It exists in the form of dry powder solids, has the characteristics of being convenient for storage and transportation, and convenient on-site liquid preparation, and is widely used in the development of unconventional oil and gas reservoirs (such as shale gas and tight reservoirs). For example, the fracturing fluid is pumped into the formation at high pressure to break the rock and form fractures. The cross-linked high-viscosity fracturing fluid carries the proppant into the fractures to ensure that the fractures remain open after the pump stops.

[0003] Currently, in the related technology, during the mixing process of oilfield dry powder fracturing fluid, the high molecular polymers (such as guar gum and polyacrylamide) in the dry powder are highly hygroscopic and prone to moisture absorption and caking during storage. If the stirring speed is insufficient or the quality of the liquid preparation water is poor (high salinity, containing impurities) during mixing, it will cause the surface of the dry powder particles to quickly swell to form "micelles", and the inside cannot be fully dissolved. The undissolved micelles will reduce the viscosity and sand-carrying capacity of the fracturing fluid, and even block the pumping pipeline, affecting construction safety. In addition, in the related technology, on-site mixing is usually carried out in a mobile liquid preparation tank. Limited by the uneven flow field distribution in the tank (such as the existence of stirring dead corners), different components (thickener, cross-linking agent, additive) may be stratified or have a locally high concentration. In the locally over-concentrated area, cross-linking is prone to occur in advance to form a gel, which destroys the performance stability of the fracturing fluid. The uneven concentration will lead to inconsistent gel-breaking times subsequently, increasing the difficulty of flowback.

[0004] Therefore, in order to solve at least one of the above technical problems, there is an urgent need to propose a brand-new mixing control scheme for oilfield dry powder fracturing fluid. Summary of the Invention

[0005] This application provides a mixing control method, device, equipment and medium for oilfield dry powder fracturing fluid, so as to improve the quality and efficiency of fracturing fluid mixing, enhance the adaptability to complex production environments, and assist in improving the development efficiency of unconventional oil and gas reservoirs.

[0006] In the first aspect, this application provides a mixing control method for oilfield dry powder fracturing fluid, and the method includes: Deploy multi-source sensors in the target equipment to collect mixing process data in real time; wherein, the target equipment at least includes: a liquid preparation tank, a stirrer, and a feeding device; the mixing process data at least includes: stirring speed, pressure in the liquid preparation tank, fluid temperature, dry powder feeding amount, and liquid preparation water flow rate; Based on the internal structure of the target device, a virtual model corresponding to the target device is established; wherein, a liquid preparation tank, a stirrer, a feeding device, and the pipeline layout between each virtual device are set in the virtual model; the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout; Based on the Navier-Stokes equation and the continuity equation, fluid dynamics is used to perform a dynamic simulation of the fluid flow in the liquid preparation tank, and the flow field distribution, the eddy current region, and the stirring dead angle are analyzed to construct a real-time fluid motion model corresponding to the target device; Based on the principle of chemical kinetics, a chemical kinetics model for dry powder dissolution, cross-linking reaction, and gel-breaking reaction is established, and the material characteristic parameters are incorporated into the chemical kinetics model; the chemical kinetics model is used to simulate the reaction process of the target device under different conditions; The mixing process data is input into the virtual model, and the virtual model is dynamically calibrated through the real-time fluid motion model and the chemical kinetics model. The water quality data, the material ratio, and the stirring conditions are dynamically adjusted, and the mixing process is simulated and analyzed in multiple scenarios in real time to obtain the mixing simulation data of the target device; the corresponding dry powder fracturing fluid mixing control is performed on the target device according to the mixing simulation data.

[0007] In a second aspect, an embodiment of the present application provides a mixing control device for oilfield dry powder fracturing fluid, and the device includes: An acquisition unit, configured to deploy multi-source sensors in the target device to acquire mixing process data in real time; wherein, the target device at least includes: a liquid preparation tank, a stirrer, and a feeding device; the mixing process data at least includes: stirring speed, pressure in the liquid preparation tank, fluid temperature, dry powder feeding amount, and mixing water flow rate; A first construction unit, configured to establish a virtual model corresponding to the target device based on the internal structure of the target device; wherein, a liquid preparation tank, a stirrer, a feeding device, and the pipeline layout between each virtual device are set in the virtual model; the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout; A second construction unit, configured to perform a dynamic simulation of the fluid flow in the liquid preparation tank by using fluid dynamics based on the Navier-Stokes equation and the continuity equation, and analyze the flow field distribution, the eddy current region, and the stirring dead angle to construct a real-time fluid motion model corresponding to the target device; A third construction unit, configured to establish a chemical kinetics model for dry powder dissolution, cross-linking reaction, and gel-breaking reaction based on the principle of chemical kinetics, and incorporate the material characteristic parameters into the chemical kinetics model; the chemical kinetics model is used to simulate the reaction process of the target device under different conditions; A control unit, configured to input the mixing process data into a virtual model, dynamically calibrate the virtual model through a real-time fluid motion model and a chemical kinetics model, dynamically adjust water quality data, material ratio, and stirring conditions, perform multi-scenario real-time simulation analysis on the mixing process to obtain mixing simulation data of a target device; and execute corresponding dry-fracturing fluid mixing control on the target device according to the mixing simulation data.

[0008] In a third aspect, an embodiment of the present application provides a computing device, which includes: At least one processor, a memory, and an input / output unit; Wherein, the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the mixing control method of the oilfield dry-fracturing fluid in the first aspect.

[0009] In a fourth aspect, a computer-readable storage medium is provided, which includes instructions that, when the instructions are run on a computer, cause the computer to execute the mixing control method of the oilfield dry-fracturing fluid in the first aspect.

[0010] In the technical solution provided by the embodiment of the present application, first, multi-source sensors are deployed in the target device to collect the mixing process data in real time; wherein, the target device at least includes: a liquid mixing tank, a stirrer, and a feeding device; the mixing process data at least includes: stirring speed, pressure in the liquid mixing tank, fluid temperature, dry powder feeding amount, and liquid mixing water flow rate. Next, based on the internal structure of the target device, a virtual model corresponding to the target device is established; wherein, the virtual model is provided with a liquid mixing tank, a stirrer, a feeding device, and the pipeline layout between each virtual device; the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout. Furthermore, based on the Navier-Stokes equation and the continuity equation, fluid dynamics is used to perform dynamic simulation on the fluid flow in the liquid mixing tank, and the flow field distribution, eddy current area, and stirring dead angle are analyzed to construct a real-time fluid motion model corresponding to the target device. Then, based on the principle of chemical kinetics, a chemical kinetics model for dry powder dissolution, cross-linking reaction, and gel-breaking reaction is established, and the material characteristic parameters are incorporated into the chemical kinetics model; the chemical kinetics model is used to simulate the reaction process of the target device under different conditions. Finally, the mixing process data is input into the virtual model, and the virtual model is dynamically calibrated through the real-time fluid motion model and the chemical kinetics model, and the water quality data, material ratio, and stirring conditions are dynamically adjusted to perform multi-scenario real-time simulation analysis on the mixing process to obtain the mixing simulation data of the target device. Thus, corresponding dry powder fracturing fluid mixing control is performed on the target device according to the mixing simulation data. In summary, the mixing control method of the oilfield dry powder fracturing fluid has many advantages, which helps to improve the accuracy, efficiency, and quality of the fracturing fluid mixing, enhance the adaptability to complex production environments, and promote the intelligent development of production management, and has positive and important significance for oilfield exploitation operations. Description of the Drawings

[0011] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation to the present application. In the drawings: Figure 1 It is a flowchart of a method for controlling the mixing of oilfield dry powder fracturing fluid according to an embodiment of the present application; Figure 2 It is a structural schematic diagram of a device for controlling the mixing of oilfield dry powder fracturing fluid according to an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to an embodiment of the present application. Detailed Embodiments

[0012] To solve at least one technical problem in the related art, it is urgent to propose a brand-new mixing control solution for oilfield dry powder fracturing fluid.

[0013] To solve at least one of the above technical problems, an embodiment of the present application provides a mixing control method for oilfield dry fracturing fluid.

[0014] Specifically, first, deploy multi-source sensors in the target equipment to collect mixing process data in real time; wherein, the target equipment at least includes: a liquid mixing tank, a stirrer, and a feeding device; the mixing process data at least includes: stirring speed, pressure in the liquid mixing tank, fluid temperature, dry powder feeding amount, and liquid mixing water flow rate. Next, based on the internal structure of the target equipment, establish a virtual model corresponding to the target equipment; wherein, the virtual model is provided with a liquid mixing tank, a stirrer, a feeding device, and the pipeline layout between each virtual device; the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout. Furthermore, based on the Navier-Stokes equation and the continuity equation, use fluid dynamics to perform a dynamic simulation of the fluid flow in the liquid mixing tank, and analyze the flow field distribution, vortex region, and stirring dead angle, and construct a real-time fluid motion model corresponding to the target equipment. Then, based on the principle of chemical kinetics, establish a chemical kinetics model for dry powder dissolution, crosslinking reaction, and gel-breaking reaction, and incorporate the material characteristic parameters into the chemical kinetics model; the chemical kinetics model is used to simulate the reaction process of the target equipment under different conditions. Finally, input the mixing process data into the virtual model, and dynamically calibrate the virtual model through the real-time fluid motion model and the chemical kinetics model, dynamically adjust the water quality data, material ratio, and stirring conditions, and perform real-time simulation analysis of multiple scenarios for the mixing process to obtain the mixing simulation data of the target equipment. Thus, perform corresponding dry fracturing fluid mixing control on the target equipment according to the mixing simulation data.

[0015] In the technical solution of the present application, by deploying multi-source sensors in key equipment such as liquid mixing tanks, stirrers, and feeding devices, full-dimensional data monitoring of the mixing process can be realized, and core data such as stirring speed, pressure, and temperature can be obtained in real time, providing an accurate and timely data basis for subsequent analysis, and effectively avoiding decision-making mistakes caused by data loss or lag. At the same time, these data can help technicians monitor the operation status of the equipment and the fluid change situation in real time, discover sudden problems such as pipeline blockage and abnormal stirring in time, and provide strong data support for fault diagnosis and early warning, ensuring the safe and stable operation of the mixing process.

[0016] Using 3D modeling technology to construct a virtual model that is highly consistent with the actual equipment, accurately restoring details such as the shape of the tank body, the structure of the stirring paddle, and the pipeline layout, providing a real and reliable digital carrier for subsequent fluid dynamics simulation and process optimization. The virtual model can support diverse simulation analyses under different working conditions and parameter settings, helping technicians evaluate in advance the impacts of different design schemes or process adjustments on the mixing process, thereby optimizing the equipment design and mixing process, avoiding the trial-and-error costs in actual operations, and improving the scientificity and efficiency of design and decision-making. In addition, the virtual model is convenient for integration with other professional software and systems, enabling multi-disciplinary data interaction and collaborative analysis, and enhancing the comprehensiveness and practicality of the entire mixing control method.

[0017] With the help of computational fluid dynamics algorithms, based on the Navier-Stokes equation and the continuity equation, in-depth simulation analysis of the fluid flow in the liquid mixing tank is carried out, which can accurately present key information such as the flow field distribution, eddy current region, and stirring dead zone. This helps technicians thoroughly understand the movement law of the fluid in the liquid mixing tank, thereby optimizing the agitator design and adjusting the stirring parameters targeted, effectively improving the uniformity of dry powder dissolution and the stirring efficiency, and ensuring the mixing quality of the fracturing fluid. Further, through the particle image velocimetry (PIV) algorithm, experimental verification of the simulation results is carried out, ensuring the accuracy and reliability of the simulation results, enhancing the credibility of the model, and providing a solid technical guarantee for practical applications. The real-time fluid motion model can dynamically simulate the changes in the fluid under different stirring conditions, predict the fluid behavior, and provide timely and effective support for real-time adjustment of the stirring strategy, enabling the mixing process to better adapt to the requirements of different working conditions.

[0018] Combining regression analysis algorithms and artificial neural network algorithms to establish a chemical kinetics model can fully consider the impacts of various complex factors such as water quality, temperature, and material properties on dry powder dissolution, crosslinking reaction, and gel-breaking reaction, and achieve high-precision prediction of the chemical reaction process. This provides a scientific theoretical basis for determining the optimal reaction conditions and optimizing the formulation design, helping to improve the performance and quality stability of the fracturing fluid. Through in-depth research on the chemical reaction law, the model can guide technicians to reasonably adjust the material ratio and reaction conditions according to different material characteristic parameters, effectively reducing quality problems caused by improper control of chemical reactions, reducing production risks, and improving the reliability and consistency of production. The chemical kinetics model can also predict in advance possible abnormal situations during the reaction process, such as premature crosslinking, incomplete gel-breaking, etc., and issue warnings in a timely manner, while providing corresponding countermeasures to help technicians take preventive measures to ensure the smooth progress of the mixing process.

[0019] By inputting the data of the mixing process collected in real time into the virtual model and dynamically calibrating it using the real-time fluid motion model and chemical kinetics model, the virtual model can reflect the actual operating state of the physical entity in real time, ensuring the timeliness and accuracy of the simulation results and providing a reliable basis for subsequent decision-making. By comprehensively applying various optimization algorithms such as genetic algorithms and response surface methods, multiple factors such as water quality data, material ratio, and stirring conditions are synergistically optimized. Through real-time simulation analysis of multiple scenarios, the interaction and influence between various factors can be comprehensively considered, the optimal parameter combination can be found, the quality and efficiency of the fracturing fluid mixing can be significantly improved, and the overall production efficiency can be enhanced. Simulating and evaluating the mixing schemes under different parameter combinations in the virtual environment can predict the implementation effects of various schemes in advance, avoid a large number of tests and adjustments in actual production, save time and costs, and at the same time provide strong technical support for formulating a scientific and reasonable mixing process.

[0020] By deeply analyzing the mixing simulation data, extracting key performance indicators and comparing them with standard parameters, and generating precise control instructions according to the optimization strategy, precise adjustment of equipment parameters such as agitator speed and feeding speed can be achieved, ensuring that the quality of the fracturing fluid mixing strictly meets the requirements, thereby improving the effect and success rate of the fracturing operation and guaranteeing the efficiency and output of oil and gas exploitation. Combining the prediction ability of the artificial neural network for abnormal working conditions, potential problems and risks in the mixing process can be sensed in advance, and the control strategy can be adjusted in a timely manner to achieve a rapid response and handling of abnormal situations, effectively avoiding the expansion of problems, ensuring the stability and safety of the mixing process, and reducing the probability of production accidents. By implementing scientific and precise mixing control, production interruptions and product unqualified phenomena caused by mixing problems can be significantly reduced, production efficiency can be improved, production costs can be reduced, and the efficiency of oilfield exploitation can be enhanced.

[0021] In summary, the mixing control method of the dry powder fracturing fluid in this oilfield helps to improve the quality and efficiency of the fracturing fluid mixing, enhance the adaptability to complex production environments, and assist in improving the development efficiency of unconventional oil and gas reservoirs through various advantages.

[0022] The mixing control scheme of the dry powder fracturing fluid in the oilfield provided by the embodiments of this application can be executed by an electronic device, which can be a server, a server cluster, or a cloud server. The electronic device can also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a dedicated device (such as a dedicated terminal device with a mixing control system for dry powder fracturing fluid in the oilfield, etc.). In an optional embodiment, a service program for executing the mixing control scheme of the dry powder fracturing fluid in the oilfield can be installed on the electronic device.

[0023] Figure 1 Schematic diagram of a mixing control method for dry powder fracturing fluid in an oilfield provided by the embodiments of this application, asFigure 1 As shown, the method includes the following steps: 101. Deploy multi-source sensors in the target device to collect mixing process data in real time.

[0024] In the embodiments of the present application, the target device at least includes: a liquid mixing tank, a stirrer, and a feeding device.

[0025] The liquid mixing tank serves as a mixing container for dry powder fracturing fluid and undertakes the spatial carrier for dry powder dissolution, fluid stirring, and chemical reactions. The liquid mixing tank is used to provide a stable mixing space, and its shape, volume, and internal structure directly affect the fluid flow state and mixing efficiency.

[0026] The stirrer promotes fluid movement through mechanical stirring to achieve dry powder dispersion, accelerated dissolution, and uniform mixing. It is suitable for low-viscosity fluids, has a simple structure, and is often used for preliminary dry powder dispersion. The turbine stirrer generates strong shear force and is suitable for high-viscosity fluids, which can break "micelles" and promote cross-linking reactions. The ribbon stirrer is used for scraping the bottom and side walls of the tank to reduce dead corners and improve overall uniformity. The stirring speed and paddle type directly affect the flow field distribution (such as eddy current, laminar flow), and it is the core device to avoid dry powder caking and improve dissolution efficiency.

[0027] The feeding device precisely controls the quantitative transportation of dry powder (such as thickener, cross-linking agent, etc.) and mixing water. The dry powder feeding system uses screw conveyors, vibrating feeders, etc. to avoid dry powder moisture absorption and caking and ensure uniform feeding. The mixing water control system controls the water flow rate through valves and flow meters to match the dry powder feeding amount (such as liquid-solid ratio control). It realizes precise control of the material ratio and avoids premature cross-linking or abnormal gel breaking caused by uneven feeding.

[0028] Further optionally, the mixing process data at least includes: stirring speed, pressure in the liquid mixing tank, fluid temperature, dry powder feeding amount, and mixing water flow rate.

[0029] Exemplarily, assume that the multi-source sensors at least include: a pressure sensor, a temperature sensor, a flow sensor, and a liquid level sensor. Based on this, in 101, a pressure sensor and a liquid level sensor are deployed on the liquid mixing tank to collect the pressure and liquid level data in the liquid mixing tank in real time; a rotational speed sensor is installed on the stirrer to obtain the stirring speed data; flow sensors are respectively set on the dry powder conveying pipeline and the mixing water conveying pipeline of the feeding device to monitor the dry powder feeding amount and the mixing water flow rate in real time; a temperature sensor is arranged in the fluid area of the liquid mixing tank to collect the fluid temperature data, and various data collected are transmitted to the data processing terminal in real time through the Internet of Things communication module.

[0030] Specifically, deploying multi-source sensors such as pressure, temperature, flow rate, and liquid level in the dry fracturing fluid mixing scenario in oil fields and achieving real-time data transmission can significantly improve the safety, efficiency, and controllability of the mixing process through all-dimensional data acquisition and intelligent analysis. Specifically, the pressure sensor and liquid level sensor on the liquid mixing tank work together, which can not only monitor the static pressure and volume change of the fluid in the tank in real time, but also indirectly evaluate the dry powder dissolution efficiency through density calculation, avoiding insufficient viscosity or pipeline blockage caused by insufficient dissolution; the agitator speed sensor is directly related to the stirring intensity and fluid shear force, providing key parameters for optimizing the stirring strategy. For example, when dealing with high-viscosity fluids, the speed can be automatically increased to maintain the turbulent state to ensure uniform dispersion of the dry powder; the dual flow sensors of the feeding device achieve dynamic monitoring of the liquid-solid ratio. When the flow rate of the mixing water is stable while the dry powder feeding amount fluctuates, the system can automatically adjust the rotation speed of the screw conveyor, controlling the liquid-solid ratio error within ±2%. Compared with manual adjustment, the error is reduced by 6%, and 3-5% of dry powder materials can be saved for single-well operations (such as saving 0.6-1 ton when consuming 20 tons of guar gum per well), significantly reducing material losses.

[0031] At the data transmission level, the Internet of Things communication module supports millisecond-level data transmission. Compared with the 15-30 minute interval of traditional manual inspections, it can capture instant anomalies such as blockages in the feeding pipeline within 5 seconds and trigger early warnings. For example, when the pressure in the liquid mixing tank suddenly rises (>0.5 MPa) and the dry powder flow rate suddenly drops (<50% of the set value), the system will automatically stop feeding, start the agitator to rotate at high speed in the reverse direction to impact the blockage point, and issue an audible and visual alarm, shortening the abnormal response time from dozens of minutes of manual handling to seconds of automatic control, significantly reducing the risk of equipment damage. At the same time, the "mixing log" formed by the full-process data record can trace back to the details of each feeding, stirring, and crosslinking. When there are problems with the batch quality, the faulty period can be quickly located through historical data such as temperature fluctuations. For example, incomplete crosslinking occurred due to the temperature exceeding the set range during a certain period, providing accurate basis for process improvement.

[0032] In terms of intelligent control, the sensor data is combined with PID regulation and fuzzy control algorithms to achieve dynamic optimization of mixing parameters. Taking temperature control as an example, when the measured temperature is lower than the target value (such as 25°C), the system automatically adjusts the heating power according to the temperature difference ratio (full power operation at a temperature difference of 5°C, 30% power maintenance at a temperature difference of 2°C), avoiding temperature overshoot or energy waste caused by traditional constant heating; when approaching the temperature upper limit (such as 45°C), the air cooling is preferentially started instead of directly cutting off the power, improving the temperature control accuracy to ±1°C and ensuring that the cross-linking reaction proceeds within the designed range. In addition, through the high-frequency fluctuation analysis of the pressure sensor (such as FFT spectrum), mechanical failures such as wear of the agitator bearing or loosening of the tank baffle can be identified in advance, and the replacement cycle of the screw of the screw conveyor can be predicted by combining the cumulative data of the flow sensor (such as checking for wear every 500 tons of dry powder transported), reducing the unplanned downtime of the equipment by more than 40% and reducing the maintenance cost.

[0033] At the level of process collaboration, the sensor data is deeply linked with the digital twin system, and real-time parameters such as the liquid level of the mixing tank and the rotation speed of the agitator are synchronized to the 3D virtual model. Operators can remotely inspect through VR devices, intuitively view the fluid flow state and the operation details of the equipment, and improve the monitoring efficiency. The integration with the fracturing construction scheduling system realizes the dynamic matching of the mixing progress and the pumping speed. When the volume of the mixing tank is lower than the safe value, the pump group is automatically notified to reduce the speed, avoiding the risk of "liquid break", and at the same time optimizing the process connection through the prediction of the mixing completion time. The single-well operation cycle can be shortened by 10 - 15%. The data-driven mixing mode also reduces the waste slurry rate from 10% in the traditional process to less than 5%. Combined with precise proportioning and energy consumption optimization (such as 15% reduction in energy consumption by two-stage stirring), the overall production cost is reduced by about 8 - 12%, providing technical support for the efficient development of unconventional oil and gas reservoirs.

[0034] 102. Based on the internal structure of the target device, a virtual model corresponding to the target device is established.

[0035] In the embodiment of the present application, a liquid mixing tank, an agitator, a feeding device, and the pipeline layout between each virtual device are set in the virtual model. Further optionally, the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout.

[0036] Exemplarily, in 102, according to the actual size parameters of the target device, three-dimensional geometric models of the liquid mixing tank, the agitator, and the feeding device are constructed; during the modeling process, internal structure details such as the shape of the tank body, the structure of the stirring paddle, and the pipeline layout are generated in the three-dimensional geometric model; through the model mapping technology, the three-dimensional geometric model with added internal structure details is imported into the digital twin platform to form a virtual model corresponding one-to-one to the target device.

[0037] Specifically, the principle of establishing a virtual model based on the internal structure of the target device lies in replicating the geometric features and spatial relationships of physical entities through digital means. Specifically, the actual size parameters such as the tank shape of the liquid dispensing tank (such as a cylindrical bottom cone structure), the agitator paddle structure (such as the size and angle of a propeller or anchor paddle), and the pipeline layout (such as the pipe diameter, bending angle of the dry powder conveying pipeline, and the interface position with the tank) are converted into accurate three-dimensional geometric models through three-dimensional modeling technology, and the flow path direction and structural details related to the hydrodynamic characteristics of the stirring area (such as the baffle position and draft tube size) are completely presented in the model. This process imports the three-dimensional model containing internal structure details into the digital twin platform through model mapping technology, making the virtual model completely aligned with the target device in terms of geometric shape, spatial layout, and key structural features, forming a digital mirror of the physical entity.

[0038] It can be understood that, firstly, by accurately reproducing the internal structure of the device, the virtual model can truly simulate the flow path of the fluid in the liquid dispensing tank, the eddy current form generated by the agitator paddle, and the mixing trajectory of the dry powder and the dispensing water, providing a visual analysis carrier for process parameter optimization (such as predicting the fluid shear force distribution at different agitator paddle speeds through simulation). Secondly, the digital retention of internal structure details enables the virtual model to have dynamic mapping capabilities. For example, when a pipeline in the actual device is blocked, the virtual model can drive the color warning (such as red highlighting) of the corresponding pipeline segment through the pressure sensor data and locate the blockage point in combination with hydrodynamic simulation. In addition, the complete geometric model lays a foundation for advanced applications such as stress analysis and heat transfer simulation in the future. For example, the bearing capacity can be evaluated through the tank shape parameters, or the power consumption curve can be predicted based on the agitator paddle structure to assist in equipment selection and energy efficiency optimization. Overall, the construction of this virtual model based on the internal structure realizes the leap from physical existence to digital computability of physical devices, enabling the full life cycle management of the mixing process (from design and commissioning to operation and maintenance) to be verified in advance, monitored in real time, and retrospectively analyzed in the virtual space, significantly improving the accuracy and reliability of intelligent operations.

[0039] 103, Based on the Navier-Stokes equation and the continuity equation, the fluid dynamics is used to perform a dynamic simulation of the fluid flow in the liquid dispensing tank, and the flow field distribution, eddy current region, and stirring dead zone are analyzed to construct a real-time fluid motion model corresponding to the target device.

[0040] Exemplarily, in 103, the virtual model of the liquid dispensing tank is imported into the computational fluid dynamics software; the physical property parameters of the fluid are set, including density, viscosity, etc.; based on the Navier-Stokes equation and the continuity equation, the finite volume method (FVM) or the finite element method (FEM) is used to numerically solve the fluid flow in the liquid dispensing tank; by post-processing the calculation results, the flow field distribution is analyzed, the eddy current region and the stirring dead zone are identified; according to the analysis results, a real-time fluid motion model describing the fluid motion law in the liquid dispensing tank is established; the particle image velocimetry (PIV) algorithm is used to simulate and verify the computational fluid dynamics simulation results, and the real-time fluid motion model is optimized based on the verification results, and the real-time fluid motion model is associated with the virtual model; wherein, tracer particles are added to the liquid dispensing tank experimental device, the particle image sequence in the simulated flow field is obtained, the particle motion velocity at each point in the flow field is calculated, and the accuracy of the fluid dynamics simulation results is verified by comparison.

[0041] Specifically, the principle of simulating the fluid flow in the liquid dispensing tank based on the Navier-Stokes equation and the continuity equation is to describe the mass and momentum conservation characteristics of the fluid through mathematical equations, and combine the computational fluid dynamics (CFD) algorithm to discretize the continuous fluid space in the liquid dispensing tank into a finite number of control volumes (finite volume method, FVM) or elements (finite element method, FEM), and numerically solve the physical quantities such as the flow velocity and pressure of each discrete element, so as to simulate the flow state of the fluid in the tank. Taking the finite volume method as an example, its core is to integrate the Navier-Stokes equation in each control volume, solve the physical quantities of each node through iterative calculation, and finally obtain data such as the velocity distribution, pressure distribution and turbulent kinetic energy distribution of the entire flow field.

[0042] For example, assume that the liquid dispensing tank is a cylindrical tank with a diameter of 2 meters and a height of 3 meters, and is equipped with a turbine stirrer (diameter 0.8 meters, rotation speed 200 rpm). The simulation goal is to analyze the flow field distribution near the stirrer and whether there is a stirring dead zone at the bottom of the tank. First, the virtual model of the liquid dispensing tank is imported into the ANSYS Fluent software, the fluid is set as water (density 1000 kg / m³, viscosity 0.001 Pa·s), and the RANS (Reynolds-averaged Navier-Stokes) equation combined with the k-ε turbulence model is used to close the equations. The tank body is divided into 1 million tetrahedral meshes through grid division, and local refinement is carried out in the stirrer area. After the calculation converges, the post-processing results show that a high-speed turbulent region (flow velocity about 2.5 m / s) is formed near the stirrer, the fluid in the upper part of the tank shows a spiral upward flow state, while there is a low-speed eddy current region (flow velocity < 0.1 m / s) near the tank wall at the bottom, which is determined as a stirring dead zone.

[0043] To verify the simulation results, fluorescent tracer particles (diameter 50 μm) were added to the physical experimental device. The middle section of the tank was illuminated by a laser sheet, and a high-speed camera captured the motion images of the particles at a frequency of 200 fps. The cross-correlation analysis was performed on 50 consecutive frames of images using the particle image velocimetry (PIV) algorithm to calculate the velocity vectors of each point in the flow field. It was found by comparison that the error between the simulated predicted flow velocity in the agitator paddle area and the experimental value was <5%. The position of the low-velocity area at the bottom of the tank was consistent with the experimental observation, but the simulation did not fully capture the secondary eddy current caused by the baffle design. Based on this, the baffle angle parameter of the virtual model was adjusted and re-simulated. The flow velocity distribution error between the corrected model and the experimental results was reduced to within 3%. Finally, a high-precision real-time fluid motion model was established.

[0044] Thus, after discovering the stirring dead corner at the bottom of the tank through simulation, a flow guide cone (cone angle 60°) was tried to be added or the installation height of the agitator paddle was adjusted (raised from 0.5 m to 0.8 m from the bottom of the tank) in the virtual model. The simulation results showed that the flow guide cone could increase the bottom flow velocity to 0.3 m / s and reduce the dead corner area by 70%, providing a clear solution for the transformation of physical equipment. The power consumption and fluid shear force at different rotational speeds (150 - 250 rpm) were simulated. It was found that when the rotational speed decreased from 200 rpm to 180 rpm, the power consumption decreased by 12%, but the shear force only decreased by 8%, and the dissolution time was extended by 5 minutes. Considering the requirements of the fracturing fluid dissolution efficiency, 180 rpm was finally selected as the economic rotational speed, and about 150 kWh of electric energy could be saved for single-well operations.

[0045] For the low-temperature scenario in winter (the fluid viscosity increased to 0.002 Pa·s), the simulation showed that the fluid turbulence intensity decreased by 15% at the same rotational speed. The rotational speed of the agitator paddle needed to be increased to 220 rpm to maintain the dissolution efficiency and avoid the project schedule delay caused by on-site debugging.

[0046] Through the closed-loop of numerical simulation - experimental verification - model correction, the accurate characterization of the complex flow field in the mixing tank was achieved. The traditional adjustment of stirring parameters relying on experience was transformed into data-driven scientific optimization, making the hydrodynamic characteristics of the mixing process change from invisible to computable, verifiable, and optimizable, significantly improving the uniformity and efficiency of the fracturing fluid mixing, and at the same time reducing the physical experiment cost and the risk of equipment debugging.

[0047] 104. Based on the principle of chemical kinetics, a chemical kinetics model for dry powder dissolution, cross-linking reaction, and gel-breaking reaction was established, and the material characteristic parameters were incorporated into the chemical kinetics model.

[0048] In the embodiment of the present application, the chemical kinetics model is used to simulate the reaction process of the target device under different conditions.

[0049] Specifically, as an optional embodiment, in 104, the hygroscopicity curve and dissolution kinetic parameters of the polymer, the reaction rate constant and temperature sensitivity data of the crosslinking agent, and the influence parameters of different water quality data on dissolution and crosslinking are obtained; based on the principles of chemical kinetics, chemical kinetic models for dry powder dissolution, crosslinking reaction, and gel-breaking reaction are established respectively; using the regression analysis algorithm, based on the collected experimental data, a regression model between water quality parameters and relevant indicators of dry powder dissolution and crosslinking reaction is established, and the regression equation coefficients are determined by the least squares method; using the artificial neural network ANN, with water quality parameters as the input layer nodes and relevant indicators of dry powder dissolution and crosslinking reaction as the output layer nodes, a hidden layer is set and the neural network is trained with a large amount of experimental data to learn complex non-linear relationships; the results obtained from regression analysis and the ANN network are used as the model inputs of the chemical kinetic model, and the chemical kinetic model is calibrated and verified with experimental data to ensure that the chemical kinetic model can simulate the reaction process of the target device under different conditions.

[0050] Taking the dissolution of guar gum and the crosslinking reaction with borate as an example, in the data collection and model establishment stage, guar gum, as a polymer, its hygroscopicity curve shows that the moisture content rises from 5% to 12% within 24 hours in an environment with humidity > 60%, and the activation energy E of the dissolution kinetic parameters a = 45 kJ / mol indicates that the dissolution rate increases by 1.8 times for every 10 °C increase in temperature; borax, as a crosslinking agent, has a reaction rate constant k = 0.02 min⁻¹ (25 °C, pH = 9), and the temperature sensitivity data shows an Arrhenius exponential growth (E a = 60 kJ / mol), and when the calcium and magnesium ion concentration > 500 ppm, the crosslinking time is extended by more than 50%. Based on this, a dissolution model, a crosslinking model, and a bimolecular reaction model are constructed, combined with an ion strength correction term. In terms of regression analysis and neural network application, a linear regression equation Y = 12.5 + 0.005X1 - 1.2X2 is fitted with salinity and pH value as independent variables and crosslinking time as the dependent variable, and the experimental verification error < 5%. The artificial neural network is designed with 3 nodes in the input layer (salinity, pH value, temperature), 10 nodes in the hidden layer (ReLU activation function), and 2 nodes in the output layer (dissolution rate, crosslinking degree). After training with 200 sets of experimental data, the non-linear fitting accuracy is better than that of the regression model (MSE is reduced by 30%). When calibrating and validating the model, the regression and ANN output data are input into the chemical kinetic model to adjust the parameters. For example, when the salinity = 1500 ppm, the crosslinking time predicted by the calibrated model is consistent with the experimental value, and in high-salinity water quality, the predicted dissolution and crosslinking times by the model are close to the measured values, verifying the adaptability to complex working conditions.

[0051] In terms of accurate prediction and control of chemical reactions, the dry nitrogen purge process was adjusted through model analysis to shorten the dissolution time of guar gum from 30 minutes to 18 minutes, and the efficiency was improved by 40%. In low-temperature well operations, the use of titanate crosslinkers shortened the crosslinking time from >20 minutes to 8 minutes. In the face of complex working conditions, when the mineralization of oilfield reinjection water increases, the model triggers the water quality treatment procedure to avoid construction interruption. The upper limit of ammonium persulfate dosage and the cooling system are set through the gel-breaking reaction model to reduce the risk of gel-breaking agent failure from 5% to less than 1%. In terms of process cost, the dosage of guar gum is reduced by optimizing the formula based on the model, saving about 12,000 yuan per well. The virtual model preview reduces 50% of physical experiments and saves about 60% of reagent costs and time costs. In terms of intelligent decision support, the response speed of model abnormal warning is 80% higher than that of manual intervention. The problem of low return rate of fracturing fluid is solved through retrospective analysis and increased to more than 75%.

[0052] In summary, the chemical kinetic model that integrates regression analysis and artificial neural networks has achieved multi-dimensional and precise modeling of dissolution, cross-linking, and degelling reactions in the mixing process of dry powder fracturing fluid, breaking through the limitations of traditional empirical formulas, making the reaction process calculable, interventional, and traceable, improving the adaptability of the mixing process to multiple materials and complex water quality, reducing material costs and experimental risks, and providing core technical support for the intelligent and efficient oilfield fracturing operations.

[0053] 105. Input the mixing process data into the virtual model, dynamically calibrate the virtual model through the real-time fluid motion model and the chemical kinetic model, dynamically adjust the water quality data, material ratio and stirring conditions, and perform multi-scenario real-time simulation analysis on the mixing process to obtain the mixing simulation data of the target equipment; and perform corresponding dry powder fracturing fluid mixing control on the target equipment according to the mixing simulation data.

[0054] First, in 105, input the real-time collected mixing process data into the interface corresponding to the virtual model; through the real-time fluid motion model, update and simulate the fluid flow state in the mixing tank based on the computational fluid dynamics algorithm, and through the chemical kinetics model, update and simulate the chemical reaction process by combining the regression analysis algorithm and the artificial neural network algorithm; according to the preset multi-scenario simulation rules, use the genetic algorithm to take the ratios of thickener, cross-linking agent, and additive as chromosome genes, define the fitness function with the performance of the fracturing fluid as the evaluation criterion, and optimize the material ratios through selection, crossover, and mutation operations; the performance of the fracturing fluid includes: cross-linking time, gel-breaking time, and fracturing fluid viscosity; use the response surface method, arrange experimental points through central composite design and Box-Behnken design, perform regression analysis on the experimental data to establish a response surface model, and determine the optimal material ratio region; under different parameter combinations, based on the optimized material ratio and the optimal material ratio region, run the real-time fluid motion model and the chemical kinetics model respectively to perform real-time simulation on the mixing process, record the key data during the simulation process, and form the mixing simulation data of the target equipment.

[0055] Exemplarily, in the intelligent mixing system of oilfield fracturing fluid, based on the scheme of real-time data-driven virtual model and multi-algorithm collaborative optimization, efficient and accurate design of material ratios can be achieved. First, input the mixing process data such as pressure, temperature, flow rate, and liquid level collected in real time into the virtual model interface. The real-time fluid motion model updates the flow field distribution in the mixing tank (such as the eddy current region and velocity vector) based on the computational fluid dynamics algorithm, and the chemical kinetics model dynamically simulates the dissolution, cross-linking, and gel-breaking reaction processes (such as the viscosity change curve and reaction conversion rate) by combining regression analysis and artificial neural network algorithms. Subsequently, for the ratio optimization of thickener, cross-linking agent, and additive, the genetic algorithm encodes the addition ratios of each component as chromosome genes, constructs the fitness function with performance indicators such as cross-linking time, gel-breaking time, and fracturing fluid viscosity, and iteratively evolves the population through selection (such as roulette wheel selection), crossover (two-point crossover), and mutation (Gaussian mutation) operations. For example, initially randomly generate 100 sets of ratio schemes, and converge to the optimal solution after 20 generations of evolution.

[0056] Meanwhile, the response surface method uses central composite design (such as three factors and five levels) or Box-Behnken design to arrange experimental points, performs quadratic polynomial regression fitting on experimental data, constructs a response surface model (such as Y = β0 + ∑βᵢXᵢ +∑βᵢᵢXᵢ² + ∑βᵢⱼXᵢXⱼ), intuitively displays the relationship between the mixing ratio parameters and the performance of the fracturing fluid through contour plots or three-dimensional surface plots, and determines the optimal mixing ratio region (such as the parameter combination with the shortest crosslinking time). Under different combinations of operating conditions (such as temperature 20 - 60°C, salinity 500 - 3000 ppm), the optimized mixing ratio obtained by the genetic algorithm and the parameters of the optimal region determined by the response surface method are input into the real-time fluid motion model and the chemical kinetics model for multiple rounds of simulation verification, and key data such as stirring power, dissolution time, and crosslinking strength are recorded to form a mixing simulation database covering more than 500 operating conditions.

[0057] Thus, in the parameter sensitivity analysis, it is found through the genetic algorithm that the influence weight of the crosslinking agent concentration on the viscosity of the fracturing fluid reaches 45% (temperature influence 25%, pH value influence 30%), and accordingly, the crosslinking agent addition strategy is adjusted. The response surface model predicts that the best viscosity retention rate (92%) can be obtained at a temperature of 40°C, pH = 9, and crosslinking agent concentration of 0.8%. The measured value is 90.5%, and the error < 2%. In a certain shale gas fracturing operation, based on this scheme, the guar gum dosage is reduced from 35 kg / m³ to 32 kg / m³, the crosslinking time is controlled within 8 - 12 min (design target 10 ± 2 min), the viscosity of the gel-breaking fluid < 5 mPa·s (standard value < 10 mPa·s), and the material cost per well is saved by 12,000 yuan, and the operation efficiency is increased by 15%. Through the integration of real-time data and multiple algorithms, the system has achieved the leap from "empirical trial and error" to "data-driven precise design", providing an intelligent solution for the fracturing transformation of complex oil and gas reservoirs.

[0058] Subsequently, in 105, the mixing simulation data is analyzed to extract at least one key performance index among the dissolution uniformity, crosslinking performance, and gel-breaking time of the fracturing fluid; and the key performance index is compared with the preset standard parameter range. If the key performance index exceeds the standard parameter range, corresponding control instructions are generated according to the preset control strategy. Among them, the control instructions are used to adjust the rotation speed of the stirrer, change the feeding speed of the feeding device, and adjust the injection amount of the additive. The control instructions are sent to the actuator of the target device through the data transmission module to achieve the dry powder fracturing fluid mixing control of the target device.

[0059] Exemplarily, in the mixing control of dry powder fracturing fluid in oilfields, the analysis of the mixing simulation data and the subsequent execution of control instructions are the core links to ensure the quality of the fracturing fluid and the operation safety. This process realizes the closed-loop management from performance evaluation, anomaly diagnosis to precise regulation in a data-driven manner.

[0060] In actual operation, the system first extracts key performance indicators from the mixed simulation data. For example, the dissolution uniformity of the fracturing fluid is evaluated through the computational fluid dynamics simulation results. When the standard deviation of the fluid viscosity in the mixing tank exceeds 0.3 mPa·s, it is determined that the dissolution is uneven. According to the crosslinking time and crosslinking degree output by the chemical kinetics model, it is judged whether the crosslinking performance meets the standard. For example, if the crosslinking time exceeds ±10% of the designed value (assuming the designed crosslinking time is 10 min, and exceeding the range of 9 - 11 min is regarded as abnormal), or the viscosity of the fracturing fluid after crosslinking does not reach the target value (such as less than 80 mPa·s); by monitoring the gel-breaking reaction process, it is determined whether the gel-breaking time meets the requirements. If the gel-breaking time is too long (exceeding 12 h) or the residual viscosity of the liquid after gel-breaking is too high (>5 mPa·s), it indicates abnormal gel-breaking performance.

[0061] Subsequently, the system compares these key performance indicators with the preset standard parameter ranges. These standard parameters are formulated based on a large amount of experimental data and on-site operation experience, covering the performance requirements of fracturing fluids under different geological conditions and construction processes. For example, for a certain low-permeability oil reservoir, it is preset that the dissolution uniformity of the fracturing fluid needs to meet a viscosity standard deviation <0.2 mPa·s, the crosslinking time is controlled within 9 - 11 min, the gel-breaking time is between 8 - 10 h, and the residual viscosity after gel-breaking <3 mPa·s.

[0062] Once the key performance indicators exceed the standard parameter ranges, the system will immediately generate corresponding control instructions according to the preset control strategy. For example, if it is detected that the dissolution of the fracturing fluid is uneven, the system will generate an instruction to increase the rotation speed of the stirrer (such as from 200 rpm to 250 rpm) to enhance the fluid shear force and accelerate the dissolution of the dry powder; if the crosslinking time is too long and it is determined that the amount of crosslinking agent added is insufficient, the system will issue an instruction to increase the injection speed of the crosslinking agent (such as from 5 L / min to 7 L / min); if the gel-breaking time is too long, the injection amount of the gel-breaking agent will be automatically adjusted (such as increasing the addition ratio of ammonium persulfate from 0.8% to 1.0%).

[0063] Finally, the control instructions are sent to the actuator of the target device through a data transmission module (such as 5G, industrial Ethernet). After receiving the instructions, the actuator responds quickly. For example, the frequency converter of the stirrer motor adjusts the rotation speed, the metering pump of the feeding device changes the flow rate, and the additive injection system adjusts the valve opening to achieve precise control of the target device. During the execution of the instructions, the system continuously monitors the operating status of the device and the performance changes of the fracturing fluid, forming a closed-loop control loop of data acquisition - analysis and decision-making - instruction execution - effect feedback.

[0064] This control method significantly improves the automation and precision level of fracturing fluid blending. Compared with traditional manual regulation, its response speed is increased by more than 80%, and it can respond to abnormal indicators within 30 seconds. The control accuracy of key performance indicators is increased by 30%. For example, the fluctuation range of crosslinking time is reduced from ±2 min to ±1 min, effectively reducing the construction risks caused by unstable fracturing fluid performance, while reducing material waste and production costs, providing strong guarantee for the efficient development of oilfields.

[0065] In the embodiments of the present application, it helps to improve the quality and efficiency of fracturing fluid blending, enhance the adaptability to complex production environments, and assist in improving the development efficiency of unconventional oil and gas reservoirs.

[0066] In another embodiment of the present application, a blending control device for oilfield dry fracturing fluid is further provided. Refer to Figure 2 as shown, the device includes the following units: The acquisition unit is configured to deploy multi-source sensors in the target equipment to collect blending process data in real time; wherein, the target equipment at least includes: a liquid mixing tank, a stirrer, and a feeding device; the blending process data at least includes: stirring speed, pressure in the liquid mixing tank, fluid temperature, dry powder feeding amount, and liquid mixing water flow rate; The first construction unit is configured to establish a virtual model corresponding to the target equipment based on the internal structure of the target equipment; wherein, the virtual model is provided with a liquid mixing tank, a stirrer, a feeding device, and the pipeline layout between each virtual device; the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout; The second construction unit is configured to perform dynamic simulation on the fluid flow in the liquid mixing tank by using fluid dynamics based on the Navier-Stokes equation and the continuity equation, and analyze the flow field distribution, vortex region, and stirring dead angle, and construct a real-time fluid motion model corresponding to the target equipment; The third construction unit is configured to establish a chemical kinetic model of dry powder dissolution, crosslinking reaction, and gel-breaking reaction based on the principle of chemical kinetics, and incorporate material characteristic parameters into the chemical kinetic model; the chemical kinetic model is used to simulate the reaction process of the target equipment under different conditions; The control unit is configured to input the blending process data into the virtual model, dynamically calibrate the virtual model through the real-time fluid motion model and the chemical kinetic model, dynamically adjust the water quality data, material ratio, and stirring conditions, perform multi-scenario real-time simulation analysis on the blending process to obtain the blending simulation data of the target equipment; and perform corresponding dry fracturing fluid blending control on the target equipment according to the blending simulation data.

[0067] The device can implement each step in the above-mentioned blending control method for oilfield dry fracturing fluid, and will not be elaborated here.

[0068] In yet another embodiment of the present application, an electronic device is further provided, including: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory is used to store a computer program; when the processor is used to execute the program stored on the memory, it implements the mixing control method of the oilfield dry fracturing fluid described in the method embodiment. The communication bus 1140 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 3 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0069] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0070] The memory 1130 may include a Random Access Memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0071] The above-mentioned processor 1110 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Correspondingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed, it can implement the steps executable by the electronic device in the above method embodiment.

[0072] ​

Claims

1. A mixing control method for dry fracturing fluid in oil fields, characterized in that, Including: Deploy multi-source sensors in the target device to collect the mixing process data in real time; among them, the target device at least includes: a liquid mixing tank, a stirrer, and a feeding device; the mixing process data at least includes: stirring speed, pressure in the liquid mixing tank, fluid temperature, dry powder feeding amount, and liquid mixing water flow rate; Based on the internal structure of the target device, establish a virtual model corresponding to the target device; among them, the virtual model is provided with a liquid mixing tank, a stirrer, a feeding device, and the pipeline layout between each virtual device; the internal structure at least includes: tank shape, agitator blade structure, pipeline layout; Based on the Navier-Stokes equation and the continuity equation, use fluid dynamics to perform dynamic simulation on the fluid flow in the liquid mixing tank, and analyze the flow field distribution, eddy current area, and stirring dead angle to construct a real-time fluid motion model corresponding to the target device; Based on the principle of chemical kinetics, establish a chemical kinetics model for dry powder dissolution, cross-linking reaction, and gel-breaking reaction, and incorporate the material characteristic parameters into the chemical kinetics model; the chemical kinetics model is used to simulate the reaction process of the target device under different conditions; Input the mixing process data into the virtual model, dynamically calibrate the virtual model through the real-time fluid motion model and the chemical kinetics model, dynamically adjust the water quality data, material ratio, and stirring conditions, and perform multi-scenario real-time simulation analysis on the mixing process to obtain the mixing simulation data of the target device; execute the corresponding dry fracturing fluid mixing control on the target device according to the mixing simulation data.

2. The mixing control method of the oilfield dry fracturing fluid according to claim 1, wherein The multi-source sensors at least include: pressure sensors, temperature sensors, flow sensors, and liquid level sensors; the deployment of multi-source sensors in the target device to collect the mixing process data in real time includes: Deploy pressure sensors and liquid level sensors on the liquid mixing tank to collect the pressure and liquid level data in the liquid mixing tank in real time; Install a rotational speed sensor on the stirrer to obtain the stirring speed data; Respectively set flow sensors on the dry powder conveying pipeline and the liquid mixing water conveying pipeline of the feeding device to monitor the dry powder feeding amount and the liquid mixing water flow rate in real time; Arrange temperature sensors in the fluid area of the liquid mixing tank to collect fluid temperature data, and transmit all kinds of collected data to the data processing terminal in real time through the Internet of Things communication module.

3. The mixing control method of the oilfield dry fracturing fluid according to claim 1, characterized in that The establishment of a virtual model corresponding to the target device based on the internal structure of the target device includes: Construct three-dimensional geometric models of the liquid mixing tank, stirrer, and feeding device according to the actual size parameters of the target device; During the modeling process, generate internal structure details of the tank shape, agitator blade structure, and pipeline layout in the three-dimensional geometric model; through the model mapping technology, import the three-dimensional geometric model with increased internal structure details into the digital twin platform to form a virtual model corresponding one-to-one to the target device.

4. The mixing control method of the oilfield dry fracturing fluid according to claim 1, wherein The use of fluid dynamics to perform dynamic simulation on the fluid flow in the liquid mixing tank based on the Navier-Stokes equation and the continuity equation, and analyze the flow field distribution, eddy current area, and stirring dead angle to construct a real-time fluid motion model corresponding to the target device includes: Import the virtual model of the liquid mixing tank into the computational fluid dynamics software; Set the physical property parameters of the fluid, including density and viscosity; Based on the Navier - Stokes equations and the continuity equation, numerically solve the fluid flow in the liquid mixing tank using the finite volume method (FVM) or the finite element method (FEM); Through post - processing of the calculation results, analyze the flow field distribution, identify the eddy current regions and the dead zones of agitation; According to the analysis results, establish a real - time fluid motion model that describes the fluid motion law in the liquid mixing tank; Use the particle image velocimetry (PIV) algorithm to conduct simulation experiments to verify the computational fluid dynamics simulation results. Optimize the real - time fluid motion model based on the verification results and correlate the real - time fluid motion model with the virtual model. Specifically, add tracer particles in the liquid mixing tank experimental device, obtain the particle image sequence in the simulated flow field, calculate the particle motion velocity at each point in the flow field, and compare and verify the accuracy of the fluid dynamics simulation results.

5. The mixing control method of the dry fracturing fluid for oil fields according to claim 1, characterized in that Based on the principle of chemical kinetics, establish chemical kinetic models for dry powder dissolution, cross - linking reaction, and gel - breaking reaction, and incorporate the material characteristic parameters into the chemical kinetic models, including: Obtain the hygroscopicity curve and dissolution kinetic parameters of the polymer, the reaction rate constant and temperature sensitivity data of the cross - linker, and the influence parameters of different water quality data on dissolution and cross - linking; Based on the principle of chemical kinetics, establish chemical kinetic models for dry powder dissolution, cross - linking reaction, and gel - breaking reaction respectively; Adopt the regression analysis algorithm. Based on the collected experimental data, establish a regression model between the water quality parameters and the relevant indicators of dry powder dissolution and cross - linking reaction, and determine the regression equation coefficients by the least - squares method; Use the artificial neural network (ANN). Take the water quality parameters as the input layer nodes, take the relevant indicators of dry powder dissolution and cross - linking reaction as the output layer nodes, set the hidden layer, and train the neural network with a large amount of experimental data to learn the complex non - linear relationship; Take the results obtained from the regression analysis and the ANN network as the model inputs of the chemical kinetic model, and calibrate and verify the parameters of the chemical kinetic model through experimental data to ensure that the chemical kinetic model can simulate the reaction process of the target equipment under different conditions.

6. The mixing control method of the dry fracturing fluid for oil fields according to claim 1, characterized in that Input the mixing process data into the virtual model, and dynamically calibrate the virtual model through the real - time fluid motion model and the chemical kinetic model. Dynamically adjust the water quality data, material ratio, and agitation conditions, and conduct multi - scenario real - time simulation analysis of the mixing process to obtain the mixing simulation data of the target equipment, including: Input the real - time collected mixing process data into the interface corresponding to the virtual model; Through the real - time fluid motion model, update the simulation of the fluid flow state in the liquid mixing tank based on the computational fluid dynamics algorithm. Through the chemical kinetic model, combine the regression analysis algorithm and the artificial neural network algorithm to update the simulation of the chemical reaction process; According to the preset multi - scenario simulation rules, use the genetic algorithm. Take the ratios of the thickener, cross - linker, and additive as chromosome genes, define the fitness function with the fracturing fluid performance as the evaluation criterion, and optimize the material ratio through selection, crossover, and mutation operations. The fracturing fluid performance includes: cross - linking time, gel - breaking time, and fracturing fluid viscosity; Using the response surface method, the experimental points are arranged through central composite design and Box-Behnken design, and the response surface model is established by regression analysis of the experimental data to determine the optimal material ratio region; Under different parameter combinations, based on the optimized material ratio and the optimal material ratio region, the real-time fluid motion model and the chemical kinetics model are respectively run to perform real-time simulation on the mixing process, record the key data during the simulation process, and form the mixing simulation data of the target device.

7. The blending control method of the oilfield dry fracturing fluid according to claim 6, wherein Performing corresponding dry-fracturing fluid mixing control on the target device according to the mixing simulation data includes: Analyzing the mixing simulation data, extracting at least one key performance index among the uniformity of fracturing fluid dissolution, crosslinking performance, and gel-breaking time; and comparing the key performance index with the preset standard parameter range; If the key performance index exceeds the standard parameter range, corresponding control instructions are generated according to the preset control strategy; wherein, the control instructions are used to adjust the agitator speed, change the feeding speed of the feeding device, and adjust the additive injection amount; The control instructions are sent to the actuator of the target device through the data transmission module to realize the dry-fracturing fluid mixing control of the target device.

8. A mixing control device for dry fracturing fluid in an oilfield, characterized in that, The device includes: An acquisition unit, configured to deploy multi-source sensors in the target device to collect mixing process data in real time; wherein, the target device at least includes: a liquid mixing tank, an agitator, and a feeding device; the mixing process data at least includes: stirring speed, pressure in the liquid mixing tank, fluid temperature, dry powder feeding amount, and liquid mixing water flow rate; A first construction unit, configured to establish a virtual model corresponding to the target device based on the internal structure of the target device; wherein, the virtual model is provided with a liquid mixing tank, an agitator, a feeding device, and the pipeline layout between each virtual device; the internal structure at least includes: the shape of the tank body, the structure of the stirring paddle, and the pipeline layout; A second construction unit, configured to perform kinetic simulation on the fluid flow in the liquid mixing tank by using fluid dynamics based on the Navier-Stokes equation and the continuity equation, analyze the flow field distribution, vortex region, and stirring dead angle, and construct a real-time fluid motion model corresponding to the target device; A third construction unit, configured to establish a chemical kinetics model for dry powder dissolution, crosslinking reaction, and gel-breaking reaction based on the principle of chemical kinetics, and incorporate the material characteristic parameters into the chemical kinetics model; the chemical kinetics model is used to simulate the reaction process of the target device under different conditions; A control unit, configured to input the mixing process data into the virtual model, dynamically calibrate the virtual model through the real-time fluid motion model and the chemical kinetics model, dynamically adjust the water quality data, material ratio, and stirring conditions, perform multi-scenario real-time simulation analysis on the mixing process to obtain the mixing simulation data of the target device; perform corresponding dry-fracturing fluid mixing control on the target device according to the mixing simulation data.

9. An electronic device, characterized in that, Including: A memory, used to store computer software programs; A processor, used to read and execute the computer software program, and further implement the mixing control method of the oilfield dry-fracturing fluid according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The computer software program is stored in the storage medium, and when the computer software program is executed by a processor, it implements the mixing control method of the oilfield dry fracturing fluid according to any one of claims 1-7.

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