Shallow heavy oil reservoir heat nitrogen foam multi-stage slug cooperative displacement method and system

Abstract

The present application discloses a shallow heavy oil reservoir heat nitrogen foam multi-stage slug cooperative displacement method and system, the method comprising constructing a basic database, calling a multi-stage slug dynamic adaptive optimization algorithm to obtain an initial slug parameter combination, inputting a pore scale interface competition dynamic network model to simulate displacement efficiency, collecting actual data through a heat nitrogen foam displacement real-time monitoring and intelligent control platform and comparing, adjusting parameters and updating model boundary conditions until the deviation meets the standard, injecting the slug according to the optimized parameters and continuously monitoring feedback. The system includes a heat nitrogen foam preparation and injection unit, a reservoir parameter monitoring unit, an algorithm operation and model processing unit, a data storage and management unit, a system control and instruction output unit, a data interaction and visualization unit, and each unit works cooperatively. The method and system realize dynamic optimization of slug parameters, real-time control of displacement process, improve the adaptability of slug and reservoir, reduce gas channeling, and improve the recovery rate and development efficiency of shallow heavy oil reservoirs.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fire alarm, hardware and algorithm of fire sensor, and particularly relates to a method and system for multi-stage slug cooperative displacement of thermal nitrogen foam in shallow heavy oil reservoir. BACKGROUND

[0002] As an important oil and gas resource, the development process of shallow heavy oil reservoir is significantly affected by high viscosity of heavy oil and reservoir heterogeneity, and it is difficult to achieve efficient recovery by conventional displacement methods. With the development of oil and gas development technology, thermal nitrogen foam displacement has become an important direction for the development of shallow heavy oil reservoirs due to its dual functions of viscosity reduction and profile control. However, the reservoir pore structure of shallow heavy oil reservoir is complex, and the fluid interface interaction is strong. The adaptability of slug parameters and reservoir conditions directly affects the displacement effect during the injection process of thermal nitrogen foam. Moreover, the thermal nitrogen gas is prone to channeling during the displacement process, and the foam stability is easily affected by the reservoir environment. Therefore, it is urgent to build a cooperative displacement technology system that takes into account parameter optimization, process simulation, real-time monitoring and intelligent control, in order to solve the problems of low recovery and insufficient development efficiency of shallow heavy oil reservoirs.

[0003] The existing technology in the field of thermal nitrogen foam displacement in shallow heavy oil reservoirs has two significant shortcomings: on the one hand, slug parameter optimization relies mainly on experience or simple models, and there is no optimization mechanism that dynamically correlates with the interface competition characteristics of the reservoir pore scale. Therefore, it is not possible to adjust the injection pressure, rate, size and alternating period of the slug in real time according to the changes in reservoir conditions during the displacement process, which leads to poor adaptability of the slug to the reservoir, easy gas channeling or premature foam collapse, and affects the displacement efficiency. On the other hand, there is a lack of an integrated platform that integrates real-time monitoring and dynamic simulation. The data collected by existing monitoring equipment is difficult to quickly interact with the pore-scale displacement simulation model, and it is not possible to timely correct the simulation deviation and adjust the control instructions. Therefore, it is not possible to achieve dynamic feedback and intelligent adjustment of the displacement process, and it is difficult to meet the needs of efficient development under complex conditions in shallow heavy oil reservoirs. SUMMARY

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present application provides a method and system for multi-stage slug cooperative displacement of thermal nitrogen foam in shallow heavy oil reservoirs.

[0005] The technical scheme adopted by the present application is a shallow heavy oil reservoir hot nitrogen foam multi-stage slug cooperative displacement method, comprising the following steps: S1: based on the geological parameters and fluid physical property parameters of the shallow heavy oil reservoir, a basic database of a hot nitrogen foam displacement real-time monitoring and intelligent control platform is constructed, the geological parameters include reservoir porosity, permeability, interlayer distribution characteristics, and the fluid physical property parameters include heavy oil viscosity, density, hot nitrogen gas diffusion coefficient and foam stability parameters; S2: a multi-stage slug dynamic adaptive optimization algorithm is called to perform preliminary iterative calculation on the injection pressure, injection rate, slug size and slug alternating period parameters of the hot nitrogen foam multi-stage slug, and an initial slug parameter combination is obtained; S3: the initial slug parameter combination is input into a pore-scale interface competition dynamic network model to simulate the migration path, interface tension change and gas channeling degree of the hot nitrogen foam in the reservoir pores, and output simulation displacement efficiency data; S4: through the hot nitrogen foam displacement real-time monitoring and intelligent control platform, pressure response signals, temperature distribution data and produced liquid component data in the actual displacement process are collected, and are compared and analyzed with the simulation displacement efficiency data output in step S3; S5: according to the comparison and analysis results, the slug parameter combination is adjusted through the multi-stage slug dynamic adaptive optimization algorithm, the boundary conditions of the pore-scale interface competition dynamic network model are updated synchronously, and steps S3-S4 are repeatedly executed until the deviation between the simulation data and the actual monitoring data meets a preset threshold; S6: based on the optimized slug parameter combination, the hot nitrogen foam injection equipment is controlled to inject multi-stage hot nitrogen foam slugs into the shallow heavy oil reservoir according to the set injection sequence and injection time length, and at the same time, the displacement process data are continuously collected by the hot nitrogen foam displacement real-time monitoring and intelligent control platform and fed back to the multi-stage slug dynamic adaptive optimization algorithm.

[0006] Further, the multi-stage slug dynamic adaptive optimization algorithm calculates the slug parameter optimization value using the following expression: wherein, is the optimized slug injection pressure, is the slug stage number, is the weight coefficient of the mth slug, is the initial injection pressure of the mth slug, is the displacement efficiency coefficient of the mth slug, is the pressure compensation coefficient, is the maximum pressure difference of the reservoir, is the flow loss coefficient, is the hot nitrogen gas channeling loss amount, is the reservoir sensitivity coefficient, is the reservoir remaining oil saturation.

[0007] ​​​Further, the pore-scale interface competition dynamic network model calculates the change of foam interfacial tension by the following expression: wherein, is the dynamic interfacial tension, is the initial interfacial tension, is the interfacial tension decay coefficient, is the displacement time, is the reservoir porosity, is the gas phase saturation, is the thermal nitrogen gas viscosity, is the fluid flow coefficient, is the heavy oil viscosity, is the oil phase saturation, is the surfactant influence coefficient, is the surfactant concentration, is the foam volume, is the pore interface area.

[0008] Further, the thermal nitrogen foam displacement real-time monitoring and intelligent control platform calculates the real-time displacement efficiency by the following expression: wherein, is the real-time displacement efficiency, is the cumulative oil production, is the heavy oil density, is the crude oil volume coefficient, is the displacement time, is the thermal nitrogen foam injection flow rate, is the produced fluid flow rate, is the thermal nitrogen gas density, is the gas volume coefficient, is the original geological reserves, is the monitoring data correction coefficient.

[0009] Further, the multi-stage slug dynamic adaptation optimization algorithm adjusts the slug alternating period by the following expression: wherein, is the slug alternating period, is the reservoir pore volume, is the reservoir porosity, is the current oil phase saturation, is the target oil phase saturation, is the thermal nitrogen foam injection flow rate, is the slug volume utilization rate, is the temperature influence coefficient, is the reservoir temperature change value, is the difference between the injected thermal nitrogen temperature and the original reservoir temperature.

[0010] Furthermore, the pore-scale interface competition dynamic network model calculates the degree of gas channeling using the following expression: ,in, The gas channeling coefficient, The number of pore throats, For the first The cross-sectional area of ​​the throat. For the first Gas flow rate within the throat For the first The maximum permissible flow rate of each throat. The density of hot nitrogen gas, This is the tortuosity coefficient of the larynx. For the viscosity of heavy oil, The viscosity of hot nitrogen gas, This represents the water phase saturation.

[0011] Further, step S3 includes the following sub-steps: Step S31: Extract reservoir pore structure parameters and fluid property parameters from the basic database of the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, and convert the pore structure parameters into the topological structure data of nodes and edges of the pore-scale interface competition dynamic network model, wherein the pore structure parameters include pore radius distribution, throat length, and pore connectivity, and the fluid property parameters include viscosity, density, and interfacial tension of each phase; Step S32: Convert the injection pressure and injection rate in the initial slug parameter combination obtained in step S2 into the inlet boundary conditions of the model, and set... The reservoir's original temperature and pressure are used as initial boundary conditions to determine the model's time step and number of iterations. Step S33: Based on the set boundary conditions and topology data, the distribution ratio of thermal nitrogen foam in each pore unit, oil phase flow velocity, and gas diffusion distance within each time step are calculated using a pore-scale interface competition dynamic network model, and the change curves of each parameter are recorded. Step S34: Based on the calculation results of each time step, the oil phase recovery rate, gas retention rate, and foam collapse rate during the displacement process are statistically analyzed, and the data are integrated into simulated displacement efficiency data and output to the thermal nitrogen foam displacement real-time monitoring and intelligent control platform.

[0012] Further, the step S4 comprises the following sub-steps: step S41: starting the pressure sensor, temperature sensor, fluid component analyzer of the real-time monitoring and intelligent control platform of the thermal nitrogen foam displacement, setting the collection frequency and data transmission interval of the sensor, ensuring the stable data communication link of each monitoring device and the platform; step S42: collecting the pressure values of different monitoring points of the reservoir through the pressure sensor, generating a pressure response curve; collecting the temperature distribution data between the injection well and the production well through the temperature sensor, forming a temperature field distribution map; detecting the oil, gas and water content ratio in the produced fluid through the fluid component analyzer; step S43: arranging the collected pressure response curve, temperature field distribution map and produced fluid component data according to the time sequence, removing the abnormal values and noise signals in the data to obtain the standardized actual monitoring data; step S44: calling the simulation displacement efficiency data output in step S3 from the real-time monitoring and intelligent control platform of the thermal nitrogen foam displacement, corresponding the actual monitoring data and the simulation data according to the same time node, and calculating the deviation value of the two in the pressure, temperature and recovery parameters.

[0013] Further, the step S5 comprises the following sub-steps: step S51: inputting the deviation value calculated in step S4 into the multi-stage slug dynamic adaptation optimization algorithm, the algorithm determining the slug parameter type that needs to be adjusted according to the size and direction of the deviation value, if the pressure deviation exceeds the threshold value, the injection pressure is adjusted first, if the recovery deviation exceeds the threshold value, the slug size and alternating period are adjusted first; step S52: according to the parameter adjustment direction determined by the algorithm, incrementally adjusting the corresponding parameters in the initial slug parameter combination, the adjustment amplitude is determined according to the deviation value and the preset adjustment coefficient, to avoid parameter mutation affecting the stability of the reservoir; step S53: inputting the adjusted slug parameter combination into the pore-scale interface competition dynamic network model, and updating the boundary conditions of the model according to the actual monitored reservoir temperature change and pressure change, including the inlet flow boundary, outlet pressure boundary and initial saturation distribution; step S54: repeating the simulation calculation in step S3 and the actual data collection comparison in step S4 until the deviation values of all parameters are less than the preset threshold value, at this time, the current slug parameter combination is recorded as the optimized slug parameter combination.

[0014] The shallow heavy oil reservoir thermal nitrogen foam multi-stage slug cooperative displacement system comprises:

[0015] The thermal nitrogen foam preparation and injection unit is connected with the thermal nitrogen generating device, the foam generator and the injection pipeline, and is used for preparing the thermal nitrogen foam according to the optimized slug parameter combination and conveying the thermal nitrogen foam to the injection well;

[0016] A reservoir parameter monitoring unit, which comprises a pressure sensor, a temperature sensor, and a fluid component analyzer, is connected to the hot nitrogen foam displacement real-time monitoring and intelligent control platform through a data transmission line, and is used to collect reservoir pressure, temperature, and produced fluid component data;

[0017] An algorithm operation and model processing unit is connected to the hot nitrogen foam displacement real-time monitoring and intelligent control platform through a data interface, and is internally provided with a multi-stage slug dynamic adaptive optimization algorithm and a pore-scale interface competition dynamic network model, and is used to perform slug parameter optimization and displacement process simulation calculation;

[0018] A data storage and management unit is connected to the hot nitrogen foam displacement real-time monitoring and intelligent control platform, and is used to store basic geological parameters, fluid physical property parameters, real-time monitoring data, simulation calculation results, and optimized slug parameter combination data;

[0019] A system control and instruction output unit is connected to the hot nitrogen foam preparation and injection unit, the reservoir parameter monitoring unit, and the algorithm operation and model processing unit, and is used to receive algorithm operation results and send control instructions to each execution unit;

[0020] A data interaction and visualization unit is connected to the data storage and management unit and the system control and instruction output unit, and is used to display monitoring data, simulation data, and control instructions in a visual form, and supports data export and external device interaction.

[0021] Beneficial effects: The hot nitrogen foam multi-stage slug cooperative displacement method and system for shallow heavy oil reservoirs are provided, a dynamic correlation mechanism of slug parameters and reservoir pore characteristics is established through the combination of a multi-stage slug dynamic adaptive optimization algorithm and a pore-scale interface competition dynamic network model, the injection pressure, rate, size, and alternating period of the slug can be adjusted in real time according to the changes in the reservoir conditions during the displacement process, the problems of experience-dependent slug parameter optimization and poor adaptability in the prior art are effectively solved, the hot nitrogen gas channeling and foam premature collapse phenomenon are reduced, and the displacement efficiency is improved; relying on the hot nitrogen foam displacement real-time monitoring and intelligent control platform, the reservoir parameter monitoring, algorithm operation, data management, and system control functions are integrated, the real-time monitoring data and the pore-scale simulation model are quickly interacted, the simulation deviation is corrected in a timely manner, the control instruction lag is avoided, and the defects of the prior art, such as the lack of an integrated platform and the inability to dynamically feedback and adjust, are solved; at the same time, the system units work cooperatively, from hot nitrogen foam preparation and injection to data interaction and visualization, forming a complete closed loop, and combining the process of multiple simulation and actual data comparison and optimization, the accuracy and stability of the displacement process are further guaranteed, the recovery ratio and development efficiency of shallow heavy oil reservoirs are finally improved, and the efficient development demand under complex reservoir conditions is met. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A method flow chart for the present application;

[0023] Figure 2 A system unit composition diagram for the present application. DETAILED DESCRIPTION

[0024] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict, and the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0025] As shown in the figure, the shallow heavy oil reservoir hot nitrogen foam multi-stage slug cooperative displacement method comprises: Figure 1

[0026] Step S1: Based on the shallow heavy oil reservoir geological parameters and fluid physical property parameters, a basic database of the hot nitrogen foam displacement real-time monitoring and intelligent control platform is constructed, the geological parameters include reservoir porosity, permeability, interlayer distribution characteristics, and the fluid physical property parameters include heavy oil viscosity, density, hot nitrogen gas diffusion coefficient, and foam stability parameters;

[0027] Specifically, step S1 constructs the basic database of the hot nitrogen foam displacement real-time monitoring and intelligent control platform, which is the core data support for all subsequent parameter calculation and simulation, and the data integrity and accuracy directly affect the reliability of subsequent slug optimization and displacement simulation. In the implementation process, first, the geological parameters of the shallow heavy oil reservoir are obtained through geological exploration means, wherein at least 30 groups of data of different well sections are collected for reservoir porosity, and the value range is controlled within 20%-35%, the permeability data need to be distinguished in horizontal and vertical directions, the horizontal permeability value range is 500-2500 mD, the vertical permeability is 1 / 5-1 / 3 of the horizontal permeability, and the interlayer distribution characteristics need to record the interlayer thickness, distribution depth and continuous length, the interlayer thickness data accuracy is controlled within 0.1 m, and the distribution depth error is not more than 0.5 m; then the fluid physical property parameters are determined through laboratory experiments, the heavy oil viscosity needs to be measured at reservoir temperature, the temperature range is 40-60℃, and the corresponding viscosity value is 1000-5000 mPa・s, the heavy oil density measurement accuracy is 0.001 g / cm³, and the value range is 0.92-0.98 g / cm³, the hot nitrogen gas diffusion coefficient needs to be measured at different pressures, the pressure range is 5-15 MPa, the diffusion coefficient value is 1.0×10⁻ 9 -3.0×10⁻ 9 m² / s, and the foam stability parameters are determined by the foam half-life, and the half-life value range is 30-120 min. All the above parameters are stored in the platform database according to the classification of "geological parameters-fluid physical property parameters", and a data verification mechanism is established, the data outside the normal value range is rechecked, and the data error rate in the database is ensured to be less than 1%, to provide accurate data basis for the subsequent steps.​

[0028] Step S2: calling a multi-stage slug dynamic adaptation optimization algorithm to perform preliminary iterative calculation on the injection pressure, injection rate, slug size, and slug alternating period parameters of the hot nitrogen foam multi-stage slug, and obtaining an initial slug parameter combination;

[0029] Specifically, step S2 is to obtain the initial slug parameter combination through the multi-stage slug dynamic adaptation optimization algorithm, to provide an initial parameter benchmark for subsequent displacement simulation, and to avoid too large deviation between the simulation result and the actual situation caused by blind setting of parameters. In the implementation process, first, core data such as reservoir permeability, heavy oil viscosity, and foam stability parameters are called from the basic database as algorithm input parameters; after the algorithm is started, the slug stage range is set to 3-5 stages, the initial value of the injection pressure of each stage slug is determined according to the reservoir breakdown pressure, the reservoir breakdown pressure is determined to be 8-18 MPa through geomechanical experiment, and the initial value of the injection pressure is set to 60%-80% of the breakdown pressure, i.e. 4.8-14.4 MPa; the initial value of the injection rate is determined in combination with the injection-production well spacing and the reservoir permeability, and when the injection-production well spacing is 200-500 m, the initial value of the injection rate is set to 50-150 m³ / d; the initial value of the slug size is calculated according to the reservoir pore volume, the reservoir pore volume is calculated through the well pattern area, reservoir thickness, and porosity, and the initial value of the slug size is set to 5%-10% of the pore volume; the initial value of the slug alternating period is calculated in combination with the injection rate and the slug size, i.e. the slug size divided by the injection rate, and the initial value of the alternating period is 10-30 d. The algorithm performs preliminary adjustment on the above parameters through 3-5 times of iterative calculation, and in the iterative process, the constraint conditions are that the injection pressure does not exceed the breakdown pressure and the injection rate matches the reservoir seepage capacity, and finally a group of initial slug parameter combinations satisfying the constraint conditions are output, the parameter combinations need to include the specific injection pressure, injection rate, slug size, and alternating period values of each stage slug, and each parameter value needs to be kept to two decimal places to ensure the parameter accuracy.

[0030] Step S3: inputting the initial slug parameter combination into the pore-scale interface competition dynamic network model to simulate the migration path, interface tension change, and gas channeling degree of the hot nitrogen foam in the reservoir pores, and outputting the simulation displacement efficiency data;

[0031] Specifically, step S3 is to input the initial slug parameter combination into the pore-scale interface competition dynamic network model for displacement simulation. Through model prediction of the displacement process, problems such as gas channeling and low displacement efficiency that may occur are identified in advance to provide a basis for subsequent actual displacement. In the implementation process, first, the injection pressure and injection rate in the initial slug parameter combination output in step S2 are converted into model inlet boundary conditions. The inlet pressure is set according to the initial injection pressure, and the inlet flow rate is set according to the initial injection rate. At the same time, the reservoir pore structure parameters are retrieved from the basic database, including pore radius distribution (value range: 5-50 pm), throat length (value range: 10-100 pm), and pore connectivity (represented by connectivity probability, value range: 0.7-0.95). These parameters are converted into the topological structure data of the model's nodes (representing pores) and edges (representing throats). The number of nodes is set to 1000-2000, and the number of edges is determined to be 1500-3000 according to the connectivity probability. The model initial boundary conditions also need to set the reservoir original temperature (40-60℃), original pressure (5-15 MPa), time step is set to 1-6h, and iteration number is set to 50-100 times. During the model running process, after completing the calculation of each time step, the distribution proportion (represented by volume fraction, accuracy retained to three decimal places) of hot nitrogen foam in each pore unit within the time step, the oil phase flow velocity (value range: 1×10⁻ 6 -1×10⁻ 5 m / s), and gas diffusion distance (value range: 0.1-1m) are output. After all iteration numbers are completed, the oil phase recovery (value range: 20%-40%), gas retention rate (value range: 30%-60%), and foam collapse rate (value range: 10%-30%) in the entire displacement process are calculated. These statistical data are arranged in the form of "time-parameter value" correspondence to obtain the simulation displacement efficiency data. The data need to be output in the form of a table, which includes four columns of time, recovery, gas retention rate, and foam collapse rate, with two decimal places for each column of data. At the same time, the curves of each parameter changing with time are generated, with the horizontal coordinate being time (unit: d) and the vertical coordinate being parameter value, to ensure that the simulation results are intuitive and accurate.

[0032] Step S4: Through the real-time monitoring and intelligent control platform of hot nitrogen foam displacement, the pressure response signal, temperature distribution data, and produced fluid component data in the actual displacement process are collected, and compared and analyzed with the simulation displacement efficiency data output in step S3.

[0033] Specifically, step S4 is to monitor in real time by thermal nitrogen foam displacement, collect actual displacement data by the intelligent regulation and control platform, and compare with the simulation data, establish the correlation between simulation and reality, identify the deviation between the two through comparison, and provide basis for subsequent parameter adjustment. In the implementation process, first, start the monitoring equipment associated with the platform, including pressure sensors (5-10 in number, distributed at different positions between injection and production wells, depth error not more than 0.5 m, matching the reservoir thickness), temperature sensors (5-10 in number, deployed at the same position as the pressure sensors), and fluid component analyzers installed at the outlet of the production well (detection accuracy of 0.1%); set the sensor collection frequency to 1-2 times / h, the data transmission interval to 0.5-1 h, and transmit the data to the platform in real time through wired transmission mode, ensuring that the data transmission delay does not exceed 10 s. The pressure sensor collects the pressure values of different monitoring points in the reservoir, generates a pressure response curve, and the curve needs to mark the pressure change trend of each monitoring point, with the pressure value accuracy of two decimal places (unit: MPa); the temperature sensor collects temperature data at each monitoring point to form a temperature field distribution map, with the injection-production well line as the horizontal coordinate and the depth as the vertical coordinate, and different colors are used to mark the temperature interval (interval of 5°C); the fluid component analyzer detects the oil, gas and water content proportion in the produced fluid, and the detection results are recorded in the order of "oil phase-gas phase-water phase", with the proportion value of two decimal places (unit: %). The collected pressure response curve, temperature field distribution map and produced fluid component data are arranged in time sequence, and the sliding average method is used to remove abnormal values (abnormal value judgment standard is ±10% of the average value of adjacent data) and noise signals in the data, to obtain standardized actual monitoring data; then the simulation displacement efficiency data output by step S3 are retrieved from the platform, the actual monitoring data and the simulation data are corresponded according to the same time node (interval of 1 d), and the deviation values of the two in the pressure (deviation allowed range ±0.5 MPa), temperature (deviation allowed range ±2°C) and recovery ratio (deviation allowed range ±3%) parameters are calculated, the deviation value calculation method is the absolute value of (actual value-simulation value), the calculation result is kept to two decimal places, and a deviation analysis report is formed.

[0034] Step S5: According to the comparison and analysis results, adjust the slug parameter combination through the multi-stage slug dynamic adaptive optimization algorithm, update the boundary conditions of the pore-scale interface competition dynamic network model synchronously, and repeat steps S3-S4 until the deviation between the simulation data and the actual monitoring data meets the preset threshold.

[0035] Specifically, step S5 is to adjust the slug parameters according to the deviation analysis results and update the model boundary conditions, to realize the matching of simulation and actual data through repeated iteration, ensure the accuracy of the parameters in the subsequent displacement process, and improve the actual displacement efficiency. In the implementation process, first, input the deviation value in the deviation analysis report formed in step S4 into the multi-stage slug dynamic adaptation optimization algorithm. The algorithm first judges whether the deviation of each parameter exceeds the preset threshold value (the pressure deviation threshold value is 0.5 MPa, the temperature deviation threshold value is 2°C, and the recovery rate deviation threshold value is 3%). If the pressure deviation exceeds the threshold value, the injection pressure is adjusted first. The adjustment range is determined according to the deviation value. For every 0.1 MPa of deviation value exceeding the threshold value, the injection pressure is adjusted by 0.05-0.1 MPa, and the adjusted injection pressure must not exceed the reservoir fracture pressure. If the recovery rate deviation exceeds the threshold value, the slug size and the alternating period are adjusted first. The adjustment range of the slug size is 5%-10% of the original size, and the adjustment range of the alternating period is 10%-15% of the original period. After the parameter adjustment is completed, a new slug parameter combination is generated, and the boundary conditions of the pore-scale interface competition dynamic network model are updated according to the reservoir temperature change (the difference between the actual temperature and the original temperature, with a value range of 5-20°C) and the pressure change (the difference between the actual pressure and the original pressure, with a value range of -2-5 MPa) in the actual monitoring data collected in step S4, including setting the inlet flow boundary according to the new injection rate, setting the outlet pressure boundary according to the actual monitoring production well pressure, and setting the initial saturation distribution according to the actual monitoring oil saturation (with a value range of 40%-70%). Then, the simulation calculation process of step S3 is repeatedly executed, the model is operated using the updated boundary conditions and the new slug parameter combination, and new simulated displacement efficiency data are obtained. The actual data collection and comparison process of step S4 is repeatedly executed again, and new deviation values are calculated. This cycle is iterated, and after each iteration, it is judged whether the deviation values are all less than the preset threshold value. Until all parameter deviation values meet the requirements, the current slug parameter combination is recorded as the optimized slug parameter combination. The optimized parameter combination needs to be verified for 3 times in succession to ensure that the deviation values of each verification are within the threshold value range, avoid parameter fluctuations, the verification interval is 12-24 h, and the optimized parameters are stored in the platform database after the verification is passed.

[0036] Step S6: Based on the optimized slug parameter combination, control the hot nitrogen foam injection equipment to inject multi-stage hot nitrogen foam slugs into the shallow heavy oil reservoir according to the set injection sequence and injection time length, and continuously collect displacement process data through the hot nitrogen foam displacement real-time monitoring and intelligent control platform and feed back to the multi-stage slug dynamic adaptation optimization algorithm.

[0037] Specifically, step S6 involves performing hot nitrogen foam injection according to the optimized slug parameters and continuously monitoring and providing feedback. This is the core step in applying the previous optimization results to actual development, achieving efficient synergistic displacement in shallow heavy oil reservoirs and improving resource recovery. During implementation, the optimized slug parameter combination is first retrieved from the database of the hot nitrogen foam displacement real-time monitoring and intelligent control platform. The injection pressure (4.8-14.4 MPa, two decimal places), injection rate (50-150 m³ / d, one decimal place), slug size (5%-10% of pore volume, three decimal places), alternation period (10-30 days, one decimal place), and injection sequence (injected sequentially from 1 to n according to the slug level) for each slug stage are determined. Subsequently, control commands are sent to the hot nitrogen foam injection equipment (including a hot nitrogen generator, a foam mixing tank, and an injection pump). The hot nitrogen generator produces hot nitrogen gas at a set temperature (120-180℃). The foam mixing tank mixes the hot nitrogen gas with a surfactant solution (concentration of 0.1%-0.5%) in a ratio (gas to liquid volume ratio of 3:1-5:1) to generate hot nitrogen foam. The injection pump delivers the hot nitrogen foam to the injection well through the injection pipeline according to the optimized injection pressure and injection rate. During the injection process, the injection sequence is strictly controlled. The injection time of each slug is calculated based on the slug size and injection rate, and the injection time error does not exceed 1 hour. When the slugs are alternating, the injection rate needs to transition smoothly, with a transition time of 1-2 hours, to avoid damage to the reservoir caused by sudden pressure changes. Meanwhile, the displacement process data is continuously collected through a real-time monitoring and intelligent control platform for thermo-nitrogen foam displacement. The working status of the data collection equipment needs to be checked every hour to ensure normal data collection. The collected data is transmitted to the platform in real time, and the platform synchronously feeds the data back to the multi-stage slug dynamic adaptation optimization algorithm. The algorithm analyzes the data in real time to determine whether the current displacement state is stable (the stability criteria are pressure fluctuation less than 0.3 MPa / h, temperature fluctuation less than 1℃ / h, and recovery rate change less than 0.5% / d). If instability occurs, the algorithm immediately issues an early warning and generates parameter fine-tuning suggestions based on real-time data. The fine-tuning range is 3%-5% of the optimized parameters to ensure that the displacement process remains in the optimal state until the injection of all multi-stage slugs is completed.

[0038] Preferably, the multi-level slug dynamic adaptation optimization algorithm uses the following expression to calculate the optimized values ​​of the slug parameters: ,in, Inject pressure into the optimized slug. For slug series, For the first The weighting coefficient of the stage block, For the first The initial injection pressure of the stage plug, For the first Displacement efficiency coefficient of stage plug. is a pressure compensation coefficient, is a maximum pressure difference of the reservoir, is a flow loss coefficient, is a thermal nitrogen gas channeling loss amount, is a reservoir sensitivity coefficient, is a residual oil saturation of the reservoir.

[0039] Specifically, the multi-stage slug dynamic adaptive optimization algorithm calculates the optimized value of the slug parameter to provide a quantitative basis for the precise adjustment of the slug injection pressure, avoiding the damage to the reservoir caused by the excessively high injection pressure or the impact on the displacement effect caused by the excessively low injection pressure. In the implementation process, first, the number of slug stages is determined, and according to the reservoir thickness and heterogeneity of the shallow heavy oil reservoir, the number of slug stages is usually set to 3-5 stages; then, a weight coefficient is assigned to each stage of the slug, and the weight coefficient is determined according to the role of each stage of the slug in the displacement process. The weight coefficient of the slug in the high-permeability area of the reservoir is 0.25-0.35, and the weight coefficient of the slug in the low-permeability area is 0.15-0.25, ensuring that the sum of the weight coefficients is 1. The initial injection pressure is determined according to the reservoir breakdown pressure, and the reservoir breakdown pressure is determined by core experiment to be 8-18 MPa, and the initial injection pressure is set to 60%-80% of the breakdown pressure; the displacement efficiency coefficient is obtained through the previous simulation experiment, and the value range is 0.6-0.9, and the value increases with the improvement of the adaptation degree of the slug and the reservoir. The pressure compensation coefficient is set according to the reservoir pressure decay, and the value is 0.05-0.15; the maximum pressure difference of the reservoir is the difference between the original pressure of the reservoir and the pressure of the production well, and the value range is 3-10 MPa; the flow loss coefficient is determined in combination with the thermal nitrogen gas channeling characteristics, and the value is 0.1-0.2; the thermal nitrogen gas channeling loss amount is calculated by monitoring the gas production of the production well, and the value range is 5-20 m³ / d; the reservoir sensitivity coefficient is determined according to the reservoir rock sensitivity experiment, and the value is 0.02-0.08; the residual oil saturation of the reservoir is obtained through the logging data, and the value range is 30%-60%. The above parameters are substituted into the algorithm expression to calculate the optimized slug injection pressure, and the calculation result needs to be kept to two decimal places, and it is necessary to verify whether the optimized pressure is within the safe range (not more than the reservoir breakdown pressure, not less than the reservoir starting pressure), and after the verification is passed, it can be used for subsequent displacement operation.

[0040] Preferably, the pore-scale interface competition dynamic network model calculates the change of the foam interfacial tension by using the following expression: wherein, is a dynamic interfacial tension, is an initial interfacial tension, is an interfacial tension decay coefficient, is a displacement time, is a reservoir porosity, is a gas saturation, is a viscosity of the thermal nitrogen gas, Fluid flow coefficient, Heavy oil viscosity, Oil phase saturation, Surfactant influence coefficient, Surfactant concentration, Foam volume, Pore interface area.

[0041] Specifically, by the expression of the pore-scale interface competition dynamic network model, the change of dynamic interfacial tension of foam is accurately calculated, the stability law of foam in reservoir pores is mastered, and data support is provided for adjusting the slug parameters and reducing the premature collapse of foam. In the implementation process, the initial interfacial tension is determined by the laboratory surface tension meter, and under the condition that the surfactant concentration is 0.1%-0.5%, the initial interfacial tension is in the range of 15-30 mN / m; the interfacial tension decay coefficient is determined according to the aging experiment of foam in the reservoir fluid, and the value is 0.01-0.05 h⁻¹, the larger the value, the faster the interfacial tension decays; the displacement time is set according to the slug injection period, and the value is in the range of 10-30 d. The reservoir porosity is obtained by core analysis, and the value is in the range of 20%-35%; the gas phase saturation is calculated by combining logging and production data, and the value is in the range of 10%-40%; the viscosity of hot nitrogen gas is determined under the conditions of reservoir temperature (40-60℃) and pressure (5-15 MPa), and the value is in the range of 1.5×10⁻ 5 -2.5×10⁻ 5 Pa・s; the fluid flow coefficient is determined in combination with the reservoir permeability and fluid viscosity, and the value is in the range of 0.001-0.01 m² / (Pa・s); the heavy oil viscosity is determined at the reservoir temperature, and the value is in the range of 1000-5000 mPa・s; the oil phase saturation is obtained by logging data, and the value is in the range of 40%-70%. The surfactant influence coefficient is determined according to the type and concentration of surfactant, and the value is 0.02-0.08; the surfactant concentration is determined according to the foam stability experiment, and the value is in the range of 0.1%-0.5%; the foam volume is calculated according to the slug size and injection rate, and the value is in the range of 1000-5000 m³; the pore interface area is calculated by the reservoir pore structure parameters, in combination with the pore radius and number, and the value is in the range of 1×10 4 -5×10 4 m². The above parameters are substituted into the model expression to calculate the dynamic interfacial tension under different displacement times, and the calculation results need to be arranged in time sequence to form the interfacial tension change curve, and the curve needs to mark the interfacial tension values of key time nodes to provide intuitive basis for judging the stability of foam. If the interfacial tension decays too fast (the decay amplitude at a certain time node exceeds 30% of the initial value), the surfactant concentration or the slug injection rate needs to be adjusted.

[0042] Preferably, the real-time monitoring and intelligent control platform for the thermal nitrogen foam displacement calculates the real-time displacement efficiency using the following expression: wherein, is the real-time displacement efficiency, is the cumulative oil production, is the heavy oil density, is the oil volume coefficient, is the displacement time, is the thermal nitrogen foam injection flow rate, is the produced fluid flow rate, is the thermal nitrogen gas density, is the gas volume coefficient, is the original geological reserves, and is the monitoring data correction coefficient.

[0043] Specifically, the real-time displacement efficiency is calculated by the expression of the real-time monitoring and intelligent control platform for the thermal nitrogen foam displacement, the actual displacement effect is grasped in real time, problems in the displacement process are found in time and strategies are adjusted in time to avoid resource waste. In the implementation process, the cumulative oil production is obtained by the production well metering equipment, the metering accuracy is 0.1 m³, and the value range is 500-5000 m³; the heavy oil density is determined under laboratory conditions, the value range is 0.92-0.98 g / cm³; the oil volume coefficient is determined according to the reservoir pressure and temperature, the value range is 1.05-1.25. The thermal nitrogen foam injection flow rate is determined by the injection well flow meter, the metering accuracy is 0.1 m³ / d, and the value range is 50-150 m³ / d; the produced fluid flow rate is determined by the production well flow meter, the value range is 30-120 m³ / d; the thermal nitrogen gas density is calculated under the reservoir temperature and pressure conditions, the value range is 2.0-5.0 kg / m³; the gas volume coefficient is determined according to the reservoir pressure and temperature, the value range is 50-200 m³ / m³. The original geological reserves are calculated by the reservoir volume, porosity and oil saturation, the value range is 1×10 5 -1×10 6 m³; the monitoring data correction coefficient is determined according to the monitoring equipment accuracy and the field environment, the value range is 0.95-1.05, and is used to correct the monitoring data deviation caused by equipment error or environmental interference. When calculating, firstly, the injection flow rate and the produced fluid flow rate data are collected in time sequence, the difference between the two is integrated, and the integral interval is from the start of displacement to the current time; then the integral result is combined with the cumulative oil production related parameters, and the real-time displacement efficiency is calculated by substituting the expression, and the calculation result is retained to two decimal places. The real-time displacement efficiency needs to be calculated once every 24 hours, and compared with the preset target displacement efficiency (the value range is 30%-50%), if it is lower than the target value by more than 10%, the slug parameter adjustment process needs to be started to ensure the stable improvement of the displacement efficiency.

[0044] Preferably, the multi-stage slug dynamic adaptation optimization algorithm adjusts the slug alternating period through the following expression: wherein, is the slug alternating period, is the reservoir pore volume, is the reservoir porosity, is the current oil phase saturation, is the target oil phase saturation, is the hot nitrogen foam injection flow rate, is the slug volume utilization rate, is the temperature influence coefficient, is the reservoir temperature change value, is the difference between the injected hot nitrogen temperature and the original reservoir temperature.

[0045] Specifically, the multi-stage slug dynamic adaptation optimization algorithm adjusts the slug alternating period through the expression, so that the slug alternating rhythm matches the reservoir fluid migration law, and the decline of displacement efficiency caused by too long period or the increase of operation cost caused by too short period is avoided. In the implementation process, the reservoir pore volume is calculated by well pattern area, reservoir thickness and porosity, the well pattern area is determined according to the injection-production well spacing (when the injection-production well spacing is 200-500 m, the well pattern area is 4×10 4 -2.5×10 5 m²), the reservoir thickness is obtained by logging data (the value range is 5-20 m), and the porosity value range is 20%-35%, and the pore volume value range is 5×10 4 -2×10 6m3. The current oil phase saturation is obtained by recent logging data, with a value range of 40%-70%; the target oil phase saturation is determined according to the development plan, with a value range of 20%-30%; the injection flow rate of the thermal nitrogen foam is monitored in real time by an injection well flow meter, with a value range of 50-150 m3 / d; the slug volume utilization rate is determined according to the previous displacement experiment, with a value range of 0.7-0.9, reflecting the proportion of the slug effectively acting on the reservoir. The temperature influence coefficient is determined according to the influence degree of the reservoir temperature change on the fluid viscosity, with a value range of 0.03-0.08; the reservoir temperature change value is the difference between the current average reservoir temperature and the original temperature, with a value range of 5-20℃; the difference between the injection temperature of the thermal nitrogen and the original reservoir temperature is monitored by a temperature sensor, with the injection temperature of the thermal nitrogen controlled at 120-180℃, the original temperature at 40-60℃, and the difference value range at 80-140℃. The slug alternating period is calculated by substituting the above parameters into the algorithm expression, with the calculation result retaining one decimal place and the value range generally being 10-30d. After the calculation is completed, the period rationality needs to be verified in combination with the switching ability of the field injection equipment, and the equipment switching time needs to be controlled at 2-4h. If the period is too short to cause frequent switching of the equipment (the switching interval is less than the minimum stable operation time of the equipment by 8h), the slug volume utilization rate or the target oil phase saturation needs to be adjusted appropriately, and the period is recalculated until the period is reasonable, so as to ensure the stability and efficiency of the slug alternating process.

[0046] Preferably, the pore-scale interfacial competitive dynamic network model calculates the gas channeling degree through the following expression: wherein, is the gas channeling coefficient, is the number of pore throats, is the cross-sectional area of the th throat, is the gas flow rate in the th throat, is the maximum allowable flow rate of the th throat, is the density of the thermal nitrogen gas, is the tortuosity coefficient of the throat, is the viscosity of the heavy oil, is the viscosity of the thermal nitrogen gas, is the water phase saturation.

[0047] Specifically, the gas channeling degree is calculated by the expression of the pore-scale interface competition dynamic network model, the hot nitrogen gas channeling risk in the reservoir is quantified, the basis is provided for taking profile control measures and optimizing slug parameters, and the invalid gas channeling leading to reduced displacement efficiency is avoided. In the implementation process, the number of pore throats is determined according to the reservoir pore structure model, the representative reservoir area is selected during the model construction, and the number of throats is valued in the range of 500-1500; the cross-sectional area of each throat is calculated by the pore structure parameters, combined with the throat radius (valued in the range of 2-20 μm), and the cross-sectional area is valued in the range of 1×10⁻¹¹-1×10⁻ 9 m². The gas flow rate in each throat is calculated by model simulation, and the value is in the range of 1×10⁻ 4 -1×10⁻³ m / s; the maximum allowable flow rate of each throat is determined according to the throat stability experiment, the throat damage caused by too high flow rate is avoided, and the value is in the range of 2×10⁻ 4 -2×10⁻³ m / s; the density of hot nitrogen gas is calculated under the conditions of reservoir temperature (40-60℃) and pressure (5-15 MPa), and the value is in the range of 2.0-5.0 kg / m³. The throat tortuosity coefficient is determined according to the reservoir rock CT scan data, and the value is in the range of 1.2-1.8, reflecting the bending degree of the throat path; the heavy oil viscosity is measured at the reservoir temperature, and the value is in the range of 1000-5000 mPa・s; the water phase saturation is obtained by logging data, and the value is in the range of 10%-30%. The above parameters are substituted into the model expression, the gas channeling coefficient is calculated, the calculation result is kept to three decimal places, and the value is usually in the range of 0.1-0.5, and the larger the coefficient, the higher the gas channeling risk. When the gas channeling coefficient exceeds 0.3, the control measures need to be started, the channeling risk can be reduced by increasing the surfactant concentration (increased by 0.05%-0.1%) or reducing the slug injection rate (reduced by 5%-10%), the gas channeling coefficient is recalculated after adjustment, until the coefficient is reduced to below 0.3, and it is ensured that the hot nitrogen gas mainly acts on the displacement of crude oil rather than invalid channeling.

[0048] Preferably, the step S3 comprises the following sub-steps: step S31: extracting reservoir pore structure parameters and fluid physical property parameters from the basic database of the real-time monitoring and intelligent control platform of hot nitrogen foam flooding, converting the pore structure parameters into the topological structure data of the nodes and edges of the pore-scale interface competition dynamic network model, wherein the pore structure parameters include pore radius distribution, throat length, and pore connectivity, and the fluid physical property parameters include the viscosity, density, and interfacial tension of each phase; step S32: converting the injection pressure and injection rate in the initial slug parameter group obtained in step S2 into the inlet boundary conditions of the model, setting the original temperature and pressure of the reservoir as the initial boundary conditions, and determining the time step and iteration number of the model; step S33: based on the set boundary conditions and topological structure data, calculating the distribution proportion of hot nitrogen foam in each pore unit, oil phase flow velocity, and gas diffusion distance in each time step through the pore-scale interface competition dynamic network model, and recording the change curves of each parameter; and step S34: according to the calculation results of each time step, calculating the oil phase recovery rate, gas retention rate, and foam collapse rate in the flooding process, integrating the data into the simulation flooding efficiency data, and outputting the data to the real-time monitoring and intelligent control platform of hot nitrogen foam flooding.

[0049] Specifically, step S3 defines the simulation process of the pore-scale interface competition dynamic network model, and ensures the accuracy of the simulation data through sub-step operation, thereby laying a foundation for subsequent comparison with actual data. In implementation, step S31: parameters are extracted from the basic database of the real-time monitoring and intelligent control platform of hot nitrogen foam flooding, the pore radius distribution in the reservoir pore structure parameters needs to cover the data in the interval of 5-50 μm, the throat length is valued at 10-100 μm, and the pore connectivity is represented by a connectivity probability of 0.7-0.95; the viscosities of each phase in the fluid physical property parameters need to be measured at a reservoir temperature of 40-60 ℃ and a pressure of 5-15 MPa, the oil phase viscosity is 1000-5000 mPa・s, the gas phase viscosity is 1.5×10⁻ 5 -2.5×10⁻ 5 Pa・s, the interfacial tension is 15-30 mN / m, and then these parameters are converted into the topological structure data of the model, and the number of nodes (representing pores) is determined to be 1000-2000, and the number of edges (representing throats) is determined to be 1500-3000. Step S32: the injection pressure 4.8-14.4 MPa and the injection rate 50-150 m³ / d in the initial slug parameter group of S2 are converted into the inlet boundary conditions, the original temperature 40-60 ℃ and the pressure 5-15 MPa of the reservoir are set as the initial boundary, the time step is 1-6 h, and the iteration number is 50-100 times. Step S33: based on the boundary conditions and topological data, the model calculates the distribution proportion (volume fraction accuracy three decimal places) of foam in each pore unit, the oil phase flow velocity 1×10⁻ 6 -1×10⁻ 5m / s, gas diffusion distance 0.1-1 m, record parameter variation curve. Step S34: statistics of oil phase recovery rate 20%-40% in displacement process, gas retention rate 30%-60%, foam collapse rate 10%-30%, integrated into simulation displacement efficiency data and transmitted to platform, the whole process needs to ensure that the error rate of each step data is less than 1%, to ensure the reliability of simulation results.

[0050] Preferably, the step S4 comprises the following sub-steps: step S41: start the pressure sensor, temperature sensor, fluid component analyzer of the real-time monitoring and intelligent control platform of hot nitrogen foam displacement, set the collection frequency and data transmission interval of the sensor, ensure the stable data communication link of each monitoring equipment and the platform; step S42: collect the pressure values of different monitoring points of the reservoir through the pressure sensor, generate the pressure response curve; collect the temperature distribution data between the injection well and the production well through the temperature sensor, form the temperature field distribution map; detect the oil, gas and water content ratio in the produced fluid through the fluid component analyzer; step S43: arrange the collected pressure response curve, temperature field distribution map and produced fluid component data according to time sequence, remove the abnormal values and noise signals in the data, obtain the standardized actual monitoring data; step S44: call the simulation displacement efficiency data output in step S3 from the real-time monitoring and intelligent control platform of hot nitrogen foam displacement, correspond the actual monitoring data and the simulation data according to the same time node, calculate the deviation value of the two in the pressure, temperature and recovery rate parameters.

[0051] Specifically, step S4 realizes the collection, processing and comparison of actual monitoring data and simulation data, and eliminates data errors through step-by-step operations to accurately identify the deviation between simulation and reality. When implemented, step S41: start the monitoring equipment associated with the platform, 5-10 pressure sensors and temperature sensors are respectively deployed at different depths between injection and production wells, the depth error is not more than 0.5 m, the fluid component analyzer is installed at the outlet of the production well, and the detection accuracy is 0.1%; set the sensor collection frequency to 1-2 times / h, the data transmission interval to 0.5-1 h, use wired transmission to ensure that the delay does not exceed 10 s, and check the stability of the equipment communication link at the same time. Step S42: generate a response curve by collecting pressure values at the monitoring points through the pressure sensor, and the pressure value accuracy is retained to two decimal places; the temperature sensor collects data to form a temperature field distribution map, and the temperature interval is divided according to 5℃ intervals and labeled with color; the fluid component analyzer detects the oil, gas and water content proportion of the produced fluid, and the result is retained to two decimal places. Step S43: arrange the collected data in time sequence, and use the moving average method to eliminate abnormal values and noise exceeding ±10% of the average value of adjacent data to obtain standardized actual monitoring data. Step S44: retrieve the simulation displacement efficiency data from the platform in step S3, correspond to the actual data according to 1d as the time node, calculate the pressure deviation (allowing ±0.5MPa), the temperature deviation (allowing ±2℃), and the recovery ratio deviation (allowing ±3%), the deviation value is retained to two decimal places, and a deviation analysis report is formed to provide a clear direction for subsequent parameter adjustment, and the timeliness of data processing and comparison needs to be ensured, and the time consumption from collection to report formation is not more than 2h.

[0052] Preferably, step S5 includes the following sub-steps: step S51: input the deviation value calculated in step S4 into a multi-stage slug dynamic adaptive optimization algorithm, the algorithm determines the type of slug parameter that needs to be adjusted according to the size and direction of the deviation value, if the pressure deviation exceeds the threshold, the injection pressure is adjusted first, if the recovery ratio deviation exceeds the threshold, the slug size and alternating period are adjusted first; step S52: according to the parameter adjustment direction determined by the algorithm, incrementally adjust the corresponding parameters in the initial slug parameter combination, and the adjustment amplitude is determined according to the deviation value and a preset adjustment coefficient to avoid parameter mutation affecting the stability of the reservoir; step S53: input the adjusted slug parameter combination into the pore-scale interface competition dynamic network model, and update the boundary conditions of the model according to the actual monitoring of the reservoir temperature change and the pressure change, including the inlet flow boundary, the outlet pressure boundary and the initial saturation distribution; step S54: repeat the simulation calculation in step S3 and the comparison of actual data collection in step S4 until the deviation values of all parameters are less than the preset threshold, at this time, the current slug parameter combination is recorded as the optimized slug parameter combination.

[0053] Specifically, the operation process of step S5 realizes the iterative optimization of the slug parameter adjustment and the model boundary update through sub-steps, gradually reduces the deviation between simulation and actual data, and ensures that the final optimized parameters meet the actual displacement requirements. When implementing, step S51: input the deviation value in the S4 deviation analysis report into the multi-stage slug dynamic adaptation optimization algorithm, and judge whether it is beyond the preset threshold (pressure 0.5 MPa, temperature 2℃, recovery rate 3%). If the pressure deviation is beyond the threshold, adjust the injection pressure by 0.05-0.1 MPa for every 0.1 MPa of deviation, and do not exceed the reservoir fracture pressure of 8-18 MPa. If the recovery rate is beyond the threshold, adjust the slug size (original size 5%-10% range) and the alternating period (original period 10%-15% range). Step S52: determine the direction increment adjustment parameter according to the algorithm, avoid the influence of mutation on the stability of the reservoir, generate a new slug parameter combination after adjustment, and update the model inlet flow, outlet pressure and initial saturation distribution boundary conditions according to the actual monitored reservoir temperature change of 5-20℃ and pressure change of-2-5 MPa. Step S53: substitute the new parameters and updated boundary into the model to repeat S3 simulation, and then repeat S4 to collect and compare the new deviation. Step S54: cycle and iterate until all deviations meet the standards. After meeting the standards, it needs to be verified for 3 times continuously (interval 12-24h), ensure that the deviation is within the threshold each time, avoid parameter fluctuation, finally record the optimized slug parameter combination and store it to the platform database, and the total number of iterations in the whole iteration process needs to be controlled within 5-8 times to ensure the optimization efficiency. At the same time, the parameter change range and deviation change trend need to be recorded after each adjustment to form an optimization log.

[0054] The multi-stage segment dynamic adaptive optimization algorithm in the application is the core calculation logic for real-time adjustment of injection parameters of the hot nitrogen foam multi-stage segment in the shallow heavy oil reservoir, specifically, a parameter optimization calculation system is constructed by integrating reservoir geological parameters, fluid physical property parameters and displacement process monitoring data. The implementation mode needs to first call the basic data from the real-time monitoring and intelligent control platform of the hot nitrogen foam displacement, including reservoir porosity 20%-35%, permeability 500-2500 mD, heavy oil viscosity 1000-5000 mPa·s, etc., then set initial parameters such as segment stage number 3-5 stages, initial injection pressure 4.8-14.4 MPa, etc., then adjust the segment injection pressure, rate, size and alternating period through 3-5 times of iteration calculation with the reservoir fracture pressure and displacement efficiency as the constraint conditions, in the iteration, the injection pressure is adjusted in priority according to the pressure deviation (more than 0.5 MPa), 0.05-0.1 MPa is adjusted for each 0.1 MPa of the deviation, or the segment size (5%-10% amplitude) and period (10%-15% amplitude) are adjusted according to the recovery rate deviation (more than 3%). The algorithm outputs the segment parameter combination adapted to the dynamic conditions of the reservoir, avoids the gas channeling or foam breaking problem caused by fixed parameters, breaks the traditional experience-based parameter setting mode, improves the adaptability of the segment to the reservoir through dynamic optimization, provides precise parameter support for efficient displacement, and helps to improve the recovery rate of the shallow heavy oil reservoir.

[0055] The pore-scale interface competition dynamic network model in the application is a numerical model for simulating the migration and interface action of the hot nitrogen foam in the pores of the shallow heavy oil reservoir, specifically, the interface competition process of the foam and the oil and water phases is quantified by constructing a pore-throat topological structure. The implementation mode needs to first convert the reservoir pore structure parameters (pore radius 5-50 μm, throat length 10-100 μm, connectivity probability 0.7-0.95) into a topological structure of 1000-2000 nodes (representing pores) and 1500-3000 edges (representing throats), then input the segment parameters (injection pressure 4.8-14.4 MPa, rate 50-150 m³ / d) as boundary conditions, set the time step 1-6 h and the iteration number 50-100 times, then calculate the foam distribution ratio (volume fraction accuracy three decimal places), oil phase flow velocity 1×10⁻ 6 -1×10⁻ 5 m / s and gas diffusion distance 0.1-1 m in each time step, and simultaneously quantify the dynamic interfacial tension change and gas channeling degree. The model predicts the oil phase recovery rate 20%-40%, gas retention rate 30%-60% and foam breaking rate 10%-30% in the displacement process, generates simulation displacement efficiency data, reveals the displacement mechanism from the micro pore scale, identifies potential problems in advance, provides simulation basis for segment parameter adjustment, and makes up for the defects of the traditional macro model that cannot accurately reflect the interface action in the pores.

[0056] The thermal nitrogen foam displacement real-time monitoring and intelligent regulation platform in the application is an integrated system carrier integrating data acquisition, processing, simulation interaction and instruction output, and specifically is a core hub connecting monitoring equipment, algorithms and execution units. The implementation thereof needs to first build a basic database to store reservoir geology (porosity, permeability) and fluid physical property (viscosity, density) data, then deploy 5-10 pressure / temperature sensors (acquisition frequency 1-2 times / h, delay <10s) and fluid component analyzers (accuracy 0.1%) to collect pressure response curves, temperature field distribution maps and produced fluid component data in real time, subsequently standardize the collected data (remove ±10% abnormal values), compare with the data simulated by the pore-scale model at 1d nodes, calculate the pressure (±0.5MPa), temperature (±2℃) and recovery (±3%) deviations, support parameter iteration and optimization result storage of multi-stage slug algorithms, and finally send control instructions to injection equipment. The platform realizes the closed-loop linkage of monitoring, simulation, optimization and regulation, solves the problem of separation of monitoring and regulation and data interaction lag in the prior art, ensures the displacement process to be in the optimal state through real-time data feedback, and provides a stable technical operation environment for the thermal nitrogen foam multi-stage slug collaborative displacement of shallow heavy oil reservoirs.

[0057] As shown in Figure 2 The thermal nitrogen foam multi-stage slug collaborative displacement system of shallow heavy oil reservoirs comprises:

[0058] A thermal nitrogen foam preparation and injection unit, which is connected with a thermal nitrogen generating device, a foam generator and an injection pipeline, and is used for preparing thermal nitrogen foam according to optimized slug parameter combinations and conveying the thermal nitrogen foam to an injection well;

[0059] A reservoir parameter monitoring unit, which comprises a pressure sensor, a temperature sensor and a fluid component analyzer, and is connected to the thermal nitrogen foam displacement real-time monitoring and intelligent regulation platform through a data transmission line, and is used for collecting reservoir pressure, temperature and produced fluid component data;

[0060] An algorithm operation and model processing unit, which is connected to the thermal nitrogen foam displacement real-time monitoring and intelligent regulation platform through a data interface, and is internally provided with a multi-stage slug dynamic adaptive optimization algorithm and a pore-scale interface competition dynamic network model, and is used for performing slug parameter optimization and displacement process simulation calculation;

[0061] A data storage and management unit, which is connected to the thermal nitrogen foam displacement real-time monitoring and intelligent regulation platform, and is used for storing basic geological parameters, fluid physical property parameters, real-time monitoring data, simulation calculation results and optimized slug parameter combination data;

[0062] A system control and instruction output unit, which is connected with the hot nitrogen foam preparation and injection unit, the reservoir parameter monitoring unit and the algorithm operation and model processing unit respectively, is used for receiving the algorithm operation results and sending control instructions to each execution unit;

[0063] A data interaction and visualization unit, which is connected with the data storage and management unit and the system control and instruction output unit, is used for displaying the monitoring data, simulation data and control instructions in a visual form and supporting data export and external device interaction.

[0064] The method and system of hot nitrogen foam multi-stage slug cooperative displacement in shallow heavy oil reservoirs. The method forms a dynamic matching ability of slug parameters and reservoir characteristics through the cooperative application of a multi-stage slug dynamic adaptive optimization algorithm and a pore-scale interface competition dynamic network model, effectively overcoming the shortcomings of the prior art that slug parameter optimization relies on experience and has poor adaptability. The algorithm can adjust the injection pressure, rate, size and alternating period of the slug in real time according to the changes of the reservoir pore structure and fluid properties during the displacement process, and the model can accurately simulate the migration of hot nitrogen foam in pores, the change of interfacial tension and gas channeling, avoid the problems of gas channeling and foam breaking too early due to fixed parameters, and significantly improve the adaptability of the slug and the reservoir, laying a foundation for efficient displacement.

[0065] Relying on the real-time monitoring and intelligent regulation and control platform of hot nitrogen foam displacement, the method and system solve the defects of the prior art that lack an integrated platform and cannot dynamically feedback and adjust. The platform integrates reservoir parameter monitoring, data processing and instruction output functions, compares and analyzes the real-time collected pressure, temperature and produced liquid component data with the model simulation results, corrects the simulation deviation in time, and avoids regulation and control lag. At the same time, the platform supports continuous iteration and optimization of the algorithm and the model, ensures that each parameter is in the optimal state during the displacement process, breaks the limitation of traditional monitoring and regulation separation, and realizes dynamic control of the displacement process.

[0066] The cooperative operation of each unit of the system and the step-by-step optimization process of the method further strengthen the technical advantages. The hot nitrogen foam preparation and injection unit, the reservoir monitoring unit, the algorithm operation unit and the data management unit form a closed loop, seamlessly connecting from parameter optimization to field execution; the iteration steps of multiple simulation and actual data comparison in the method ensure the accuracy and stability of displacement. This integrated design of "algorithm-model-platform-system" fully overcomes the shortcomings of the background technology, ultimately improves the recovery ratio and development efficiency of shallow heavy oil reservoirs, and meets the needs of efficient development of complex reservoirs.

[0067] In the description of the application, it should be noted that unless otherwise explicitly specified and limited, the terms "arranged", "mounted", "connected", "linked", "fixed" should be understood broadly, for example, can be fixedly connected, can be detachably connected, or integrally connected; can be mechanically connected, can be electrically connected; can be directly connected, can be indirectly connected through an intermediate medium, or can be internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0068] Although embodiments of the present application have been shown and described, it would be appreciated by those of ordinary skill in the art that various equivalents, modifications, replacements and variations of these embodiments can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalent scope.

Claims

1. A method for coordinated displacement of shallow heavy oil reservoirs using multi-stage slugs and thermal nitrogen foam, characterized in that: include: Step S1: Based on the geological parameters and fluid properties of shallow heavy oil reservoirs, construct the basic database for a real-time monitoring and intelligent control platform for hot nitrogen foam displacement. The geological parameters include reservoir porosity, permeability, and interlayer distribution characteristics, while the fluid properties include heavy oil viscosity, density, hot nitrogen gas diffusion coefficient, and foam stability parameters. Step S2: Call the multi-stage slug dynamic adaptation optimization algorithm to perform preliminary iterative calculations on the injection pressure, injection rate, slug size, and slug alternation cycle parameters of the hot nitrogen foam multi-stage slugs, obtaining an initial slug parameter combination. Step S3: Input the initial slug parameter combination into the pore-scale interface competition dynamic network model to simulate the migration path of hot nitrogen foam in reservoir pores, changes in interfacial tension, and the degree of gas channeling, outputting simulated displacement efficiency data. Step S4: Real-time monitoring and intelligent control platform for hot nitrogen foam displacement. The monitoring and intelligent control platform collects pressure response signals, temperature distribution data, and produced fluid composition data during the actual displacement process on site, and compares and analyzes them with the simulated displacement efficiency data output in step S3; Step S5: Based on the comparative analysis results, the slug parameter combination is adjusted through the multi-stage slug dynamic adaptation optimization algorithm, and the boundary conditions of the pore-scale interface competition dynamic network model are updated synchronously. Steps S3-S4 are repeated until the deviation between the simulated data and the actual monitoring data meets the preset threshold; Step S6: Based on the optimized slug parameter combination, the hot nitrogen foam injection equipment is controlled to inject multi-stage hot nitrogen foam slugs into the shallow heavy oil reservoir according to the set injection sequence and injection duration. At the same time, the hot nitrogen foam displacement real-time monitoring and intelligent control platform continuously collects displacement process data and feeds it back to the multi-stage slug dynamic adaptation optimization algorithm; The multi-level slug dynamic adaptation optimization algorithm uses the following expression to calculate the optimized values ​​of slug parameters: ,in, Inject pressure into the optimized slug. For slug series, For the first The weighting coefficient of the stage block, For the first The initial injection pressure of the stage plug, For the first Displacement efficiency coefficient of stage plug. This is the pressure compensation coefficient. This represents the maximum pressure difference in the reservoir. The flow loss coefficient is... This represents the loss due to hot nitrogen gas crossflow. The reservoir sensitivity coefficient is... This represents the remaining oil saturation of the reservoir.

2. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, The pore-scale interface competition dynamic network model uses the following expression to calculate the foam interface tension change: ,in, For dynamic interface tension, The initial interfacial tension, The interfacial tension attenuation coefficient, To replace time, For reservoir porosity, For gas phase saturation, The viscosity of hot nitrogen gas, The fluid flow coefficient, For the viscosity of heavy oil, For oil phase saturation, The surfactant influence coefficient is... This refers to the surfactant concentration. For the volume of the foam, This represents the pore interface area.

3. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, The real-time monitoring and intelligent control platform for thermal nitrogen foam displacement uses the following expression to calculate the real-time displacement efficiency: ,in, To improve real-time displacement efficiency. To calculate cumulative oil production, The density of heavy oil, This is the crude oil volume coefficient. To replace time, Inject flow rate into the hot nitrogen foam. For the output liquid flow rate, The density of hot nitrogen gas, The gas volume coefficient, Original geological reserves, Correction coefficients for monitoring data.

4. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, The multi-stage slug dynamic adaptation optimization algorithm adjusts the slug alternation period using the following expression: ,in, For the slug alternation cycle, The reservoir pore volume, For reservoir porosity, This represents the current oil phase saturation. To achieve the target oil phase saturation, Inject flow rate into the hot nitrogen foam. To improve slug volume utilization, This is the temperature influence coefficient. This represents the reservoir temperature change value. This represents the temperature difference between the injected hot nitrogen and the original reservoir temperature.

5. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, The pore-scale interface competition dynamic network model calculates the degree of gas channeling using the following expression: ,in, The gas channeling coefficient, The number of pore throats, For the first The cross-sectional area of ​​the throat. For the first Gas flow rate within the throat For the first The maximum permissible flow rate of each throat. The density of hot nitrogen gas, This is the tortuosity coefficient of the larynx. For the viscosity of heavy oil, The viscosity of hot nitrogen gas, This represents the water phase saturation.

6. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S31: Extract reservoir pore structure parameters and fluid property parameters from the basic database of the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement. Transform the pore structure parameters into the topological data of nodes and edges in a dynamic network model of interfacial competition at the pore scale. The pore structure parameters include pore radius distribution, throat length, and pore connectivity; the fluid property parameters include viscosity, density, and interfacial tension of each phase. Step S32: Transform the injection pressure and injection rate from the initial slug parameter combination obtained in Step S2 into the inlet boundary conditions of the model. Set the initial reservoir temperature and pressure as... Initial boundary conditions are established to determine the time step and number of iterations for the model. Step S33: Based on the set boundary conditions and topology data, the distribution ratio of thermal nitrogen foam in each pore unit, oil phase flow velocity, and gas diffusion distance are calculated using a pore-scale interface competition dynamic network model at each time step, and the change curves of each parameter are recorded. Step S34: Based on the calculation results of each time step, the oil phase recovery rate, gas retention rate, and foam collapse rate during the displacement process are statistically analyzed. The data are integrated into simulated displacement efficiency data and output to the thermal nitrogen foam displacement real-time monitoring and intelligent control platform.

7. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, Step S4 includes the following sub-steps: Step S41: Start the pressure sensor, temperature sensor, and fluid composition analyzer of the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, set the sensor acquisition frequency and data transmission interval, and ensure the stability of the data communication link between each monitoring device and the platform; Step S42: Collect the pressure values ​​of different monitoring points in the reservoir through the pressure sensor to generate a pressure response curve; collect the temperature distribution data between the injection well and the production well through the temperature sensor to form a temperature field distribution map; Step S43: The oil, gas and water content ratios in the produced fluid are detected by a fluid composition analyzer; Step S44: The collected pressure response curve, temperature field distribution map and produced fluid composition data are sorted according to the time series, and outliers and noise signals in the data are removed to obtain standardized actual monitoring data; Step S45: The simulated displacement efficiency data output in Step S3 is retrieved from the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, and the actual monitoring data and simulated data are correlated according to the same time node to calculate the deviation values ​​of the two in terms of pressure, temperature and recovery rate parameters.

8. The method for synergistic displacement of shallow heavy oil reservoirs using thermal nitrogen foam multi-stage slugs according to claim 1, characterized in that, Step S5 includes the following sub-steps: Step S51: Input the deviation value calculated in Step S4 into the multi-level slug dynamic adaptation optimization algorithm. The algorithm determines the type of slug parameter to be adjusted based on the magnitude and direction of the deviation value. If the pressure deviation exceeds the threshold, the injection pressure is adjusted first. If the recovery rate deviation exceeds the threshold, the slug size and alternation cycle are adjusted first. Step S52: According to the parameter adjustment direction determined by the algorithm, the corresponding parameters in the initial slug parameter combination are incrementally adjusted. The adjustment range is determined based on the deviation value and the preset adjustment coefficient to avoid parameter mutations that may affect reservoir stability. Step S53: Input the adjusted slug parameter combination into the pore-scale interface competition dynamic network model. At the same time, update the boundary conditions of the model based on the actual monitored reservoir temperature and pressure changes, including the inlet flow boundary, outlet pressure boundary, and initial saturation distribution. Step S54: Repeat the simulation calculation in Step S3 and compare it with the actual data acquisition in Step S4 until the deviation values ​​of all parameters are less than the preset threshold. At this time, record the current slug parameter combination as the optimized slug parameter combination.

9. A multi-stage slug synergistic displacement system for shallow heavy oil reservoirs using thermal nitrogen foam, characterized in that: This system is applied to the shallow heavy oil reservoir thermal nitrogen foam multi-stage slug synergistic displacement method as described in claim 1, comprising: The hot nitrogen foam preparation and injection unit is connected to the hot nitrogen generating equipment, foam generator, and injection pipeline. It is used to prepare hot nitrogen foam according to the optimized slug parameter combination and deliver it to the injection well. The reservoir parameter monitoring unit includes a pressure sensor, a temperature sensor, and a fluid composition analyzer. The sensors and analyzer are connected to the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement via a data transmission line to collect data on reservoir pressure, temperature, and produced fluid composition. The algorithm operation and model processing unit is connected to the real-time monitoring and intelligent control platform for hot nitrogen foam displacement through a data interface. It has a built-in multi-level slug dynamic adaptation optimization algorithm and a dynamic network model of pore scale interface competition, which is used to perform slug parameter optimization and displacement process simulation calculation. The data storage and management unit is connected to the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement. It is used to store basic geological parameters, fluid property parameters, real-time monitoring data, simulation calculation results, and optimized slug parameter combination data. The system control and command output unit is connected to the hot nitrogen foam preparation and injection unit, the reservoir parameter monitoring unit, and the algorithm calculation and model processing unit, respectively. It is used to receive the algorithm calculation results and send control commands to each execution unit. The data interaction and visualization unit, which is connected to the data storage and management unit and the system control and command output unit, is used to display monitoring data, simulation data and control commands in a visual form, and supports data export and interaction with external devices.

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