Shallow heavy oil reservoir hot nitrogen foam multi-stage slug synergistic displacement method and system

Abstract

The invention discloses a shallow heavy oil reservoir hot nitrogen foam multi-stage slug cooperative displacement method and system, and the method comprises the steps: constructing a basic database, calling a multi-stage slug dynamic adaptation optimization algorithm to obtain an initial slug parameter combination, inputting a pore scale interface competition dynamic network model to simulate the displacement efficiency, and carrying out the multi-stage slug cooperative displacement. Actual data are collected and compared through hot nitrogen foam displacement real-time monitoring and an intelligent regulation and control platform, parameters are adjusted, model boundary conditions are updated till deviation reaches the standard, slug injection is conducted according to optimized parameters, and monitoring and feedback are continuously conducted. The system comprises a hot 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 and a data interaction and visualization unit which work cooperatively. According to the method and system, slug parameter dynamic optimization and displacement process real-time management and control are achieved, the adaptability of slugs and reservoirs is improved, gas channeling is reduced, and the recovery efficiency and development efficiency of shallow heavy oil reservoirs are improved.

Description

Technical Field

[0001] This invention relates to the fields of fire alarms, fire sensor hardware and algorithms, and particularly to a method and system for multi-stage slug-driven displacement of hot nitrogen foam in shallow heavy oil reservoirs. Background Technology

[0002] Shallow heavy oil reservoirs, as important oil and gas resources, are significantly affected by the high viscosity of heavy oil and the heterogeneity of the reservoirs, making it difficult to achieve efficient recovery using conventional displacement methods. With the development of oil and gas development technologies, thermal nitrogen foam displacement has become an important direction for the development of shallow heavy oil reservoirs due to its dual effects of viscosity reduction and profile modification. However, shallow heavy oil reservoirs have complex pore structures and strong fluid interface interactions. The compatibility between slug parameters and reservoir conditions during thermal nitrogen foam injection directly affects the displacement effect. Furthermore, thermal nitrogen gas is prone to cross-flow during displacement, and foam stability is easily affected by the reservoir environment. Therefore, it is urgent to construct a synergistic displacement technology system that takes into account parameter optimization, process simulation, real-time monitoring, and intelligent control to solve the problems of low recovery rate and insufficient development efficiency in shallow heavy oil reservoirs.

[0003] Existing technologies in the field of thermal nitrogen foam displacement in shallow heavy oil reservoirs have two significant drawbacks: First, slug parameter optimization relies heavily on experience or simple models, lacking an optimization mechanism that dynamically correlates with the interfacial competition characteristics at the reservoir pore scale. This makes it impossible to adjust the injection pressure, rate, size, and alternation cycle of the slugs in real time according to changes in reservoir conditions during displacement, resulting in poor slug-reservoir compatibility and a tendency for gas channeling or premature foam collapse, thus affecting displacement efficiency. Second, there is a lack of an integrated platform that combines real-time monitoring and dynamic simulation. Data collected by existing monitoring equipment cannot be quickly interacted with the pore-scale displacement simulation model, making it impossible to correct simulation deviations in a timely manner. Furthermore, the transmission of control commands is lagging, hindering dynamic feedback and intelligent adjustment of the displacement process and failing to meet the needs of efficient development under complex conditions in shallow heavy oil reservoirs. Summary of the Invention

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

[0005] The technical solution adopted in this invention is a method for coordinated displacement of shallow heavy oil reservoirs using multi-stage hot nitrogen foam slugs, comprising: Step S1: Based on geological parameters and fluid properties of shallow heavy oil reservoirs, constructing a 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: Calling a 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 multi-stage hot nitrogen foam slugs, obtaining an initial slug parameter combination; Step S3: Inputting the initial slug parameter combination into a pore-scale interface competition dynamic network model to simulate the migration path, interfacial tension changes, and gas channeling degree of hot nitrogen foam in reservoir pores, outputting the simulated displacement efficiency. Data; Step S4: Collect pressure response signals, temperature distribution data, and produced fluid composition data during the actual displacement process through the real-time monitoring and intelligent control platform for hot nitrogen foam displacement, and compare and analyze them with the simulated displacement efficiency data output in Step S3; Step S5: Based on the comparative analysis results, adjust the slug parameter combination through the multi-stage slug dynamic adaptation optimization algorithm, and simultaneously update the boundary conditions of the pore-scale interface competition dynamic network model. Repeat Steps S3-S4 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, 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 duration. At the same time, continuously collect displacement process data through the real-time monitoring and intelligent control platform for hot nitrogen foam displacement and feed it back to the multi-stage slug dynamic adaptation optimization algorithm.

[0006] Furthermore, 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. 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.

[0007] Furthermore, the pore-scale interface competition dynamic network model calculates the foam interface tension variation using the following expression: ,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.

[0008] Furthermore, 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.

[0009] Furthermore, 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.

[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, step S4 includes the following sub-steps: Step S41: Activate 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 pressure values ​​at different monitoring points in the reservoir through the pressure sensor to generate a pressure response curve; collect temperature distribution data between the injection well and the production well through the temperature sensor to form a temperature field distribution map; detect the proportions of oil, gas, and water in the produced fluid through the fluid composition analyzer; Step S43: Organize the collected pressure response curve, temperature field distribution map, and produced fluid composition data according to the time series, remove outliers and noise signals from the data, and obtain standardized actual monitoring data; Step S44: Retrieve the simulated displacement efficiency data output in step S3 from the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, match the actual monitoring data and the simulated data according to the same time node, and calculate the deviation values ​​of the two in terms of pressure, temperature, and recovery rate parameters.

[0013] Further, 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 affecting 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.

[0014] A multi-stage slug synergistic displacement system for shallow heavy oil reservoirs using thermal nitrogen foam includes: 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.

[0015] Beneficial Effects: This invention proposes a method and system for coordinated displacement of shallow heavy oil reservoirs using multi-stage hot nitrogen foam slugs. By combining a multi-stage slug dynamic adaptation optimization algorithm with a dynamic network model of pore-scale interface competition, a dynamic correlation mechanism between slug parameters and reservoir porosity characteristics is established. This allows for real-time adjustment of slug injection pressure, rate, size, and alternation cycle based on changes in reservoir conditions during displacement. This effectively solves the problems of slug parameter optimization relying on experience and poor adaptability in existing technologies, reduces hot nitrogen gas cross-flow and premature foam collapse, and improves displacement efficiency. Furthermore, relying on a real-time monitoring and intelligent control platform for hot nitrogen foam displacement, it integrates reservoir... The system integrates layer parameter monitoring, algorithm calculation, data management, and system control functions, enabling rapid interaction between real-time monitoring data and pore-scale simulation models. This allows for timely correction of simulation deviations and avoids delays in control commands, overcoming the shortcomings of existing technologies that lack an integrated platform and cannot provide dynamic feedback adjustments. Simultaneously, the collaborative work of each unit forms a complete closed loop from hot nitrogen foam preparation and injection to data interaction and visualization. Combined with the process of multiple simulations and comparisons with actual data to optimize the process, the accuracy and stability of the displacement process are further ensured, ultimately improving the recovery rate and development efficiency of shallow heavy oil reservoirs and meeting the needs for efficient development under complex reservoir conditions. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the multi-stage slug synergistic displacement method using thermal nitrogen foam in shallow heavy oil reservoirs includes: 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 thermal nitrogen foam displacement. The geological parameters include reservoir porosity, permeability, and interlayer distribution characteristics, while the fluid properties include heavy oil viscosity, density, thermal nitrogen gas diffusion coefficient, and foam stability parameters. Specifically, step S1 involves constructing the foundational database for a real-time monitoring and intelligent control platform for thermal nitrogen foam displacement. This database serves as the core data support for all subsequent parameter calculations and simulations, and its completeness and accuracy directly impact the reliability of subsequent slug optimization and displacement simulation. During implementation, geological parameters of shallow heavy oil reservoirs are first obtained through geological exploration. For reservoir porosity, at least 30 sets of data from different well sections must be collected, with values ​​controlled within the range of 20%-35%. Permeability data must distinguish between horizontal and vertical directions, with horizontal permeability ranging from 500-2500 mD and vertical permeability being 1 / 5-1 / 3 of the horizontal permeability. Interlayer distribution characteristics must be recorded, including interlayer thickness, distribution depth, and continuous length, with interlayer thickness data accuracy controlled to 0.1 m. The distribution depth error should not exceed 0.5m. Subsequently, fluid properties were determined through laboratory experiments. Heavy oil viscosity needed to be measured at reservoir temperature, within a range of 40-60℃, corresponding to a viscosity value of 1000-5000 mPa·s. Heavy oil density measurement accuracy was 0.001 g / cm³, with a value range of 0.92-0.98 g / cm³. The hot nitrogen gas diffusion coefficient needed to be measured under different pressures, within a pressure range of 5-15 MPa, with a diffusion coefficient value of 1.0 × 10⁻⁻⁻⁶. 9 -3.0×10⁻ 9 The foam stability parameter was determined by measuring the foam half-life, which ranged from 30 to 120 minutes. All parameters were categorized as "geological parameters - fluid properties" and stored in the platform database. A data verification mechanism was established to perform secondary checks on data exceeding the normal range, ensuring that the error rate in the database was less than 1%, thus providing an accurate data foundation for subsequent steps.

[0019] 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 slug to obtain the initial slug parameter combination; Specifically, step S2 obtains the initial slug parameter combination through a multi-stage slug dynamic adaptation optimization algorithm, providing an initial parameter benchmark for subsequent displacement simulations and avoiding excessive deviation between simulation results and reality due to blind parameter setting. During implementation, core data such as reservoir permeability, heavy oil viscosity, and foam stability parameters are first retrieved from the basic database as algorithm input parameters. After the algorithm starts, the slug stage range is initially set to 3-5 stages. The initial injection pressure of each slug stage is determined based on the reservoir fracturing pressure, which is measured to be 8-18 MPa through geomechanical experiments. The initial injection pressure is set to 60%-80% of the fracturing pressure, i.e., 4.8-14.4 MPa. The initial injection rate is determined in conjunction with the injection pressure... The well spacing and reservoir permeability are determined. When the well spacing is 200-500m, the initial injection rate is set to 50-150m³ / d. The initial slug size is calculated based on the reservoir pore volume, which is obtained by calculating the well area, reservoir thickness, and porosity. The initial slug size is set to 5%-10% of the pore volume. The initial slug alternation period is calculated by combining the injection rate and slug size, i.e., the slug size divided by the injection rate, resulting in an initial alternation period of 10-30d. The algorithm performs 3-5 iterations to initially adjust the above parameters. During the iteration process, the constraints are "injection pressure does not exceed the fracturing pressure and injection rate matches the reservoir permeability." Finally, a set of initial slug parameter combinations that meet the constraints is output. The parameter combination must include the specific injection pressure, injection rate, slug size, and alternation period values ​​for each slug stage, and each parameter value must be retained to two decimal places to ensure parameter accuracy.

[0020] Step S3: Input the initial slug parameter combination into the pore-scale interface competition dynamic network model to simulate the migration path, interfacial tension change, and gas channeling degree of hot nitrogen foam in the reservoir pores, and output simulated displacement efficiency data. Specifically, step S3 involves inputting the initial slug parameter combination into the pore-scale interface competition dynamic network model for displacement simulation. The model predicts the displacement process, identifying potential problems such as gas channeling and low displacement efficiency in advance, providing a predictive basis for subsequent actual displacement. During implementation, the injection pressure and injection rate from the initial slug parameter combination output in step S2 are first 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. Simultaneously, reservoir pore structure parameters are retrieved from the basic database, including pore radius distribution (range 5-50 μm), throat length (range 10-100 μm), and pore connectivity (expressed as connectivity probability, range 0.7-0.95). These parameters are then converted into topological 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 based on the connectivity probability to be 1500-3000. The initial boundary conditions for the model also need to be set as follows: reservoir initial temperature (40-60℃), initial pressure (5-15MPa), time step (1-6h), and iteration count (50-100). During model operation, after each time step calculation is completed, the distribution ratio of thermal nitrogen foam in each pore element (expressed as a volume fraction, with three decimal places) and oil phase flow velocity (range 1×10⁻⁻⁻⁻⁶) within that time step are output. 6 -1×10⁻ 5 The simulation results are calculated based on the following parameters: oil recovery rate (range: m / s), gas diffusion distance (range: 0.1-1m); after all iterations are completed, the oil recovery rate (range: 20%-40%), gas retention rate (range: 30%-60%), and foam bursting rate (range: 10%-30%) during the entire displacement process are statistically analyzed. These statistical data are then organized into simulated displacement efficiency data according to the correspondence between "time-parameter value". The data should be output in tabular form, with four columns: time, recovery rate, gas retention rate, and foam bursting rate. Each column should retain two decimal places. Curves showing the change of each parameter over time should also be generated, with the horizontal axis representing time (unit: d) and the vertical axis representing the parameter value, ensuring that the simulation results are intuitive and accurate.

[0021] Step S4: Collect pressure response signals, temperature distribution data, and product liquid composition data during the actual displacement process on site through the real-time monitoring and intelligent control platform for hot nitrogen foam displacement, and compare and analyze them with the simulated displacement efficiency data output in Step S3. Specifically, step S4 involves collecting actual displacement data through a real-time monitoring and intelligent control platform for thermal nitrogen foam displacement and comparing it with simulated data to establish a correlation between simulation and reality. By comparing the two, discrepancies are identified, providing a basis for subsequent parameter adjustments. During implementation, the platform's associated monitoring equipment is first activated, including 5-10 pressure sensors (distributed at depths matching reservoir thickness, with a depth error not exceeding 0.5m) and 5-10 temperature sensors (deployed in the same locations as the pressure sensors) deployed at different locations between injection and production wells, and a fluid composition analyzer (with a detection accuracy of 0.1%) installed at the production well outlet. The sensor acquisition frequency is set to 1-2 times / hour, and the data transmission interval is 0.5-1 hour. Data is transmitted to the platform in real-time via wired transmission, ensuring a data transmission delay of no more than 10 seconds. Pressure sensors collect pressure values ​​at different monitoring points in the reservoir and generate pressure response curves. The curves must indicate the pressure change trend at each monitoring point, and the pressure values ​​must be accurate to two decimal places (unit: MPa). Temperature sensors collect temperature data at each monitoring point and generate a temperature field distribution map. The distribution map must use the line connecting the injection and production wells as the horizontal axis and depth as the vertical axis, and use different colors to mark temperature ranges (intervals of 5℃). A fluid composition analyzer detects the proportions of oil, gas, and water in the produced fluid. The detection results are recorded in the order of "oil phase - gas phase - water phase", and the proportion values ​​are accurate to two decimal places (unit: %). The collected pressure response curves, temperature field distribution maps, and produced liquid component data were organized according to time series. The moving average method was used to remove outliers (outliers were judged as exceeding the average of adjacent data by ±10%) and noise signals to obtain standardized actual monitoring data. Then, the simulated displacement efficiency data output from step S3 was retrieved from the platform. The actual monitoring data and simulated data were matched at the same time nodes (1 day interval). The deviations between the two data were calculated in terms of pressure (allowable deviation range ±0.5MPa), temperature (allowable deviation range ±2℃), and recovery rate (allowable deviation range ±3%). The deviation was calculated as the absolute value of (actual value - simulated value). The calculation results were rounded to two decimal places to form a deviation analysis report.

[0022] Step S5: Based on the comparative analysis results, adjust the slug parameter combination through the multi-level slug dynamic adaptation optimization algorithm, and synchronously update the boundary conditions of the pore-scale interface competition dynamic network model. Repeat steps S3-S4 until the deviation between the simulated data and the actual monitoring data meets the preset threshold. Specifically, step S5 involves adjusting the slug parameters and updating the model boundary conditions based on the deviation analysis results. Through repeated iterations, the simulation and actual data are matched to ensure the accuracy of parameters during subsequent displacement and improve actual displacement efficiency. During implementation, the deviation values ​​from the deviation analysis report generated in step S4 are first input into the multi-level slug dynamic adaptation optimization algorithm. The algorithm first determines whether the deviations of each parameter exceed preset thresholds (pressure deviation threshold is 0.5 MPa, temperature deviation threshold is 2℃, and recovery rate deviation threshold is 3%). If the pressure deviation exceeds the threshold, the injection pressure is adjusted first, with the adjustment range determined based on the deviation value. For every 0.1 MPa the deviation exceeds the threshold, 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, the slug size and alternation cycle are adjusted first, with the slug size adjustment range being 5%-10% of the original size and the alternation cycle adjustment range being 10%-15% of the original cycle. After the parameters are adjusted, a new slug parameter combination is generated. At the same time, based on the reservoir temperature changes (difference between actual temperature and original temperature, ranging from 5 to 20°C) and pressure changes (difference between actual pressure and original pressure, ranging from -2 to 5 MPa) collected in step S4, the boundary conditions of the pore-scale interface competition dynamic network model are updated. This includes setting the inlet flow boundary according to the new injection rate, setting the outlet pressure boundary according to the actual monitored production well pressure, and setting the initial saturation distribution according to the actual monitored oil phase saturation (ranging from 40% to 70%). Then, the simulation calculation process in step S3 is repeated, using the updated boundary conditions and the new slug parameter combination to perform model calculations and obtain new simulated displacement efficiency data; then, the actual data acquisition and comparison process in step S4 is repeated to calculate the new deviation value; this process is repeated in a loop, and after each iteration, it is re-evaluated whether the deviation value is less than the preset threshold, until all parameter deviation values ​​meet the requirements. At this point, the current slug parameter combination is recorded as the optimized slug parameter combination. The optimized parameter combination needs to be verified three times consecutively to ensure that the deviation value in each verification is within the threshold range to avoid parameter fluctuations. The verification interval is 12-24 hours. After successful verification, the optimized parameters are stored in the platform database.

[0023] 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 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.

[0024] 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.

[0025] 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. 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.

[0026] Specifically, the multi-stage slug dynamic adaptation optimization algorithm provides a quantitative basis for the precise adjustment of slug injection pressure by calculating the optimized slug parameters, avoiding excessively high injection pressure leading to reservoir damage or excessively low pressure affecting displacement efficiency. During implementation, the number of slug stages is first determined. Based on the reservoir thickness and heterogeneity of shallow heavy oil reservoirs, the number of slug stages is typically set to 3-5. Then, a weight coefficient is assigned to each slug stage. The weight coefficient is determined based on the role of each slug in the displacement process. The weight coefficient for slugs in high-permeability reservoir areas is 0.25-0.35, and the weight coefficient for slugs in low-permeability areas is 0.15-0.25, ensuring the total weight coefficient is 1. The initial injection pressure is determined based on the reservoir fracturing pressure, which is measured to be 8-18 MPa through core experiments. The initial injection pressure is set to 60%-80% of the fracturing pressure. The displacement efficiency coefficient is obtained through previous simulation experiments, ranging from 0.6 to 0.9, and the value increases with the improvement of slug-reservoir compatibility. The pressure compensation coefficient is set based on the reservoir pressure decay, with a value of 0.05-0.15; the maximum reservoir pressure difference is the difference between the original reservoir pressure and the production well pressure, with a value range of 3-10 MPa; the flow loss coefficient is determined in conjunction with the hot nitrogen gas crossflow characteristics, with a value of 0.1-0.2; the hot nitrogen gas crossflow loss is calculated by monitoring the gas production of the production well, with a value range of 5-20 m³ / d; the reservoir sensitivity coefficient is determined based on reservoir rock sensitivity experiments, with a value of 0.02-0.08; the reservoir residual oil saturation is obtained through well logging data, with a value range of 30%-60%. Substituting the above parameters into the algorithm expression, the optimized slug injection pressure is calculated. The calculation result must be retained to two decimal places, and it is necessary to verify whether the optimized pressure is within a safe range (not exceeding the reservoir fracture pressure and not lower than the reservoir initiation pressure). Only after successful verification can it be used for subsequent displacement operations.

[0027] Preferably, the pore-scale interface competition dynamic network model uses the following expression to calculate the foam interface tension change: ,in, For dynamic interface tension, For the initial interface 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.

[0028] Specifically, by using the expression of the dynamic network model of interfacial competition at the pore scale, the dynamic interfacial tension changes of foam are accurately calculated, and the stability law of foam in reservoir pores is understood, providing data support for adjusting slug parameters and reducing premature foam collapse. During implementation, the initial interfacial tension is measured using a laboratory surface tension meter. Under the condition of surfactant concentration of 0.1%-0.5%, the initial interfacial tension ranges from 15-30 mN / m. The interfacial tension attenuation coefficient is determined based on aging experiments of foam in reservoir fluids, with a value of 0.01-0.05 h⁻¹, where a larger value indicates faster interfacial tension attenuation. The displacement time is set according to the slug injection cycle, with a value ranging from 10-30 days. Reservoir porosity was obtained through core analysis, with values ​​ranging from 20% to 35%; gas saturation was calculated by combining well logging and production data, with values ​​ranging from 10% to 40%; hot nitrogen gas viscosity was measured under reservoir temperature (40-60℃) and pressure (5-15MPa) conditions, with values ​​ranging from 1.5 × 10⁻⁻⁻⁶. 5 -2.5×10⁻ 5 The fluid flow coefficient is determined by combining reservoir permeability and fluid viscosity, ranging from 0.001 to 0.01 m² / (Pa·s); heavy oil viscosity is measured at reservoir temperature, ranging from 1000 to 5000 mPa·s; oil phase saturation is obtained from well logging data, ranging from 40% to 70%. The surfactant influence coefficient is determined based on surfactant type and concentration, ranging from 0.02 to 0.08; surfactant concentration is determined based on foam stability experiments, ranging from 0.1% to 0.5%; foam volume is calculated based on slug size and injection rate, ranging from 1000 to 5000 m³; pore interface area is calculated based on reservoir pore structure parameters, combined with pore radius and number, ranging from 1 × 10⁻⁶. 4 -5×10 4 m². Substitute the above parameters into the model expression to calculate the dynamic interfacial tension at different displacement times. The calculation results need to be organized into a time series to form an interfacial tension change curve. The curve needs to be marked with the interfacial tension values ​​at key time nodes to provide an intuitive basis for judging foam stability. If the interfacial tension decays too quickly (the decay rate at a certain time node exceeds 30% of the initial value), the surfactant concentration or slug injection rate needs to be adjusted.

[0029] Preferably, 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.

[0030] Specifically, the real-time displacement efficiency is calculated using the expression of the hot nitrogen foam displacement real-time monitoring and intelligent control platform. This allows for real-time monitoring of the actual displacement effect, timely detection of problems during the displacement process, and adjustment of strategies to avoid resource waste. During implementation, the cumulative oil production is obtained through production well metering equipment with a metering accuracy of 0.1 m³ and a value range of 500-5000 m³. The heavy oil density is measured under laboratory conditions, with a value range of 0.92-0.98 g / cm³. The crude oil volume coefficient is determined based on reservoir pressure and temperature, with a value range of 1.05-1.25. The hot nitrogen foam injection flow rate was measured using a flow meter in the injection well, with a measurement accuracy of 0.1 m³ / d and a range of 50-150 m³ / d. The produced fluid flow rate was measured using a flow meter in the production well, with a range of 30-120 m³ / d. The hot nitrogen gas density was calculated under reservoir temperature and pressure conditions, with a range of 2.0-5.0 kg / m³. The gas volume coefficient was determined based on reservoir pressure and temperature, with a range of 50-200 m³ / m³. The original geological reserves were calculated using reservoir volume, porosity, and oil saturation, with a range of 1×10⁻⁶. 5 -1×10 6 m³; The monitoring data correction coefficient is determined based on the accuracy of the monitoring equipment and the on-site environment, with a value range of 0.95-1.05, used to correct monitoring data deviations caused by equipment errors or environmental interference. During calculation, firstly, injection flow rate and produced fluid flow rate data are collected in time series, and the difference between the two is integrated, with the integration interval from the start of displacement to the current time. Then, the integration result is combined with relevant parameters of cumulative oil production and substituted into the expression to calculate the real-time displacement efficiency, with the result rounded to two decimal places. The real-time displacement efficiency needs to be calculated every 24 hours and compared with the preset target displacement efficiency (range 30%-50%). If it is more than 10% lower than the target value, the slug parameter adjustment process needs to be initiated to ensure a stable increase in displacement efficiency.

[0031] Preferably, 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.

[0032] Specifically, the slug alternation cycle is adjusted using a multi-stage slug dynamic adaptation optimization algorithm to match the slug alternation rhythm with the reservoir fluid migration pattern. This avoids decreased displacement efficiency due to excessively long cycles or increased operating costs due to excessively short cycles. During implementation, the reservoir pore volume is calculated using the well network area, reservoir thickness, and porosity. The well network area is determined based on the injection-production well spacing (when the injection-production well spacing is 200-500m, the well network area is 4×10⁻⁶ m). 4 -2.5×10 5 The reservoir thickness (m²) was obtained from well logging data (range 5-20m), and the porosity ranged from 20%-35%. The calculated pore volume ranged from 5×10⁻⁶ m². 4 -2×10 6m³. Current oil phase saturation is obtained from recent well logging data, ranging from 40% to 70%; target oil phase saturation is determined according to the development plan, ranging from 20% to 30%; hot nitrogen foam injection flow rate is monitored in real-time by the injection well flow meter, ranging from 50 to 150 m³ / d; slug volume utilization rate is determined based on previous displacement experiments, ranging from 0.7 to 0.9, reflecting the proportion of effective slug action on the reservoir. The temperature influence coefficient is determined based on the degree of influence of reservoir temperature changes on fluid viscosity, ranging from 0.03 to 0.08; the reservoir temperature change value is the difference between the current average reservoir temperature and the original temperature, ranging from 5 to 20℃; the difference between the injected hot nitrogen temperature and the original reservoir temperature is monitored by a temperature sensor, with the injected hot nitrogen temperature controlled at 120-180℃ and the original temperature at 40-60℃, the difference ranging from 80-140℃. Substituting the above parameters into the algorithm expression, the slug alternation cycle is calculated. The result is rounded to one decimal place, and the value range is typically 10-30 days. After calculation, the rationality of the cycle needs to be verified in conjunction with the switching capability of the on-site injection equipment. The equipment switching time should be controlled within 2-4 hours. If the cycle is too short, resulting in frequent equipment switching (switching interval less than the minimum stable operating time of the equipment, 8 hours), the slug volume utilization rate or target oil phase saturation needs to be adjusted appropriately, and the cycle recalculated until it is reasonable, ensuring a stable and efficient slug alternation process.

[0033] Preferably, 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.

[0034] Specifically, the degree of gas channeling is calculated using the expression of a dynamic network model of interfacial competition at the pore scale, quantifying the channeling risk of hot nitrogen gas in the reservoir. This provides a basis for taking profile control measures and optimizing slug parameters, avoiding reduced displacement efficiency due to ineffective gas channeling. During implementation, the number of pore throats is determined based on the reservoir pore structure model. A representative reservoir region is selected during model construction, with the number of throats ranging from 500 to 1500. The cross-sectional area of ​​each throat is calculated using pore structure parameters, combined with the throat radius (ranging from 2 to 20 μm), resulting in a cross-sectional area ranging from 1 × 10⁻¹¹ to 1 × 10⁻¹¹. 9 m². The gas velocity within each throat was calculated using model simulation, with values ​​ranging from 1 × 10⁻ 4 -1×10⁻³m / s; The maximum allowable flow velocity for each larynx is determined based on larynx stability experiments to avoid larynx damage caused by excessive flow velocity, and the value range is 2×10⁻³m / s. 4 -2×10⁻³ m / s; the density of hot nitrogen gas is calculated under reservoir temperature (40-60℃) and pressure (5-15MPa) conditions, with a value range of 2.0-5.0 kg / m³. The throat tortuosity coefficient is determined based on reservoir rock CT scan data, with a value range of 1.2-1.8, reflecting the degree of tortuosity of the throat path; the viscosity of heavy oil is measured at reservoir temperature, with a value range of 1000-5000 mPa・s; the water phase saturation is obtained from well logging data, with a value range of 10%-30%. Substituting the above parameters into the model expression, the gas channeling coefficient is calculated. The calculation result is retained to three decimal places, with a value range of 0.1-0.5. The larger the coefficient, the higher the risk of gas channeling. When the gas crossflow coefficient exceeds 0.3, control measures need to be initiated. The risk of crossflow can be reduced by increasing the surfactant concentration (increasing by 0.05%-0.1%) or reducing the slug injection rate (reducing by 5%-10%). After adjustment, the gas crossflow coefficient needs to be recalculated until the coefficient drops below 0.3 to ensure that the hot nitrogen gas mainly acts to displace crude oil rather than causing ineffective crossflow.

[0035] Preferably, 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... The initial boundary conditions are the original temperature and pressure of the layer, and the time step and number of iterations of the model are determined. Step S33: Based on the set boundary conditions and topology data, the distribution ratio of thermal nitrogen foam in each pore unit, the oil phase flow velocity, and the gas diffusion distance are calculated through the pore-scale interface competition dynamic network model, and the change curves of each parameter are recorded. Step S34: According to 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.

[0036] Specifically, step S3 clarifies the simulation process of the dynamic network model for interfacial competition at the pore scale. Step-by-step operation ensures the accuracy of the simulation data, laying the foundation for subsequent comparison with actual data. During implementation, step S31 involves extracting parameters from the basic database of the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement. Among the reservoir pore structure parameters, the pore radius distribution must cover the range of 5-50 μm, the throat length must be 10-100 μm, and pore connectivity must be expressed as a connectivity probability of 0.7-0.95. Among the fluid properties, the viscosity of each phase must be measured at a reservoir temperature of 40-60℃ and a pressure of 5-15 MPa, with an oil phase viscosity of 1000-5000 mPa·s and a gas phase viscosity of 1.5 × 10⁻⁻⁻⁶. 5 -2.5×10⁻ 5 The interfacial tension is 15-30 mN / m. These parameters are then converted into model topology data, determining the number of nodes (representing pores) to be 1000-2000 and the number of edges (representing throats) to be 1500-3000. Step S32: The injection pressure (4.8-14.4 MPa) and injection rate (50-150 m³ / d) in the initial slug parameters of S2 are converted into inlet boundary conditions. The initial boundary conditions are set as the reservoir's original temperature (40-60℃) and pressure (5-15 MPa), with a time step of 1-6 h and 50-100 iterations. Step S33: Based on the boundary conditions and topology data, the model calculates the distribution ratio of foam in each pore unit (volume fraction accurate to three decimal places) and the oil phase flow velocity (1×10⁻⁻⁻⁶) within each time step. 6 -1×10⁻ 5m / s, gas diffusion distance 0.1-1m, record parameter change curves. Step S34: Statistically analyze the oil recovery rate (20%-40%), gas retention rate (30%-60%), and foam collapse rate (10%-30%) during the displacement process, integrate them into simulated displacement efficiency data, and transmit them to the platform. The entire process must ensure that the data error rate of each step is less than 1% to ensure the reliability of the simulation results.

[0037] Preferably, step S4 includes the following sub-steps: Step S41: Activate 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 pressure values ​​at different monitoring points in the reservoir through the pressure sensor to generate a pressure response curve; collect temperature distribution data between the injection well and the production well through the temperature sensor to form a temperature field distribution map; detect the proportions of oil, gas, and water in the produced fluid through the fluid composition analyzer; Step S43: Organize the collected pressure response curve, temperature field distribution map, and produced fluid composition data according to the time series, remove outliers and noise signals from the data, and obtain standardized actual monitoring data; Step S44: Retrieve the simulated displacement efficiency data output in step S3 from the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, match the actual monitoring data and the simulated data according to the same time node, and calculate the deviation values ​​of the two in terms of pressure, temperature, and recovery rate parameters.

[0038] Specifically, step S4 involves collecting and processing actual monitoring data and comparing it with simulated data. This step-by-step process eliminates data errors and accurately identifies deviations between simulation and reality. During implementation, step S41: Activate the platform-associated monitoring equipment. Deploy 5-10 pressure sensors and 10 temperature sensors at different depths between injection and production wells, with a depth error not exceeding 0.5m. Install a fluid component analyzer at the production well outlet, with a detection accuracy of 0.1%. Set the sensor acquisition frequency to 1-2 times / hour and the data transmission interval to 0.5-1 hour. Use wired transmission to ensure a delay of no more than 10 seconds, and simultaneously check the stability of the equipment communication link. Step S42: Generate a response curve by collecting pressure values ​​from monitoring points using pressure sensors, retaining two decimal places for pressure value accuracy. Use temperature sensors to collect data to form a temperature field distribution map, dividing temperature intervals into 5℃ intervals and marking them with colors. Use the fluid component analyzer to detect the proportions of oil, gas, and water in the produced liquid, retaining two decimal places for the results. Step S43: Organize the collected data according to the time series, using a moving average method to remove outliers and noise exceeding ±10% of the average of adjacent data, obtaining standardized actual monitoring data. Step S44: Retrieve the simulated displacement efficiency data from the platform, and calculate the pressure deviation (allowable ±0.5MPa), temperature deviation (allowable ±2℃), and recovery rate deviation (allowable ±3%) according to the actual data with 1 day as the time node. The deviation values ​​are retained to two decimal places, and a deviation analysis report is generated to provide a clear direction for subsequent parameter adjustments. The entire process must ensure the timeliness of data processing and comparison, and the time from data collection to report generation should not exceed 2 hours.

[0039] Preferably, 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 affecting 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.

[0040] Specifically, the operation process in step S5 involves iterative optimization of slug parameter adjustment and model boundary updates in stages, gradually reducing the deviation between simulation and actual data to ensure that the final optimized parameters meet the actual displacement requirements. During implementation, step S51: Input the deviation values ​​from the deviation analysis report in S4 into the multi-level slug dynamic adaptation optimization algorithm to determine if they exceed preset thresholds (pressure 0.5 MPa, temperature 2℃, recovery rate 3%). If the pressure exceeds the deviation, adjust the injection pressure by 0.05-0.1 MPa for every 0.1 MPa deviation, without exceeding the reservoir fracture pressure by 8-18 MPa. If the recovery rate exceeds the deviation, adjust the slug size (5%-10% of the original size) and alternation period (10%-15% of the original period). Step S52: Adjust the parameters incrementally according to the direction determined by the algorithm to avoid abrupt changes affecting reservoir stability. After adjustment, generate a new slug parameter combination. Simultaneously, based on the actual monitored reservoir temperature changes of 5-20℃ and pressure changes of -2-5 MPa, update the model inlet flow rate, outlet pressure, and initial saturation distribution boundary conditions. Step S53: Substitute the new parameters and updated boundaries into the model and repeat the simulation in S3. Then repeat S4 to collect and compare data to calculate the new deviation. Step S54: Iterate until all deviations meet the target. After meeting the target, verify three times consecutively (12-24 hours apart) to ensure that each deviation is within the threshold and avoid parameter fluctuations. Finally, record the optimized slug parameter combination and store it in the platform database. The total number of iterations should be controlled within 5-8 times to ensure optimization efficiency. After each adjustment, record the parameter change magnitude and deviation change trend to form an optimization log.

[0041] The multi-stage slug dynamic adaptation optimization algorithm in this invention is the core calculation logic for real-time adjustment of the injection parameters of multi-stage hot nitrogen foam slugs in shallow heavy oil reservoirs. Specifically, it integrates reservoir geological parameters, fluid property parameters and displacement process monitoring data to construct a parameter optimization calculation system. The implementation requires first retrieving basic data from the real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, including reservoir porosity of 20%-35%, permeability of 500-2500 mD, and heavy oil viscosity of 1000-5000 mPa·s. Then, initial parameters such as the number of slug stages of 3-5 and the initial value of injection pressure of 4.8-14.4 MPa are set. Subsequently, with reservoir fracture pressure and displacement efficiency as constraints, the slug injection pressure, rate, size, and alternation cycle are calculated and adjusted through 3-5 iterations. During the iteration, the injection pressure is adjusted first according to the pressure deviation (when it exceeds 0.5 MPa), and 0.05-0.1 MPa is adjusted for every 0.1 MPa deviation. Alternatively, the slug size (range 5%-10%) and cycle (range 10%-15%) are adjusted according to the recovery rate deviation (when it exceeds 3%). The algorithm outputs slug parameter combinations that adapt to the dynamic conditions of the reservoir, avoiding gas channeling or foam collapse problems caused by fixed parameters. It breaks away from the traditional empirical parameter setting mode, improves the adaptability of slugs to the reservoir through dynamic optimization, provides accurate parameter support for efficient displacement, and helps improve the recovery rate of shallow heavy oil reservoirs.

[0042] The pore-scale interface competition dynamic network model in this invention is a numerical model for simulating the migration and interfacial interactions of hot nitrogen foam within the pores of shallow heavy oil reservoirs. Specifically, it quantifies the interfacial competition process between foam and the oil and water phases by constructing a pore-throat topology. The implementation involves first converting the reservoir pore structure parameters (pore radius 5-50 μm, throat length 10-100 μm, connectivity probability 0.7-0.95) into a topology of 1000-2000 nodes (representing pores) and 1500-3000 edges (representing throats). Then, slug parameters (injection pressure 4.8-14.4 MPa, velocity 50-150 m³ / d) are input as boundary conditions. A time step of 1-6 h and 50-100 iterations are set. Subsequently, the foam distribution ratio (volume fraction accurate to three decimal places) and the oil phase flow velocity (1×10⁻⁻⁻⁶) are calculated within each time step. 6 -1×10⁻ 5 The model measures gas diffusion distances of 0.1-1 m and m / s, while simultaneously quantifying dynamic interfacial tension changes and gas channeling. Its purpose is to predict oil recovery rates of 20%-40%, gas retention rates of 30%-60%, and foam collapse rates of 10%-30% during the displacement process, generating simulated displacement efficiency data. It reveals the displacement mechanism at the microscopic pore scale, identifies potential problems in advance, provides a simulation basis for slug parameter adjustments, and overcomes the shortcomings of traditional macroscopic models that cannot accurately reflect the interfacial interactions within pores.

[0043] The real-time monitoring and intelligent control platform for thermal nitrogen foam displacement in this invention is an integrated system carrier that combines data acquisition, processing, simulation interaction, and command output. Specifically, it serves as the core hub connecting monitoring equipment, algorithms, and execution units. Its implementation requires first constructing a basic database to store reservoir geology (porosity, permeability) and fluid property (viscosity, density) data. Then, 5-10 pressure / temperature sensors (acquisition frequency 1-2 times / h, delay <10s) and a fluid component analyzer (accuracy 0.1%) are deployed to collect pressure response curves, temperature field distribution maps, and produced fluid component data in real time. Subsequently, the collected data is standardized (±10% outliers are removed) and compared with the pore-scale model simulation data at 1-day nodes to calculate the deviations in pressure (±0.5MPa), temperature (±2℃), and recovery rate (±3%). Simultaneously, it supports parameter iteration and optimization result storage for multi-stage slug algorithms, and finally sends control commands to the injection equipment. The platform aims to achieve a closed-loop linkage of monitoring, simulation, optimization, and control; to solve the problems of separation between monitoring and control and data interaction lag in existing technologies; and to ensure that the displacement process is always in the optimal state through real-time data feedback, providing a stable technical operating environment for multi-stage slug synergistic displacement of hot nitrogen foam in shallow heavy oil reservoirs.

[0044] like Figure 2 As shown, the shallow heavy oil reservoir thermal nitrogen foam multi-stage slug synergistic displacement system includes: 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.

[0045] A method and system for multi-stage slug-based synergistic displacement of hot nitrogen foam in shallow heavy oil reservoirs is proposed. This method achieves dynamic matching capability between slug parameters and reservoir characteristics through the synergistic application of a multi-stage slug dynamic adaptation optimization algorithm and a dynamic network model of interface competition at the pore scale. This effectively overcomes the shortcomings of existing technologies, such as reliance on experience for slug parameter optimization and poor adaptability. The algorithm can adjust the injection pressure, rate, size, and alternation cycle of the slugs in real time according to changes in reservoir pore structure and fluid properties during displacement. The model can accurately simulate the migration of hot nitrogen foam in pores, changes in interfacial tension, and gas channeling, avoiding gas channeling and premature foam collapse caused by fixed parameters. This significantly improves the adaptability between the slugs and the reservoir, laying the foundation for efficient displacement.

[0046] Based on a real-time monitoring and intelligent control platform for thermal nitrogen foam displacement, this method and system overcome the shortcomings of existing technologies, such as the lack of an integrated platform and the inability to dynamically adjust based on feedback. The platform integrates reservoir parameter monitoring, data processing, and command output functions. By collecting real-time data on pressure, temperature, and produced fluid composition and comparing it with model simulation results, it promptly corrects simulation deviations and avoids control lag. Simultaneously, the platform supports continuous iterative optimization of algorithms and models, ensuring that all parameters remain in optimal condition during the displacement process. This breaks through the limitations of traditional separation of monitoring and control, enabling dynamic management and control of the displacement process.

[0047] The coordinated operation of each unit within the system and the step-by-step optimization process further enhance the technological advantages. The units, including hot nitrogen foam preparation and injection, reservoir monitoring, algorithm calculation, and data management, form a closed loop, seamlessly connecting parameter optimization with on-site execution. The iterative steps involving multiple simulations and comparisons with actual data ensure the accuracy and stability of the displacement process. This integrated design of "algorithm-model-platform-system" comprehensively overcomes the shortcomings of background technologies, ultimately improving the recovery rate and development efficiency of shallow heavy oil reservoirs and meeting the needs of efficient development of complex reservoirs.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

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 a 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.

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 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.

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 pore-scale interface competition dynamic network model uses the following expression to calculate the foam interface tension change: ,in, For dynamic interface tension, For the initial interface 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.

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 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.

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 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.

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, 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.

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 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.

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 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.

9. 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.

10. A multi-stage slug synergistic displacement system for shallow heavy oil reservoirs using thermal nitrogen foam, characterized in that: include: 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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