Shallow layer heavy oil well hot nitrogen cooperation deep steering plugging fracturing yield increasing system and method

Abstract

The invention discloses a shallow heavy oil well hot nitrogen cooperation deep steering plugging fracturing stimulation system and method. The system comprises a hot nitrogen parameter regulation and control module, a crack evolution simulation module, a plugging agent displacement analysis module, a multi-field coupling solving module, an intelligent analysis processing module and a fracturing execution regulation and control module. Initial parameters are output through the hot nitrogen parameter regulation and control module, and the fracture evolution simulation module processes the parameters through a thermally-induced fatigue fracture network fractal evolution model and transmits results to the plugging agent displacement analysis module; the plugging agent displacement analysis module is used for calculating data based on a multi-slug plugging agent dynamic displacement-retention model, and generating multi-field distribution data by adopting a multi-field coupling numerical solution algorithm through the multi-field coupling solution module; the intelligent analysis processing module optimizes parameters by means of the hot nitrogen fracturing full-life-cycle intelligent analysis platform, and the fracturing execution regulation and control module implements operation. The problems of insufficient cooperation of cracks and hot nitrogen and poor plugging adaptability are solved, accurate regulation and control in the whole process are achieved, and the yield increasing effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fracturing of shallow heavy oil resources, and particularly relates to a heat-nitrogen synergistic deep-diverting plugging fracturing stimulation system and method for shallow heavy oil wells. BACKGROUND

[0002] Shallow heavy oil resources are characterized by shallow burial, complex pore structure and high viscosity of heavy oil, and are faced with problems such as insufficient formation energy, large fluid flow resistance and low recovery efficiency in the process of exploitation. The heat-nitrogen fracturing technology has become an important means to improve the flowability of shallow heavy oil due to the low viscosity, high thermal conductivity and cleanup characteristics of heat-nitrogen, but it needs to be combined with deep-diverting plugging to achieve efficient crack propagation and enhanced recovery. In the current fracturing operation of shallow heavy oil wells, the heat-nitrogen injection parameters, crack evolution state and plugging agent migration law are coupled with each other, and the dynamic matching relationship between heat-induced crack development and plugging agent retention needs to be accurately controlled. However, the traditional operation relies on empirical parameter setting and lacks systematic integration and intelligent control of multi-field parameters in the whole life cycle of fracturing, which is difficult to adapt to the complex exploitation requirements brought by reservoir heterogeneity and rheological properties of heavy oil.

[0003] The existing technology has two significant shortcomings in the field of heat-nitrogen fracturing of shallow heavy oil wells: first, the synergistic control of crack evolution and heat-nitrogen injection is insufficient, and the fractal evolution process of crack network cannot be accurately described by combining the heat-induced fatigue effect, and only single pressure or temperature parameters are used to guide the operation, which leads to the deviation of crack propagation path from the expected one, insufficient deep reservoir reconstruction and the inability to form efficient seepage channels; second, the dynamic adaptability of plugging agent displacement-retention process is lacking, and the correlation analysis mechanism of multi-pulse plugging agent, crack parameters and heat-nitrogen field distribution has not been established, and the concentration distribution and retention efficiency of plugging agent in the crack are difficult to accurately control, resulting in incomplete plugging and heat-nitrogen channeling or excessive retention and blocking of effective cracks, which seriously affects the fracturing stimulation effect. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present application provides a heat-nitrogen synergistic deep-diverting plugging fracturing stimulation system and method for shallow heavy oil wells.

[0005] The technical scheme adopted by the present application is a shallow heavy oil well heat nitrogen synergistic deep diversion plugging and fracturing stimulation system, comprising: a heat nitrogen parameter control module, a fracture evolution simulation module, a plugging agent displacement analysis module, a multi-field coupling solution module, an intelligent analysis processing module, and a fracturing execution control module; the heat nitrogen parameter control module outputs heat nitrogen injection rate, temperature, and pressure parameters to the fracture evolution simulation module and the fracturing execution control module; the fracture evolution simulation module processes the rate, temperature, and pressure parameters based on a heat-induced fatigue fracture network fractal evolution model, and then transmits the fracture fractal dimension and expansion path parameters to the plugging agent displacement analysis module; the plugging agent displacement analysis module processes the fracture parameters by using a multi-segment plug plugging agent dynamic displacement-retention model, and outputs plugging agent concentration distribution and retention efficiency parameters to the multi-field coupling solution module; the multi-field coupling solution module integrates heat nitrogen parameters, fracture parameters, and plugging agent parameters by using a multi-field coupling numerical solution algorithm, generates pressure field, temperature field, and concentration field distribution data, and transmits the data to the intelligent analysis processing module; the intelligent analysis processing module processes the multi-field distribution data based on a heat nitrogen fracturing full life cycle intelligent analysis platform, and outputs a fracturing parameter optimization scheme to the fracturing execution control module; and the fracturing execution control module adjusts heat nitrogen injection and plugging agent injection parameters according to the optimization scheme, and completes the shallow heavy oil well heat nitrogen synergistic deep diversion plugging and fracturing operation.

[0006] Further, the heat-induced fatigue fracture network fractal evolution model in the fracture evolution simulation module is expressed as: wherein, is the fracture network fractal dimension, is the initial fracture fractal dimension, is the heat-induced fractal evolution coefficient, is the heat nitrogen injection temperature, is the initial formation temperature, is the critical temperature difference of heavy oil, is the pressure influence coefficient, is the heat nitrogen injection pressure, is the fracturing time, is the heat nitrogen injection rate, is the viscosity of heavy oil; and the pressure field control equation of the multi-field coupling numerical solution algorithm in the multi-field coupling solution module is: wherein, is the reservoir permeability, is the fluid viscosity, is the formation pressure, is the reservoir porosity, is the fluid density, is the Dirac function of the injection point.

[0007] Further, the multi-segment plug plugging agent dynamic displacement-retention model in the plugging agent displacement analysis module is expressed as: wherein, is a plugging agent retention efficiency, is a plugging agent adsorption coefficient, is a plugging agent slug concentration, is a displacement rate influence coefficient, is a plugging agent displacement rate, is a hot nitrogen injection rate, is a fracture network fractal dimension, is an initial fracture fractal dimension, is a hot nitrogen injection temperature, is an initial formation temperature; the parameter optimization objective function of the hot nitrogen fracturing full life cycle intelligent analysis platform in the intelligent analysis processing module is: wherein, is an optimization objective value, is a weight coefficient, is a reference retention efficiency, is an initial fracture fractal dimension, is a maximum injection pressure.

[0008] Further, the temperature field evolution equation of the multi-field coupling numerical solution algorithm in the multi-field coupling solving module is: wherein, is a formation rock density, is a formation rock specific heat capacity, is a formation temperature, is a fracturing time, is a formation thermal conductivity, is a hot nitrogen injection flow rate, is a hot nitrogen density, is a hot nitrogen specific heat capacity, is a hot nitrogen injection temperature; the fracture propagation length calculation formula of the fracture evolution simulation module is: wherein, is a fracture propagation length, is an initial fracture length, is a fracture propagation coefficient, is a formation pore pressure, is a fracturing time, is a hot nitrogen injection temperature, is an initial formation temperature, is a heavy oil viscosity.

[0009] Further, the hot nitrogen injection rate dynamic adjustment formula of the hot nitrogen parameter regulation module is: wherein, is a hot nitrogen injection rate, is an initial injection rate, is a fracture influence coefficient, is a fracture network fractal dimension, D0 is the initial fracture fractal dimension, D is the time attenuation coefficient, T is the fracturing time, P is the maximum injection pressure, P is the hot nitrogen injection pressure; the plugging agent concentration distribution equation of the plugging agent displacement analysis module is: wherein, C is the plugging agent concentration, T is the displacement time, V is the plugging agent displacement rate, D is the plugging agent diffusion coefficient, K is the plugging agent reaction rate constant, E is the plugging agent retention efficiency.

[0010] Further, the fracturing effect prediction formula of the hot nitrogen fracturing full life cycle intelligent analysis platform of the intelligent analysis processing module is: wherein, Q is the predicted oil production, Q is the base oil production, F is the effect prediction function, D is the fracture network fractal dimension, D0 is the initial fracture fractal dimension, E is the plugging agent retention efficiency, E0 is the base retention efficiency, T is the formation average temperature, T0 is the initial formation temperature, T is the hot nitrogen injection temperature; the porosity dynamic change formula of the multi-field coupling solution module is: wherein, φ is the dynamic porosity, φ0 is the initial porosity, β is the thermal expansion coefficient, T is the formation temperature, T0 is the initial formation temperature, C is the pressure compression coefficient, P is the formation pressure, P0 is the initial pressure, E is the plugging agent influence coefficient, E is the plugging agent retention efficiency, E0 is the base retention efficiency.

[0011] Further, the multi-field coupling solving module comprises a parameter preprocessing unit, a field equation construction unit, a numerical discretization unit, and a coupling solving unit. The parameter preprocessing unit receives the thermal nitrogen injection rate, temperature, and pressure parameters output by the thermal nitrogen parameter regulation module, the fracture fractal dimension and propagation path parameters output by the fracture evolution simulation module, and the plugging agent concentration distribution and retention efficiency parameters output by the plugging agent displacement analysis module, performs data format matching and range verification on the parameters from different sources, and normalizes the data to a unified input format after removing abnormal fluctuation data. The field equation construction unit establishes the percolation field equation describing the pressure transmission law, the temperature field equation describing heat conduction and convection, and the concentration field equation describing plugging agent migration based on the multi-field coupling numerical solving algorithm and in combination with the physical property parameters of the shallow heavy oil reservoir, and clearly defines the coupling correlation terms between the field equations. The numerical discretization unit discretizes the constructed pressure field, temperature field, and concentration field equations in the spatial and temporal domains using the finite element method, converts the partial differential equations into algebraic equation sets, determines the grid division density and time step, and ensures that the discretization precision is adapted to the heterogeneity characteristics of the reservoir. The coupling solving unit solves the discretized algebraic equation sets using an iterative method, corrects the solving process through the interaction coefficient of the pressure field and the temperature field and the coupling factor of the temperature field and the concentration field, and obtains the spatial distribution data of the pressure, temperature, and concentration in the formation at different times and transmits the data to the intelligent analysis and processing module.

[0012] Further, the intelligent analysis and processing module comprises a data receiving and storage unit, a full life cycle modeling unit, a parameter optimization unit, and a scheme output unit. The data receiving and storage unit receives the pressure field, temperature field, and concentration field distribution data transmitted by the multi-field coupling solving module, classifies and stores the data according to the fracturing construction stages, establishes a time-parameter correlation database, and uses a chain storage structure to quickly retrieve and update the data. The full life cycle modeling unit integrates the reservoir basic parameters, thermal nitrogen injection parameters, fracture evolution parameters, and plugging agent performance parameters based on the thermal nitrogen fracturing full life cycle intelligent analysis platform, constructs a full-stage mathematical model covering the fracturing preparation, crack initiation, expansion, plugging, and production stages, and clearly defines the dynamic evolution relationship between the parameters in each stage. The parameter optimization unit performs optimization calculation on the thermal nitrogen injection rate, temperature, pressure, and plugging agent slug concentration and injection timing parameters based on the full life cycle model, with the crack propagation range, plugging agent retention efficiency, and thermal nitrogen utilization efficiency as constraint conditions, and uses the gradient descent method to determine the optimal parameter combination. The scheme output unit converts the optimized parameter combination into a structured fracturing construction scheme, including the parameter control threshold and adjustment logic in each construction stage, and transmits the scheme to the fracturing execution and regulation module in the form of a data message.

[0013] Furthermore, the fracturing execution control module includes an injection parameter control unit, an equipment linkage unit, a real-time monitoring unit, and a feedback adjustment unit. The injection parameter control unit receives the fracturing parameter optimization scheme output by the intelligent analysis and processing module, analyzes the target values ​​of hot nitrogen injection rate, temperature, pressure, and plugging agent injection rate and concentration parameters in the scheme, and converts them into control signals recognizable by the actuator. The equipment linkage unit coordinates the operating status of the hot nitrogen generator, injection pump group, and plugging agent mixing device according to the control signals, and performs timing matching of hot nitrogen preparation, delivery, and plugging agent mixing and injection to ensure that multiple devices work collaboratively according to preset logic. The real-time monitoring unit collects real-time data of hot nitrogen injection pressure, temperature, flow rate, formation pressure, and fracture propagation signals through sensors deployed at the wellhead, tubing, and formation. After noise reduction processing, the data is transmitted to the feedback adjustment unit. The feedback adjustment unit compares and analyzes the real-time monitoring data with the target parameters in the optimization scheme, calculates the deviation value, generates parameter adjustment instructions based on the deviation magnitude, and sends them to the injection parameter control unit to dynamically correct the injection parameters of hot nitrogen and plugging agent.

[0014] A method for enhancing production in shallow heavy oil wells using a combined thermal-nitrogen and deep-seated diversion fracturing and plugging technique is described. This method, applied to the aforementioned system, includes the following steps: Step S1: The thermal-nitrogen parameter control module collects reservoir thickness, porosity, initial temperature, and initial pressure parameters from the shallow heavy oil well. Combined with the viscosity characteristics of heavy oil, initial values ​​for the thermal-nitrogen injection rate, temperature, and pressure are set and simultaneously sent to the fracture evolution simulation module and the fracturing execution control module. Step S2: The fracture evolution simulation module calls the thermally induced fatigue fracture network fractal evolution model, substitutes the initial thermal-nitrogen parameters and reservoir parameters for calculation, and obtains the initial simulation results of the fracture fractal dimension and propagation path. These results are then transmitted to the plugging agent displacement analysis module. Step S3: The plugging agent displacement analysis module, based on a multi-slug plugging agent dynamic displacement-retention model, uses the fracture simulation results to determine the plugging agent slug concentration and injection sequence parameters, and calculates the plugging agent displacement parameters. The concentration distribution and retention efficiency data of the sealing agent are transmitted to the multi-field coupled solution module. In step S4, the multi-field coupled solution module uses a multi-field coupled numerical solution algorithm to integrate the thermal nitrogen parameters, fracture parameters, and sealing agent parameters to construct coupled equations for the pressure field, temperature field, and concentration field. Through numerical discretization and iterative solution, the multi-field distribution data is obtained and sent to the intelligent analysis and processing module. In step S5, the intelligent analysis and processing module relies on the intelligent analysis platform for the entire life cycle of thermal nitrogen fracturing to perform full-stage fitting analysis on the multi-field distribution data, identify areas with insufficient fracture propagation or abnormal sealing agent retention, optimize the thermal nitrogen and sealing agent injection parameters, and generate an adjusted fracturing scheme. In step S6, the fracturing execution and control module receives the optimized scheme, adjusts the heating power of the thermal nitrogen generator and the discharge rate of the injection pump group, controls the concentration ratio of the sealing agent mixing device, implements fracturing operations according to the adjusted parameters, and simultaneously collects real-time construction data to feed back to each module for dynamic correction.

[0015] Beneficial Effects: This invention proposes a system and method for deep fracturing and production enhancement in shallow heavy oil wells through the coordinated operation of six modules, including a thermal nitrogen parameter control module and a fracture evolution simulation module. It relies on a thermally induced fatigue fracture network fractal evolution model to accurately capture the dynamic correlation between thermal nitrogen injection and fracture development, replacing the traditional single-parameter guidance mode. By integrating pressure, temperature, and concentration field data through a multi-field coupled numerical solution algorithm, it achieves precise prediction and control of fracture propagation paths, solving the problems of insufficient coordinated control of fracture evolution and thermal nitrogen injection, and inadequate deep reservoir stimulation, thus forming efficient seepage channels. Simultaneously, the plugging agent displacement analysis module, based on a multi-segment plugging agent dynamic displacement-retention model, establishes a correlation mechanism between plugging agent parameters and fracture and thermal nitrogen fields. Combined with the full life-cycle control of the intelligent analysis platform, it achieves precise matching of plugging agent concentration distribution and retention efficiency, avoiding thermal nitrogen crossflow caused by incomplete plugging and fracture blockage caused by excessive retention. Through multi-module data interoperability and intelligent algorithm support, the system achieves dynamic optimization of parameters throughout the fracturing process, significantly improving the production enhancement effect of shallow heavy oil wells. Attached Figure Description

[0016] Figure 1 This is a diagram showing the system module composition of the present invention;

[0017] Figure 2 This is a flowchart of the method steps of the present invention. Detailed Implementation

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

[0019] like Figure 1 As shown, the shallow heavy oil well thermal-nitrogen synergistic deep diversion fracturing and production enhancement system includes: thermal-nitrogen parameter control module, fracture evolution simulation module, plugging agent displacement analysis module, multi-field coupling solution module, intelligent analysis and processing module, and fracturing execution control module;

[0020] The hot nitrogen parameter control module outputs hot nitrogen injection rate, temperature, and pressure parameters to the fracture evolution simulation module and the fracturing execution control module;

[0021] Specifically, the thermal nitrogen parameter regulation module is implemented based on the core data of the reservoir exploration of the shallow heavy oil well in the early stage. Before the operation, the formation testing equipment is used to carry out comprehensive reservoir exploration to obtain the basic parameters such as reservoir thickness of 9-16 meters, porosity of 16%-24%, initial temperature of 28-42℃, and formation pressure of 1.3-2.8 MPa. At the same time, the viscosity data of heavy oil is measured by a rotary viscometer in the laboratory to determine the viscosity of 1200-4800 mPa・s at 55℃. Combined with these data, the initial regulation reference is determined by the parameter matching algorithm built in the module. The module is connected with the temperature control module, high-pressure pressure regulating valve and electromagnetic flow sensor of the thermal nitrogen generation system through a special line. With the help of PID closed-loop control, the temperature of the injected thermal nitrogen is accurately controlled at 210-360℃, the injection pressure is stabilized at 3.2-5.3 MPa, and the injection rate is adjusted to 0.5-1.3 m³ / min. At the same time, the embedded data acquisition terminal collects the dynamic values of the parameters at a frequency of every 10 seconds. After denoising processing by Kalman filtering algorithm, the data is transmitted to the fracture evolution simulation module and the fracturing execution regulation module through RS485 bus. The module provides the basic thermal nitrogen parameters suitable for the characteristics of the reservoir for all subsequent modules, ensures the continuous and stable state of thermal nitrogen injection through real-time dynamic regulation, avoids the influence of parameter fluctuation on the operation effect, and lays a suitable thermal dynamic foundation for fracture evolution and plugging operation.

[0022] The fracture evolution simulation module transmits the fracture fractal dimension and expansion path parameters to the plugging agent displacement analysis module after processing the rate, temperature and pressure parameters based on the thermal fatigue crack network fractal evolution model.

[0023] Specifically, after the crack evolution simulation module is started, it first completes the communication handshake with the thermal nitrogen parameter control module through the high-speed Ethernet data interface, receives the thermal nitrogen injection temperature, pressure, and rate parameters transmitted by the thermal nitrogen parameter control module, and simultaneously retrieves the previously stored reservoir rock mechanics parameters (elastic modulus 22-34 GPa, Poisson's ratio 0.26-0.34, tensile strength 2.5-4.5 MPa). Then, the built-in thermal fatigue crack network fractal evolution model is called to start the calculation process. During the calculation process, the module uses a fixed time step of 20 seconds, iteratively analyzes the correlation between the thermal stress generated by thermal nitrogen injection and the rock fracture threshold using the finite difference method, and modifies the calculation results every 5 iterations in combination with the reservoir heterogeneity data (permeability variation coefficient 0.3-0.8). After 30-50 iterations, the key parameters such as crack fractal dimension 1.2-1.8, extension length 5-13 meters, crack width 0.9-2.2 mm, and extension direction angle (30°-60° with the wellbore) are output. These parameters are standardized and packaged in JSON format, and then transmitted to the plugging agent displacement analysis module at a rate of 100 Mbps through industrial Ethernet. This breakthrough overcomes the limitations of traditional reliance on manual experience to determine the dynamic development state of cracks under the action of thermal nitrogen, accurately quantifies the dynamic development state of cracks under the action of thermal nitrogen, and provides quantitative data support for the targeted injection of plugging agents, ensuring that the crack modification range and direction meet the deep diversion requirements.

[0024] The plugging agent displacement analysis module processes the crack parameters through a multi-slug plugging agent dynamic displacement-retention model, and outputs the plugging agent concentration distribution and retention efficiency parameters to the multi-field coupling solution module.

[0025] Specifically, the plugging agent displacement analysis module obtains the complete fracture parameter data set output by the fracture evolution simulation module through the data receiving port, activates the built-in multi-slug plugging agent dynamic displacement-retention model immediately, designs a three-slug plugging agent combination scheme according to the fracture width gradient distribution and the branch characteristics of the extension path through the slug proportioning algorithm in the module, sets the pre-slug concentration to 1.6%-2.1%, the main plugging slug concentration to 3.2%-4.3%, and the post-protective slug concentration to 1.1%-1.7%, and then calculates the plugging agent displacement rate according to the hot nitrogen injection rate and the equivalent cross-sectional area of the fracture, and controls the displacement rate at 0.3-0.9 m³ / min. The module imports the reservoir permeability (50-200 mD) and porosity parameters through the built-in fluid migration simulation unit, simulates the flow front advance, wall adsorption and pore retention process of the plugging agent in the complex fracture network by using the lattice Boltzmann method, generates a concentration distribution curve at different positions once every 1 minute of simulation, and finally outputs the overall retention efficiency of 62%-88%. After the calculation is completed, the module arranges the concentration distribution data, retention efficiency value and displacement rate parameter into a structured data set, and transmits it to the multi-field coupled solving module through the special CAN data bus. The module realizes the accurate dynamic matching of the physical parameters of the plugging agent and the geometric characteristics of the fracture, avoids insufficient or excessive use of the plugging agent, and provides a reliable technical basis for efficient deep diversion plugging.

[0026] The multi-field coupled solving module integrates the hot nitrogen parameters, fracture parameters and plugging agent parameters by using a multi-field coupled numerical solving algorithm, generates pressure field, temperature field and concentration field distribution data, and transmits them to the intelligent analysis and processing module;

[0027] Specifically, the multi-field coupling solving module receives the three types of data of thermal nitrogen parameters, fracture parameters and plugging agent parameters through the multi-channel data interface at the same time. First, the parameter preprocessing process is started, the input data is uniformly formatted (converted to floating-point data) by Python script, and the range checking program is executed to automatically eliminate abnormal values such as thermal nitrogen temperature > 380℃, retention efficiency > 95%, fracture width > 3mm, etc. which are out of the reasonable range, and then a three-dimensional coupled calculation model is constructed based on the multi-field coupling numerical solving algorithm. The model takes the fractured wellbore as the coordinate center, divides the calculation area (the grid in the near-well area is densified) according to the unequal grid density of 0.15-0.35 meters, sets the time step to 15-35 seconds, solves the seepage field continuity equation, temperature field heat conduction-convection equation and concentration field diffusion-adsorption equation respectively, and obtains the spatial distribution data of pressure (2.2-4.8MPa), temperature (65-290℃) and plugging agent concentration (0.6%-4.3%) at different depths in the formation within 1.5-14 hours after injection through iterative calculation. During the solving process, the interaction of each field parameter is corrected through the preset coupling factors (temperature influence factor on viscosity 0.002-0.005, pressure influence factor on porosity 0.01-0.03), and finally the multi-field distribution data is converted into SVG format visualization atlas and transmitted to the intelligent analysis processing module through optical fiber. Systematically integrating multi-dimensional parameter information, the coupling effect law among thermal nitrogen migration, fracture expansion and plugging agent retention is clearly revealed, providing comprehensive and accurate data support for subsequent parameter optimization.

[0028] The intelligent analysis processing module processes the multi-field distribution data based on the thermal nitrogen fracturing full life cycle intelligent analysis platform, and outputs the fracturing parameter optimization scheme to the fracturing execution control module.

[0029] Specifically, the intelligent analysis processing module relies on the thermal nitrogen fracturing full life cycle intelligent analysis platform to carry out work. After the platform is started, the visual atlas transmitted by the multi-field coupling solving module is obtained through the data receiving module, the key data points are extracted by calling the data analysis program, and the data is stored in five stages of fracturing preparation, crack initiation, expansion, plugging and production. A time-parameter association database is established using a MySQL distributed storage architecture to ensure that the data read-write response time is less than 0.5 seconds. Subsequently, the platform calls the full life cycle analysis model, and the measured crack transformation volume, plugging and steering efficiency and other data are compared with the preset transformation target (crack transformation volume ≥800m³, plugging and steering efficiency ≥75%) to calculate the difference. Through threshold judgment (deviation >10% triggers early warning), the problem areas of insufficient crack expansion and uneven distribution of plugging agents are identified, and the corresponding parameter abnormal types are marked. Then, the gradient descent optimization algorithm is used to take the thermal nitrogen utilization rate (target ≥85%) and the plugging efficiency (target ≥80%) as the double objective function to iteratively optimize the thermal nitrogen injection temperature, pressure and plugging agent main slug concentration. Finally, the thermal nitrogen injection temperature is adjusted to 240-340℃, the pressure is adjusted to 3.6-5.1MPa, and the main slug concentration is adjusted to 3.5%-4.1%, a structured optimization scheme is generated and transmitted to the fracturing execution control module through the RESTful API interface. The module realizes intelligent decision-making of the whole fracturing process, breaks the limitations of static parameter setting through dynamic optimization, and ensures accurate adaptation to the dynamic changes of the reservoir throughout the operation process.

[0030] The fracturing execution control module adjusts the thermal nitrogen injection and plugging agent injection parameters according to the optimization scheme to complete the thermal nitrogen collaborative deep diversion and plugging fracturing operation of shallow heavy oil wells.

[0031] Specifically, the fracturing execution control module receives the optimization scheme output by the intelligent analysis and processing module through an industrial data interface, and immediately starts a parameter analysis unit to convert the target parameters such as temperature, pressure, and concentration in the scheme into 4-20 mA standard control signals recognizable by the equipment: by adjusting the gas supply proportional valve of the hot nitrogen generator, the heating power is accurately controlled at 85-125 kW to maintain the target injection temperature; by adjusting the frequency of the high-pressure injection pump motor (30-50 Hz) through the frequency converter, the displacement is stabilized at 0.6-1.1 m³ / min to ensure the injection pressure; by adjusting the flow of the medicament adding pump of the plugging agent mixing tank through the servo motor, the concentration error of each slug is ensured to be ≤±0.2%. At the same time, the module collects real-time construction data at a frequency of every 7 minutes through the pressure sensor (range 0-8 MPa, accuracy ±0.5% FS), temperature sensor (range 0-400℃, accuracy ±1℃) deployed at the wellhead and the acoustic wave monitor in the deep formation, and transmits the data to the data comparison unit for point-by-point calculation of the deviation from the target parameters. When the deviation of any parameter exceeds 4%, the module automatically triggers the adjustment program, generates a correction instruction and sends it to the corresponding execution equipment to dynamically correct the operating parameters, ensuring that the entire fracturing operation is strictly performed according to the optimization scheme. The implementation converts the intelligent decision-making results into precise actual construction actions, and through real-time monitoring and closed-loop feedback adjustment mechanism, it ensures the precision and long-term stability of the fracturing operation, and provides core support at the equipment level for the realization of the production increase target of shallow heavy oil wells.

[0032] Preferably, the thermal fatigue crack network fractal evolution model expression in the crack evolution simulation module is: wherein, is the crack network fractal dimension, is the initial crack fractal dimension, is the thermal-induced fractal evolution coefficient, is the hot nitrogen injection temperature, is the initial temperature of the formation, is the critical temperature difference of heavy oil, is the pressure influence coefficient, is the hot nitrogen injection pressure, is the fracturing time, is the hot nitrogen injection rate, is the viscosity of heavy oil; and the pressure field control equation of the multi-field coupling numerical solution algorithm in the multi-field coupling solving module is: wherein, is the reservoir permeability, is the fluid viscosity, is the formation pressure, is the reservoir porosity, is the fluid density, is the Dirac function of the injection point.

[0033] Specifically, when the crack evolution simulation module applies the thermal fatigue crack network fractal evolution model, the initial crack fractal dimension is determined to be 1.0-1.3 through acoustic logging and core imaging detection, the thermal fractal evolution coefficient is set to be 0.15-0.3 for sandstone reservoirs and 0.2-0.35 for limestone reservoirs, the critical temperature difference of thick oil is determined to be 40-60℃ by a rotary viscometer, the pressure influence coefficient is taken to be 0.02-0.05 according to the compaction degree of the shallow reservoir, and the time parameters of 10-30 minutes in the early stage and 60-120 minutes in the later stage are accurately corresponding to the construction stage. In the calculation, the difference between the thermal nitrogen injection temperature and the initial temperature of the formation, the ratio of the thermal nitrogen injection rate to the viscosity of thick oil are integrated, and the dynamic fractal dimension is output through specific operation to realize the quantification of the development state of the crack network. When the pressure field control equation of the multi-field coupling solution module is implemented, the reservoir permeability of 50-200 mD is obtained from the core flow experiment, the fluid viscosity of 0.02-0.5 mPa·s is integrated with the characteristics of thermal nitrogen and thick oil, the porosity of 15%-25% is obtained through core analysis, the fluid density is calculated according to the thermal nitrogen temperature and pressure, and the injection point Dirac function is located at the wellhead. Through gradient operation and time derivative term coupling, the pressure transmission law is accurately described, the limitation of single parameter analysis is broken, and the dynamic correlation between crack evolution and pressure field is established to provide accurate data support for construction regulation.

[0034] Preferably, the multi-slug plugging agent dynamic displacement-retention model expression in the plugging agent displacement analysis module is: wherein, is the plugging agent retention efficiency, is the plugging agent adsorption coefficient, is the plugging agent slug concentration, is the displacement rate influence coefficient, is the plugging agent displacement rate, is the thermal nitrogen injection rate, is the crack network fractal dimension, is the initial crack fractal dimension, is the thermal nitrogen injection temperature, is the initial temperature of the formation; and the parameter optimization objective function of the thermal nitrogen fracturing full life cycle intelligent analysis platform in the intelligent analysis processing module is: wherein, is the optimization objective value, is the weight coefficient, is the reference retention efficiency, is the initial crack fractal dimension, is the maximum injection pressure.

[0035] Specifically, the adaptability of the plugging agent performance and parameter optimization, the multi-slug dynamic displacement-retention model of the plugging agent displacement analysis module is applied, the adsorption coefficient of the particle type plugging agent is 0.3-0.6, the adsorption coefficient of the gel type is 0.4-0.7, the concentration of the pre-slug is 1.5%-2.0%, the concentration of the main slug is 3.0%-4.0%, the concentration of the post-slug is 1.0%-1.5%, the displacement rate influence coefficient is 0.2-0.5 combined with the fracture permeability, the fracture fractal dimension is the output value of the simulation module, and the temperature difference between the hot nitrogen and the formation is controlled within 150-300 DEG C. The concentration and displacement rate ratio of the plugging agent, the change rate of the fracture fractal dimension and the temperature difference influence are calculated and integrated, the retention efficiency of 60%-85% is output, and the plugging effect is quantified. In the parameter optimization objective function of the intelligent analysis and processing module, the plugging agent retention efficiency weight is 0.4-0.6, the fracture fractal dimension weight is 0.3-0.5, and the injection pressure weight is 0.1-0.2. The construction priority is adjusted, the reference retention efficiency of 70%-85% is taken from the ideal simulation value in the laboratory, and the maximum injection pressure of 5-8 MPa is set according to the pressure limit of the wellhead equipment. The optimization target value is generated by weighted calculation to guide parameter adjustment, balance the plugging effect and construction cost, avoid the problems of insufficient or excessive plugging, and improve the accuracy of the diversion plugging.

[0036] Preferably, the temperature field evolution equation of the multi-field coupling numerical solution algorithm in the multi-field coupling solution module is: wherein, is the formation rock density, is the formation rock specific heat capacity, is the formation temperature, is the fracturing time, is the formation thermal conductivity, is the hot nitrogen injection flow rate, is the hot nitrogen density, is the hot nitrogen specific heat capacity, is the hot nitrogen injection temperature; and the fracture propagation length calculation formula of the fracture evolution simulation module is: wherein, is the fracture propagation length, is the initial fracture length, is the fracture propagation coefficient, is the formation pore pressure, is the fracturing time, is the hot nitrogen injection temperature, is the initial formation temperature, is the heavy oil viscosity.

[0037] Specifically, the temperature field evolution and the accurate prediction of the crack propagation, the temperature field evolution equation of the multi-field coupling solving module is implemented, the sandstone reservoir rock density is 2400-2600 kg / m³, the specific heat capacity is 0.8-1.2 kJ / (kg·℃), the formation thermal conductivity coefficient is 1.0-2.0 W / (m·K) measured by the core thermal conductivity instrument, the hot nitrogen injection flow is 0.1-0.5 kg / s converted from the injection rate and the density, the hot nitrogen density at 200°C is 1.5-2.0 kg / m³, and the specific heat capacity is 1.0-1.2 kJ / (kg·℃). The calculation passes through the time derivative term to describe the temperature dynamic change, the gradient operation analyzes the heat conduction, the superposition of the hot nitrogen convection heat transfer term, and the output of the temperature distribution at different times. In the extension length calculation formula of the crack evolution simulation module, the initial crack length is 2-5 meters from the perforation and crack detection, the extension coefficient is 0.01-0.03 adjusted according to the rock tensile strength, the formation pore pressure is 1.0-3.0 MPa obtained from the formation test, the fracturing time is 1-3 hours, the temperature difference is 150-300°C, and the heavy oil viscosity is 500-5000 mPa·s, which are matched with the reservoir conditions. Through the coupling calculation of the pressure difference, the time index term, and the viscosity ratio, the crack length is predicted, which provides accurate basis for the injection time and range of the plugging agent, and ensures the space-time adaptation of plugging and crack propagation.

[0038] Preferably, the hot nitrogen injection rate dynamic adjustment formula of the hot nitrogen parameter regulation module is: , wherein, is the hot nitrogen injection rate, is the initial injection rate, is the crack influence coefficient, is the crack network fractal dimension, is the initial crack fractal dimension, is the time attenuation coefficient, is the fracturing time, is the maximum injection pressure, is the hot nitrogen injection pressure; the plugging agent concentration distribution equation of the plugging agent displacement analysis module is: , wherein, is the plugging agent concentration, is the displacement time, is the plugging agent displacement rate, is the plugging agent diffusion coefficient, is the plugging agent reaction rate constant, is the plugging agent retention efficiency.

[0039] Specifically, the thermal nitrogen parameters are real-time regulated with the plugging agent distribution, the injection rate of the thermal nitrogen parameter regulation module is dynamically adjusted, the initial rate is 0.5-1.2 m³ / min determined according to the borehole size, the fracture influence coefficient is 0.2-0.4 combined with the fracture propagation rate, the time attenuation coefficient is 0.01-0.03 according to the thermal nitrogen utilization efficiency, and the maximum injection pressure is 5-8 MPa set according to the equipment capacity. The initial rate is corrected according to the change rate of the fracture fractal dimension, the time attenuation term and the pressure residual coefficient are combined, and the injection rate is dynamically output to ensure the matching with the fracture propagation. In the concentration distribution equation of the plugging agent displacement analysis module, the diffusion coefficient is 1×10⁻ 9 -5×10⁻ 9 m² / s according to the plugging agent particle size and fluid viscosity, the reaction rate constant is 0.001-0.005 min⁻¹ at 40-80 ℃ formation temperature, and the retention efficiency is 60%-85% taken from the displacement model output. Through the coupling of the time derivative term, the convection term, the diffusion term and the reaction consumption term, the concentration distribution is accurately described, the dynamic cooperation of thermal nitrogen injection and plugging agent migration is realized, the heat loss caused by channeling and the seepage obstacle caused by plugging are avoided, and the construction stability is improved.

[0040] Preferably, the fracturing effect prediction formula of the thermal nitrogen fracturing full life cycle intelligent analysis platform of the intelligent analysis processing module is: , wherein, is the predicted oil production, is the base oil production, is the effect prediction function, is the fracture network fractal dimension, is the initial fracture fractal dimension, is the plugging agent retention efficiency, is the base retention efficiency, is the average formation temperature, is the initial formation temperature, is the thermal nitrogen injection temperature; the dynamic change formula of the porosity of the multi-field coupling solving module is: , wherein, is the dynamic porosity, is the initial porosity, is the thermal expansion coefficient, is the formation temperature, is the initial formation temperature, is the pressure compression coefficient, is the formation pressure, is the initial pressure, is the plugging agent influence coefficient, is the plugging agent retention efficiency, is the base retention efficiency.

[0041] Specifically, in the prediction of fracturing effect and dynamic feedback of reservoir properties, the intelligent analysis and processing module implements the fracturing effect prediction formula. The baseline oil production of 5-20 t / d is taken from the historical average of adjacent wells. The weights for fracture fractal dimension are 0.3-0.5, plugging agent retention efficiency is 0.4-0.6, and formation average temperature is 0.1-0.2, set according to the reservoir stimulation target. The baseline retention efficiency of 70%-85% and temperature difference of 150-300℃ correspond to previous parameters. The post-fracturing oil production is predicted by weighted summation of the ratios of each parameter to the baseline value, providing a basis for scheme optimization. In the porosity dynamic change formula of the multi-field coupled solution module, the initial porosity is 15%-25% based on core analysis, and the sandstone thermal expansion coefficient is 1×10⁻. 5 -3×10⁻ 5 ℃⁻¹, pressure compressibility coefficient 2×10⁻ 4 -5×10⁻ 4 MPa⁻¹, with a plugging agent influence coefficient of 0.02-0.05. The dynamic porosity is calculated by comprehensively considering the effects of increased porosity due to rising temperature, decreased porosity due to rising pressure, and plugging agent retention. This accurately reflects changes in reservoir properties, providing real-time updated basic parameters for multi-field coupled calculations, significantly improving simulation and prediction accuracy, and ensuring the reliability of scheme optimization.

[0042] Preferably, the multi-field coupled solution module includes a parameter preprocessing unit, a field equation construction unit, a numerical discretization unit, and a coupled solution unit. The parameter preprocessing unit receives the hot nitrogen injection rate, temperature, and pressure parameters output by the hot nitrogen parameter control module, the fracture fractal dimension and propagation path parameters output by the fracture evolution simulation module, and the plugging agent concentration distribution and retention efficiency parameters output by the plugging agent displacement analysis module. It performs data format matching and range verification on parameters from different sources, and after removing abnormal fluctuation data, it normalizes them into a unified input format. The field equation construction unit, based on the multi-field coupled numerical solution algorithm and combined with the physical property parameters of the shallow heavy oil reservoir, establishes seepage field equations describing the pressure transmission law. The system first establishes the temperature field equations for heat conduction and convection, and the concentration field equations for plugging agent migration, and clarifies the coupling relationships between these equations. The numerical discretization unit uses the finite element method to discretize the constructed pressure, temperature, and concentration field equations in both the spatial and temporal domains, transforming partial differential equations into a system of algebraic equations. It determines the grid density and time step to ensure discretization accuracy is adapted to the reservoir's heterogeneity. The coupled solution unit uses an iterative solution method to solve the discretized algebraic equations. By correcting the solution process using the interaction coefficients between the pressure and temperature fields and the coupling factors between the temperature and concentration fields, it obtains spatial distribution data of pressure, temperature, and concentration within the formation at different times and transmits this data to the intelligent analysis and processing module.

[0043] Specifically, the multi-field coupling solving module is developed around parameter processing, equation construction, numerical operation and coupling solving whole process, and the four units cooperatively realize the accurate integration of multi-field data. The parameter preprocessing unit receives the injection rate of 0.5-1.2 m³ / min, the injection temperature of 210-360 ℃, the injection pressure of 3.2-5.3 MPa output by the thermal nitrogen parameter regulation module, the fractal dimension of 1.2-1.8, the extension path of 5-13 meters output by the fracture evolution simulation module, and the concentration distribution of 0.6%-4.3%, the retention efficiency of 62%-88% output by the plugging agent displacement analysis module. Different source parameters are first converted to floating point data by a data format converter, then range checking is performed by 3σ criterion, abnormal values exceeding ±3 times of standard deviation are removed, and finally the input format of 1000×1000 matrix is normalized. The field equation construction unit combines the parameters such as rock density of 2400-2600 kg / m³, thermal conductivity coefficient of 1.0-2.0 W / (m·K) of shallow heavy oil reservoir, and establishes the equations of seepage field, temperature field and concentration field respectively, and determines the interaction coefficient of pressure field and temperature field of 0.002-0.005 and the coupling factor of temperature field and concentration field of 0.01-0.03. The numerical discrete unit adopts the finite element method, divides the calculation area according to the grid spacing of 0.15-0.35 meters, sets the time step of 15-35 seconds, and converts the partial differential equation into algebraic equation group. The coupling solving unit adopts Gauss-Seidel iteration method for solving, and the iteration accuracy is controlled within 1×10⁻ 6 The multi-field distribution data of 1.5-14 hours is output by the coupling item correction calculation process, which breaks the limitation of single field analysis and provides comprehensive multi-field correlation data support for subsequent regulation.

[0044] Preferably, the intelligent analysis processing module comprises a data receiving and storing unit, a full life cycle modeling unit, a parameter optimization unit, and a scheme output unit. The data receiving and storing unit receives pressure field, temperature field, and concentration field distribution data transmitted by the multi-field coupling solving module, classifies and stores the data according to the fracturing construction stages, establishes a time-parameter correlation database, and uses a chain storage structure for quick data retrieval and updating. The full life cycle modeling unit relies on the thermal nitrogen fracturing full life cycle intelligent analysis platform, integrates reservoir basic parameters, thermal nitrogen injection parameters, fracture evolution parameters, and plugging agent performance parameters, constructs a full-stage mathematical model covering fracturing preparation, crack initiation, expansion, plugging, and production, and clearly defines the dynamic evolution relationship of parameters at each stage. The parameter optimization unit, based on the full life cycle model, uses the crack expansion range, plugging agent retention efficiency, and thermal nitrogen utilization efficiency as constraint conditions, uses the gradient descent method to perform optimization calculation on the thermal nitrogen injection rate, temperature, pressure, and plugging agent slug concentration, and injection timing parameters, and determines the optimal parameter combination. The scheme output unit converts the optimized parameter combination into a structured fracturing construction scheme, including parameter control thresholds and adjustment logic at each construction stage, and transmits the scheme to the fracturing execution and control module in the form of data messages.

[0045] Specifically, the intelligent analysis processing module realizes intelligent management and control of the full life cycle of fracturing through four units. The data receiving and storing unit receives 2.2-4.8 MPa pressure, 65-290°C temperature, and 0.6%-4.3% concentration distribution data transmitted by the multi-field coupling solving module through optical fibers, classifies the data into five stages of preparation, crack initiation, expansion, plugging, and production, uses a MySQL distributed database for storage, and has a data writing rate of ≥100 MB / s and millisecond-level retrieval through chain indexing. The full life cycle modeling unit integrates basic parameters such as reservoir porosity of 15%-25% and permeability of 50-200 mD, as well as thermal nitrogen, crack, and plugging agent related parameters, constructs a full-stage mathematical model containing more than 1000 nodes, clearly defines the dynamic correlation function of parameters at each stage, and has a model fitting goodness of ≥0.95. The parameter optimization unit uses crack modification volume of ≥800 m³ and plugging diversion efficiency of ≥75% as constraints, uses the gradient descent algorithm to optimize thermal nitrogen injection parameters and plugging agent parameters, controls the number of iterations to be 50-100 times, and has an optimization accuracy of ≤0.1%. The scheme output unit converts the optimal parameter combination into a structured scheme containing more than 20 control thresholds, transmits the scheme through an API interface in JSON format, has a transmission delay of ≤1 second, realizes intelligent conversion from data to decision-making, and significantly improves the accuracy and efficiency of parameter optimization.

[0046] Preferably, the fracturing execution regulation module comprises an injection parameter control unit, a device linkage unit, a real-time monitoring unit, and a feedback regulation unit. The injection parameter control unit receives the fracturing parameter optimization scheme output by the intelligent analysis and processing module, analyzes the target values of the hot nitrogen injection rate, temperature, pressure, and the plugging agent injection rate and concentration parameters in the scheme, and converts them into control signals recognizable by the execution mechanism. The device linkage unit coordinates the operating states of the hot nitrogen generation device, the injection pump group, and the plugging agent mixing device according to the control signals, performs time sequence matching of hot nitrogen preparation, transportation, and plugging agent mixing and injection, and ensures that multiple devices work collaboratively according to the preset logic. The real-time monitoring unit collects real-time data of hot nitrogen injection pressure, temperature, flow rate, and formation pressure and fracture propagation signals through sensors deployed at the wellhead, pipe column, and formation, transmits the data to the feedback regulation unit after noise reduction processing, and transmits the data to the feedback regulation unit after noise reduction processing. The feedback regulation unit compares and analyzes the real-time monitoring data with the target parameters in the optimization scheme, calculates the deviation value, generates a parameter adjustment instruction according to the deviation size, and sends it to the injection parameter control unit to dynamically correct the injection parameters of hot nitrogen and plugging agent.

[0047] Specifically, the fracturing execution regulation module converts the optimization scheme into precise construction actions through four units. The injection parameter control unit receives the optimization scheme output by the intelligent analysis and processing module, analyzes the target values of 240-340℃ temperature, 3.6-5.1MPa pressure, and 0.6-1.1m³ / min rate, and converts them into 4-20mA control signals through a D / A converter with a signal accuracy of ≤±0.1mA. The device linkage unit coordinates the operation of the hot nitrogen generation furnace, the injection pump group, and the mixing device according to the control signals. The hot nitrogen generation furnace heating power regulation range is 85-125kW, the injection pump group displacement adjustment response time is ≤2 seconds, and the mixing device stirring speed is 0-1500r / min, realizing collaborative work with a multi-device time sequence deviation of ≤50ms. The real-time monitoring unit collects data through wellhead pressure sensors (range 0-8MPa, accuracy ±0.5%FS), temperature sensors (range 0-400℃, accuracy ±1℃), and formation acoustic monitors. Data acquisition and noise reduction processing are completed every 7 minutes, and the data signal-to-noise ratio is ≥40dB. The feedback regulation unit compares the monitoring data with the target parameters, calculates the deviation value, generates an adjustment instruction within 1 second when the deviation exceeds 4%, and corrects the device parameters through a PID controller with an adjustment accuracy of ≤±2%. A closed-loop regulation system is constructed to ensure that the construction process is highly consistent with the optimization scheme and to improve the stability and effectiveness of fracturing operations.

[0048] The thermal fatigue crack network fractal evolution model is a quantitative tool for describing the fracture development law under the action of thermal nitrogen in shallow heavy oil wells. By integrating thermal nitrogen parameters and reservoir characteristics, the fractal characteristics and dynamic expansion process of the fracture network are accurately characterized. Its implementation relies on the fracture evolution simulation module, which first receives the 0.5-1.2 m³ / min injection rate, 210-360 ℃ injection temperature, and 3.2-5.3 MPa injection pressure output by the thermal nitrogen parameter control module, and combines with the mechanical parameters of the reservoir rock such as 22-34 GPa elastic modulus and 0.26-0.34 Poisson's ratio to iteratively calculate the correlation between thermal stress and rock failure with a time step of 20 seconds. Every 5 iterations incorporates the permeability variation coefficient 0.3-0.8 correction results, and finally outputs parameters such as 1.2-1.8 fractal dimension and 5-13 meters of extension length. The role of this model is to break through the limitations of traditional empirical judgment, quantify the fractal evolution law of cracks under the effect of thermal fatigue, and provide accurate crack geometric parameters for subsequent plugging agent injection. Establish a quantitative correlation between thermal nitrogen injection and fracture development, solve the problem of inaccurate fracture expansion prediction in traditional technology, provide a target basis for deep diversion plugging, and improve the accuracy of reservoir reconstruction.

[0049] The multi-slug plugging agent dynamic displacement-retention model is a professional tool for simulating the migration and retention behavior of plugging agents in complex fracture networks, aiming to achieve accurate adaptation of plugging agent parameters to fracture characteristics. When implemented, the plugging agent displacement analysis module receives the fracture fractal dimension and extension path parameters output by the fracture evolution simulation module, and combines with the reservoir 50-200 mD permeability and 15%-25% porosity to first design a concentration scheme of 1.6%-2.1% pre-slug, 3.2%-4.3% main slug, and 1.1%-1.7% post-slug through slug ratio algorithm, calculate the 0.3-0.9 m³ / min displacement rate, and then simulate the flow and adsorption process using the lattice Boltzmann method, update the concentration distribution every 1 minute, and output the 62%-88% retention efficiency. The role of this model is to accurately predict the concentration distribution and retention effect of different slug plugging agents in fractures, avoiding under-plugging or over-plugging. Establish a dynamic correlation mechanism between multi-slug plugging agents and fractures, thermal nitrogen fields, solve the problem of poor adaptability of traditional plugging agents, ensure the effective retention of plugging agents in the target area, achieve efficient deep diversion, and reduce the loss of thermal nitrogen channeling.

[0050] The multi-field coupling numerical solution algorithm is a numerical calculation method for integrating pressure field, temperature field and concentration field data to analyze the interaction rules of the three fields. The multi-dimensional parameter collaborative analysis is realized through the multi-field coupling solution module. The implementation needs to go through four steps: the parameter preprocessing unit adopts the 3σ criterion to check the thermal nitrogen, fracture and plugging agent parameters, eliminate abnormal values and normalize them into a 1000x1000 matrix; the field equation construction unit establishes the equation combined with the rock density of 2400-2600 kg / m³, the thermal conductivity coefficient of 1.0-2.0 W / (m·K), and the pressure-temperature coupling coefficient of 0.002-0.005; the numerical discretization unit converts the equation into an algebraic equation set according to the grid of 0.15-0.35 meters and the step of 15-35 seconds; the coupling solution unit uses the Gauss-Seidel iteration method for solution, and the precision is controlled within 1x10⁻ 6 The role of this algorithm is to reveal the coupling rules of thermal nitrogen migration, fracture expansion and plugging agent retention, and output multi-field distribution data for 1.5-14 hours. It breaks the limitations of single-field analysis, provides comprehensive data support for intelligent decision-making, solves the problem of lack of multi-factor collaborative analysis in traditional technology, and improves the simulation accuracy and scientificity of regulation.

[0051] The thermal nitrogen fracturing full life cycle intelligent analysis platform is an intelligent management and control carrier that organizes the data processing, modeling optimization and scheme output of the whole fracturing process. It realizes intelligent decision-making in all stages relying on the intelligent analysis and processing module. Its implementation depends on four units: the data receiving and storage unit stores multi-field data according to the five stages of construction through the MySQL database, and the writing rate is ≥100 MB / s; the full life cycle modeling unit integrates reservoir basic parameters and dynamic parameters to build a model with more than 1000 nodes, and the goodness of fit is ≥0.95; the parameter optimization unit takes the fracture modification volume ≥800 m³ and the plugging efficiency ≥75% as constraints, and performs 50-100 times of gradient descent iteration optimization, with an accuracy of ≤0.1%; the scheme output unit converts the optimal parameters into a scheme with more than 20 control thresholds and transmits it in JSON format, with a delay of ≤1 second. The role of this platform is to realize the intelligent management and control of the whole process from data acquisition to scheme output, dynamically optimize the thermal nitrogen and plugging agent parameters. It replaces the traditional empirical operation mode, builds a data-driven intelligent decision-making system, solves the drawbacks of static setting of fracturing parameters, ensures accurate adaptation of the operation to the changes in the reservoir, and greatly improves the stimulation effect.

[0052] For example Figure 2As shown, the shallow heavy oil well heat nitrogen cooperative deep diversion plugging and fracturing stimulation method is applied to a shallow heavy oil well heat nitrogen cooperative deep diversion plugging and fracturing stimulation system, and includes the following steps: step S1, the heat nitrogen parameter control module is used to collect the reservoir thickness, porosity, initial temperature and initial pressure parameters of the shallow heavy oil well, set the initial values of the heat nitrogen injection rate, temperature and pressure according to the viscosity characteristics of the heavy oil, and synchronously send them to the fracture evolution simulation module and the fracturing execution control module; step S2, the fracture evolution simulation module calls the heat-induced fatigue fracture network fractal evolution model, substitutes the heat nitrogen initial parameters and the reservoir parameters to calculate the initial simulation results of the fracture fractal dimension and the expansion path, and transmits the results to the plugging agent displacement analysis module; step S3, the plugging agent displacement analysis module uses the fracture simulation results to determine the plugging agent slug concentration and injection sequence parameters based on the multi-slug plugging agent dynamic displacement-retention model, calculates the plugging agent concentration distribution and retention efficiency data, and transmits them to the multi-field coupled solution module; step S4, the multi-field coupled solution module uses a multi-field coupled numerical solution algorithm, integrates the heat nitrogen parameters, fracture parameters and plugging agent parameters to construct the coupled equations of the pressure field, temperature field and concentration field, obtains the multi-field distribution data through numerical discretization and iterative solution, and sends them to the intelligent analysis and processing module; step S5, the intelligent analysis and processing module relies on the heat nitrogen fracturing full life cycle intelligent analysis platform to perform full-stage fitting analysis on the multi-field distribution data, identifies the areas of insufficient fracture expansion or abnormal plugging agent retention, optimizes the heat nitrogen and plugging agent injection parameters, and generates an adjusted fracturing scheme; step S6, the fracturing execution control module receives the optimized scheme, adjusts the heating power of the heat nitrogen generating equipment and the displacement of the injection pump set, controls the concentration ratio of the plugging agent mixing device, implements the fracturing operation according to the adjusted parameters, and simultaneously collects real-time construction data to feed back to each module for dynamic correction.

[0053] The shallow heavy oil well heat nitrogen cooperative deep diversion plugging and fracturing stimulation system and method deeply analyze the internal correlation between the heat nitrogen temperature, pressure, injection rate and fracture development by closely linking the heat nitrogen parameter control module and the fracture evolution simulation module, relying on the heat-induced fatigue fracture network fractal evolution model, and no longer relying on a single parameter to guide the construction. The multi-field coupled numerical solution algorithm is used to integrate and analyze the pressure field, temperature field and concentration field data, which can accurately predict the fracture expansion path and fractal characteristics, adjust the heat nitrogen injection parameters, ensure the directional extension of the fracture to the deep reservoir, and solve the problems of fracture expansion deviating from the expectation and insufficient deep reconstruction in the traditional technology, thereby building an efficient channel for heavy oil flow.

[0054] In the aspect of dynamic adaptation of the plugging agent, the technology significantly optimizes the poor effect of traditional plugging. The plugging agent displacement analysis module, based on a multi-slug plugging agent dynamic displacement-retention model, deeply correlates the plugging agent parameters with the fracture evolution parameters and the thermal nitrogen field distribution data, and accurately calculates the plugging agent concentration distribution and retention efficiency. With the whole life cycle regulation of the intelligent analysis and processing module, the parameters such as the plugging agent slug concentration and injection sequence can be adjusted in real time according to the fracture development dynamics and the multi-field distribution changes, so as to realize the accurate retention of the plugging agent in the target fracture area, avoid the thermal nitrogen channeling caused by incomplete plugging in the traditional technology, and prevent the effective fracture blockage caused by excessive retention, thus ensuring the effect of the diversion plugging.

[0055] The synergistic linkage and intelligent regulation capability of the system as a whole further strengthen the technical advantages and make up for the traditional defects. The six modules form a closed-loop regulation system through data interconnection, and the intelligent analysis platform for the whole life cycle of thermal nitrogen fracturing integrates the fracturing data in the whole stage to realize the whole-process optimization from parameter setting, simulation analysis to construction regulation. Compared with the traditional experience-based operation mode, the system can dynamically correct the thermal nitrogen injection and plugging agent injection parameters, accurately adapt to the heterogeneity of shallow heavy oil reservoirs and the rheological properties of heavy oil, and realize the dynamic optimization of fracturing parameters through the synergistic effect of multiple modules and intelligent algorithms, thus greatly improving the stimulation effect and completely changing the status quo of the lack of system integration and accurate regulation in the traditional technology.

[0056] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection", "link", "fixation" should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integrally connected; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present application can be understood through specific circumstances.

[0057] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various equivalent changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalent ranges.

Claims

1. A shallow heavy oil well thermal-nitrogen synergistic deep diversion fracturing and production enhancement system, characterized in that, include: The system comprises a thermo-nitrogen parameter control module, a fracture evolution simulation module, a plugging agent displacement analysis module, a multi-field coupled solution module, an intelligent analysis and processing module, and a fracturing execution control module. The thermo-nitrogen parameter control module outputs thermo-nitrogen injection rate, temperature, and pressure parameters to the fracture evolution simulation module and the fracturing execution control module. The fracture evolution simulation module processes the rate, temperature, and pressure parameters based on a thermally induced fatigue fracture network fractal evolution model and then transmits the fracture fractal dimension and propagation path parameters to the plugging agent displacement analysis module. The plugging agent displacement analysis module processes fracture parameters using a multi-segment plugging agent dynamic displacement-retention model and outputs plugging agent concentration distribution and retention efficiency parameters to the multi-field coupled solution module. The multi-field coupled solution module integrates thermo-nitrogen parameters, fracture parameters, and plugging agent parameters using a multi-field coupled numerical solution algorithm to generate pressure field, temperature field, and concentration field distribution data, which are then transmitted to the intelligent analysis and processing module. The intelligent analysis and processing module processes the multi-field distribution data based on the thermo-nitrogen fracturing full lifecycle intelligent analysis platform and outputs an optimized fracturing parameter scheme to the fracturing execution control module. The fracturing execution control module adjusts the parameters of hot nitrogen injection and plugging agent injection according to the optimization plan to complete the hot nitrogen coordinated deep diversion plugging fracturing operation in shallow heavy oil wells.

2. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing and fracturing production enhancement system according to claim 1, characterized in that, The expression for the fractal evolution model of the thermal fatigue crack network in the crack evolution simulation module is as follows: ,in, Let fractal dimension be the crack network. Let fractal dimension be the initial crack. The thermally induced fractal evolution coefficient, The temperature for hot nitrogen injection. The initial temperature of the formation. This refers to the critical temperature difference of heavy oil. This is the pressure influence coefficient. Pressure is applied to inject hot nitrogen. For fracturing time, For the hot nitrogen injection rate, For the viscosity of heavy oil; the pressure field governing equation of the multi-field coupled numerical solution algorithm in the multi-field coupled solution module is: ,in, For reservoir permeability, For fluid viscosity, For formation pressure, For reservoir porosity, For fluid density, The Dirac function is used for injection points.

3. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing fracturing and production enhancement system according to claim 1, characterized in that, The expression for the dynamic displacement-retention model of the multi-segment plugging agent in the plugging agent displacement analysis module is as follows: ,in, For the retention efficiency of the sealing agent. The adsorption coefficient of the plugging agent. This refers to the slug concentration of the plugging agent. The displacement rate influence coefficient. The displacement rate of the plugging agent. For the hot nitrogen injection rate, Let fractal dimension be the crack network. Let fractal dimension be the initial crack. The temperature for hot nitrogen injection. The initial formation temperature; the objective function for parameter optimization of the intelligent analysis platform for the entire lifecycle of thermal-nitrogen fracturing in the intelligent analysis and processing module is: ,in, To optimize the target value, These are the weighting coefficients. Based on the baseline retention efficiency, Let fractal dimension be the initial crack. This is the maximum injection pressure.

4. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing and fracturing production enhancement system according to claim 1, characterized in that, The temperature field evolution equation in the multi-field coupled numerical solution algorithm of the multi-field coupled solution module is as follows: ,in, Density of the strata rocks The specific heat capacity of the strata rocks, For the formation temperature, For fracturing time, The thermal conductivity of the formation, For the hot nitrogen injection flow rate, For thermal nitrogen density, For the specific heat capacity of nitrogen, The temperature for hot nitrogen injection is given; the formula for calculating the crack propagation length in the crack evolution simulation module is: ,in, For the crack propagation length, The initial crack length is... Let be the crack propagation coefficient. Formation pore pressure, For fracturing time, The temperature for hot nitrogen injection. The initial temperature of the formation. This refers to the viscosity of heavy oil.

5. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing and fracturing production enhancement system according to claim 1, characterized in that, The dynamic adjustment formula for the hot nitrogen injection rate of the hot nitrogen parameter control module is as follows: ,in, For the hot nitrogen injection rate, The initial injection rate, The crack influence coefficient is... Let fractal dimension be the crack network. Let fractal dimension be the initial crack. The time decay coefficient, For fracturing time, For maximum injection pressure, The hot nitrogen injection pressure is used; the plugging agent concentration distribution equation for the plugging agent displacement analysis module is: ,in, The concentration of the plugging agent. To replace time, The displacement rate of the plugging agent. The diffusion coefficient of the plugging agent is . Let be the reaction rate constant of the plugging agent. The retention efficiency of the sealing agent.

6. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing and fracturing production enhancement system according to claim 1, characterized in that, The fracturing effect prediction formula of the intelligent analysis and processing module's intelligent analysis platform for the entire life cycle of thermal nitrogen fracturing is as follows: ,in, To predict oil production, Based on the benchmark oil production, For effect prediction function, Let fractal dimension be the crack network. Let fractal dimension be the initial crack. For the retention efficiency of the sealing agent. Based on the baseline retention efficiency, The average temperature of the formation. The initial temperature of the formation. The temperature for hot nitrogen injection; the dynamic porosity variation formula of the multi-field coupled solution module is: ,in, For dynamic porosity, Initial porosity, The coefficient of thermal expansion is For the formation temperature, The initial temperature of the formation. The coefficient of compressibility is the pressure compressibility. For formation pressure, As the initial pressure, The influence coefficient of the plugging agent. For the retention efficiency of the sealing agent. The baseline retention efficiency.

7. The shallow heavy oil well thermal-nitrogen synergistic deep diversion fracturing and production enhancement system according to claim 1, characterized in that, The multi-field coupled solution module includes a parameter preprocessing unit, a field equation construction unit, a numerical discretization unit, and a coupled solution unit. The parameter preprocessing unit receives the hot nitrogen injection rate, temperature, and pressure parameters output by the hot nitrogen parameter control module, the fracture fractal dimension and propagation path parameters output by the fracture evolution simulation module, and the plugging agent concentration distribution and retention efficiency parameters output by the plugging agent displacement analysis module. It performs data format matching and range verification on parameters from different sources, and after removing abnormal fluctuation data, it normalizes them into a unified input format. The field equation construction unit, based on the multi-field coupled numerical solution algorithm and combined with the physical property parameters of shallow heavy oil reservoirs, establishes seepage field equations describing pressure transmission, temperature field equations describing heat conduction and convection, and concentration field equations describing plugging agent migration, and clarifies the coupling correlation terms between each field equation. The numerical discretization unit uses the finite element method to discretize the constructed pressure field, temperature field, and concentration field equations in the spatial and time domains, transforming partial differential equations into a system of algebraic equations, determining the grid density and time step, and ensuring that the discretization accuracy is adapted to the heterogeneous characteristics of the reservoir. The coupled solution unit uses an iterative solution method to solve the discretized algebraic equations. By correcting the solution process through the interaction coefficient between the pressure field and the temperature field and the coupling factor between the temperature field and the concentration field, it obtains the spatial distribution data of pressure, temperature and concentration in the formation at different times and transmits them to the intelligent analysis and processing module.

8. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing fracturing and production enhancement system according to claim 1, characterized in that, The intelligent analysis and processing module includes a data receiving and storage unit, a full lifecycle modeling unit, a parameter optimization unit, and a scheme output unit. The data receiving and storage unit receives pressure field, temperature field, and concentration field distribution data transmitted from the multi-field coupled solution module, classifies and stores the data according to the fracturing construction stage, establishes a time-parameter correlation database, and uses a chain storage structure for rapid data retrieval and updating. The full lifecycle modeling unit, relying on the hot nitrogen fracturing full lifecycle intelligent analysis platform, integrates reservoir basic parameters, hot nitrogen injection parameters, fracture evolution parameters, and plugging agent performance parameters to construct a comprehensive analysis system covering fracturing preparation, fracturing lifecycle, and fracturing process. A mathematical model for the entire process of fracture initiation, propagation, plugging, and production is established, clarifying the dynamic evolution relationship of parameters at each stage. The parameter optimization unit, based on the full life-cycle model, uses the gradient descent method to optimize parameters such as hot nitrogen injection rate, temperature, pressure, plugging agent slug concentration, and injection timing, constrained by fracture propagation range, plugging agent retention efficiency, and hot nitrogen utilization efficiency, to determine the optimal parameter combination. The scheme output unit transforms the optimized parameter combination into a structured fracturing construction scheme, including parameter control thresholds and adjustment logic for each construction stage, and transmits it to the fracturing execution and control module in the form of data packets.

9. The shallow heavy oil well thermal-nitrogen synergistic deep diversion sealing and fracturing production enhancement system according to claim 1, characterized in that, The fracturing execution control module includes an injection parameter control unit, an equipment linkage unit, a real-time monitoring unit, and a feedback adjustment unit. The injection parameter control unit receives the fracturing parameter optimization scheme output by the intelligent analysis and processing module, analyzes the target values ​​of hot nitrogen injection rate, temperature, pressure, and plugging agent injection rate and concentration parameters in the scheme, and converts them into control signals that can be recognized by the actuator. The equipment linkage unit coordinates the operating status of the hot nitrogen generator, injection pump group, and plugging agent mixing device according to the control signals, and performs timing matching of hot nitrogen preparation, delivery, and plugging agent mixing and injection to ensure that multiple devices work together according to preset logic. The real-time monitoring unit collects real-time data on hot nitrogen injection pressure, temperature, flow rate, formation pressure, and fracture propagation signals through sensors deployed at the wellhead, tubing, and formation. After noise reduction processing, the data is transmitted to the feedback adjustment unit. The feedback adjustment unit compares and analyzes the real-time monitoring data with the target parameters in the optimization scheme, calculates the deviation value, generates parameter adjustment commands based on the deviation magnitude, and sends them to the injection parameter control unit to dynamically correct the injection parameters of hot nitrogen and plugging agent.

10. A method for enhancing production in shallow heavy oil wells through combined thermal and nitrogen-based deep diversion fracturing and plugging, characterized in that: This method is applied to the shallow heavy oil well thermal-nitrogen synergistic deep diversion fracturing and production enhancement system described in claim 1, comprising the following steps: Step S1: Collecting reservoir thickness, porosity, initial temperature, and initial pressure parameters of the shallow heavy oil well through the thermal-nitrogen parameter control module, setting initial values ​​for thermal-nitrogen injection rate, temperature, and pressure in conjunction with the viscosity characteristics of heavy oil, and simultaneously sending them to the fracture evolution simulation module and the fracturing execution control module; Step S2: The fracture evolution simulation module calls the thermal fatigue fracture network fractal evolution model, substitutes the initial thermal-nitrogen parameters and reservoir parameters for calculation, obtains the initial simulation results of fracture fractal dimension and propagation path, and transmits the results to the plugging agent displacement analysis module; Step S3: The plugging agent displacement analysis module, based on the multi-segment plugging agent dynamic displacement-retention model, uses the fracture simulation results to determine the plugging agent segment concentration and injection sequence parameters, and calculates the plugging agent concentration distribution and retention. Efficiency data is transmitted to the multi-field coupled solution module; Step S4: The multi-field coupled solution module uses a multi-field coupled numerical solution algorithm to integrate hot nitrogen parameters, fracture parameters, and plugging agent parameters to construct coupled equations for pressure, temperature, and concentration fields. Multi-field distribution data is obtained through numerical discretization and iterative solving and sent to the intelligent analysis and processing module; Step S5: The intelligent analysis and processing module, relying on the intelligent analysis platform for the entire lifecycle of hot nitrogen fracturing, performs full-stage fitting analysis on the multi-field distribution data, identifies areas of insufficient fracture propagation or abnormal plugging agent retention, optimizes the hot nitrogen and plugging agent injection parameters, and generates an adjusted fracturing scheme; Step S6: The fracturing execution and control module receives the optimized scheme, adjusts the heating power of the hot nitrogen generator and the discharge rate of the injection pump group, controls the concentration ratio of the plugging agent mixing device, and implements fracturing operations according to the adjusted parameters. Simultaneously, real-time construction data is collected and fed back to each module for dynamic correction.

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