Optimized scheduling method for plunger process control and ground cooperation based on RTU integration

Abstract

The invention discloses a plunger process control and ground collaborative optimal scheduling method based on RTU integration. The method comprises the following steps of multi-source data real-time acquisition and transmission, edge calculation model construction and optimization, collaborative scheduling instruction generation and issuing, and cloud strategy iteration and monitoring. Through the wellhead transmitting instrument and the integrated RTU technology, real-time and accurate acquisition and transmission of data of the plunger process control system and the ground system are achieved, a collaborative optimization scheduling strategy is adopted, production tasks are reasonably distributed and scheduled, the collaboration among the systems is enhanced, and in addition, by dynamically adjusting production parameters and the equipment operation state, the production efficiency is improved. According to the method and the system, global optimal scheduling is realized, the operation state of the system can be monitored and analyzed in real time by combining a visual interface, and the optimal scheduling result is displayed in a visual mode, so that intelligent and automatic optimal scheduling is realized, the requirement of manual intervention is reduced, and the maintenance cost of the gas well is reduced.

Description

Technical Field

[0001] This invention relates to the field of oil and gas production engineering and automation control technology in the petroleum industry, and in particular to an optimized scheduling method for plunger process control and ground coordination based on RTU integration. Background Technology

[0002] With the rapid popularization of information technology in oil and gas field construction, the development of information-based oil and gas field projects is also gradually improving. Various oil and gas field companies will accelerate the digital transformation and intelligent development of oil and gas fields, creating intelligent oil and gas fields characterized by comprehensive perception, automatic control, intelligent prediction, and continuous optimization.

[0003] In oil extraction, the plunger process has been widely used in drainage and gas production operations of oil-water gas reservoirs and shale gas due to its high efficiency, economy, and environmental friendliness. However, with the continuous increase in the number of gas wells, the management and control of the plunger process has become increasingly complex. Traditional plunger process management mainly relies on manual intervention to adjust operating parameters, and the information management software systems are primarily designed for data monitoring, lacking intelligent and automated optimization and scheduling methods. This results in limited process effectiveness, increased well maintenance costs, and difficulty in achieving collaborative management of surface facilities.

[0004] In current plunger process control systems, information exchange and scheduling between surface and downhole equipment often suffer from lag and inaccuracy, leading to low production efficiency and resource waste. Traditional control systems mostly employ distributed control, making it difficult to achieve globally optimized scheduling. Although some PLC- or DCS-based control systems have attempted to address these issues in recent years, they still have shortcomings in terms of integration, real-time performance, and flexibility. Therefore, this invention proposes an optimized scheduling method for plunger process control and surface collaboration based on RTU integration to solve the problems existing in the prior art. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to propose an optimized scheduling method for plunger process control and ground coordination based on RTU integration. By integrating RTU technology, real-time monitoring and control of the plunger process can be achieved. At the same time, by combining information from the ground system, intelligent optimized scheduling can be performed, which can improve the real-time performance, accuracy, and coordination of the scheduling system, thereby effectively improving the production efficiency of the plunger process and the operating efficiency of the ground system.

[0006] To achieve the objectives of this invention, the invention is implemented through the following technical solution: an optimized scheduling method for plunger process control and ground coordination based on RTU integration, comprising the following steps:

[0007] Step 1: Real-time acquisition of dynamic data of plunger gas lift wells through RTU edge computing devices deployed at the well site, linear interpolation compensation for data loss, and real-time alarms for configuration error data;

[0008] Step 2: Construct an edge computing model including a single-well dynamic model and a multi-well collaborative model, and run edge computing optimization on the RTU edge computing device, wherein:

[0009] The single-well dynamic model divides the plunger operation cycle into four stages: upward movement, continuous flow, downward movement, and pressure recovery. Based on Newton's second law and the gas law, the plunger motion trajectory is calculated, and the well inclination angle θ and friction correction coefficient K are introduced to correct the working conditions of highly deviated wells.

[0010] The multi-well collaborative model takes maximizing the total gas production of the platform and minimizing production fluctuations as its dual objectives. It integrates the objectives through a dynamic weighting factor α and uses node analysis to generate the well group inflow curve.

[0011] Step 3: Using the switching time offset as the decision variable, generate a staggered peak scheduling scheme, with constraints including:

[0012] Minimum shut-in time for a single well ≥ plunger drop time;

[0013] Pipeline pressure fluctuation threshold ≤ 0.5 MPa;

[0014] Based on the model optimization results, the instructions are converted into solenoid valve control signals through the Modbus TCP / IP protocol of the RTU edge computing device;

[0015] Step 4: Upload the optimized parameters of edge computing to the cloud platform to optimize long-term production strategies, and display the well group operation status and scheduling results in real time through a visual interface.

[0016] Further improvements are made in the following steps: In step one, during the data acquisition process, static data including well trajectory, casing inner diameter, and gas-liquid physical properties are stored in the SQLite database of the RTU edge computing device, and pressure jump data are processed using sliding window mean filtering with a filtering window length of 5 sampling periods.

[0017] Further improvements are made in the following aspects: In step two, in the single-well dynamic model, the gravity gradient of the liquid column and the gas flow rate of the wellhead choke valve are calculated during the upward stage, the formation gas production is updated through the mass conservation equation in each stage, and the shut-in time is dynamically adjusted based on the casing pressure recovery rate during the pressure recovery stage.

[0018] Further improvements are made in the following aspects: In step two, in the multi-well collaborative model, a pipeline topology with the separator as the root node is constructed, the pressure-flow relationship of the nodes is calculated through multiphase pipe flow theory, and the nonlinear constraints are handled by a piecewise linearization method, transforming the MINLP (mixed integer nonlinear programming) problem into a MILP (mixed integer linear programming) problem to be solved.

[0019] A further improvement lies in the following: In step two, the generation logic of the dynamic weighting factor α is as follows:

[0020] Initial value α = 0.7;

[0021] When a pressure fluctuation difference in the pipeline network is detected to be greater than 0.3 MPa, α is automatically lowered to 0.4.

[0022] When the liquid accumulation height in a single well exceeds 30% of the tubing length, an emergency drainage mode with α=0.9 is triggered.

[0023] A further improvement is that, in step three, the constraints also include:

[0024] The platform's total gas production is ≥ 100% of the historical average.

[0025] A further improvement is that in step three, when the communication of the RTU edge computing device is interrupted, it automatically switches to the preset timed shutdown mode, and when the solenoid valve response times out, it triggers the pressure safety threshold to shut down the well.

[0026] A further improvement is that, in step four, the cloud platform optimizes the long-term production system through a genetic algorithm and distributes it to the RTU edge computing device to update the terminal device. The RTU edge computing device uploads the optimized running data packet every 24 hours according to the settings.

[0027] A further improvement lies in the following: the specific steps for platform multi-well collaborative optimization using the multi-well collaborative model are as follows:

[0028] S1: Obtain real-time production data and basic static parameters of each well within the platform;

[0029] S2: Based on the single-well dynamic simulation model, construct a production capacity database for each well under different switching regimes;

[0030] S3: Establish a comprehensive fitness function with the objectives of maximizing the platform's total gas production and minimizing production fluctuations;

[0031] S4: Use an optimization algorithm to generate a collaborative scheduling scheme that includes the switching regime and start time offset of each well;

[0032] S5: Based on node analysis, calculate the total gas production and production fluctuation of the platform under each scheduling scheme, and evaluate its adaptability;

[0033] S6: Iterative optimization, outputting the optimal cooperative scheduling scheme;

[0034] S7: Distribute the above scheme to the RTU edge computing devices of each well for execution control.

[0035] The beneficial effects of this invention are as follows: This invention realizes real-time and accurate acquisition and transmission of data between the plunger process control system and the ground system through wellhead transmitter instruments and integrated RTU technology, eliminating the lag and inaccuracy of information interaction, thereby significantly improving scheduling efficiency.

[0036] Furthermore, the present invention adopts a collaborative optimization scheduling strategy, which rationally allocates and schedules production tasks based on the actual needs of the plunger process control system and the resource status of the ground system, thereby enhancing the synergy between systems and ensuring the smooth operation of the production process.

[0037] In addition, through in-depth data mining and processing, this invention can promptly discover and predict potential problems in the system, and achieve global optimized scheduling by dynamically adjusting production parameters and equipment operating status, which not only improves production efficiency but also maximizes resource utilization.

[0038] In addition, this invention combines advanced algorithms and a visual interface, which can monitor and analyze the system's operating status in real time and display the optimized scheduling results in a visual way. This not only improves the system's intelligence level, but also provides operators with a more convenient and intuitive management tool.

[0039] Based on the above advantages, the present invention achieves intelligent and automated optimized scheduling, reduces the need for manual intervention, and thus reduces the maintenance cost of gas wells. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the optimized scheduling method for plunger process control and ground coordination based on RTU integration according to the present invention.

[0041] Figure 2 This is a schematic diagram illustrating the production of a well after it is randomly arranged within a day, according to an embodiment of the present invention.

[0042] Figure 3 This is a schematic diagram of the random scheduling and arrangement of multiple wells in an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of the platform plunger gas lift particle swarm optimization concept in an embodiment of the present invention;

[0044] Figure 5 This is a schematic diagram of the platform optimization scheduling scheme in an embodiment of the present invention;

[0045] Figure 6This is a schematic diagram of a plunger gas lift well production system in an embodiment of the present invention;

[0046] Figure 7 This is a schematic diagram of the wellhead inflow curve of the i-th plunger well in the open state according to the present invention;

[0047] Figure 8 This is a schematic diagram of the inflow curves of well 1 and well 2 at node A in an embodiment of the present invention;

[0048] Figure 9 This is a schematic diagram of the inflow curve of node A in an embodiment of the present invention;

[0049] Figure 10 This is a schematic diagram of the separator inflow curve in an embodiment of the present invention;

[0050] Figure 11 This is a schematic diagram of the optimized platform plunger air lift system layout in the calculation case of this invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Highly deviated wells and horizontal wells have been widely used in gas reservoir development, but water production in gas wells can severely impact their productivity. Plunger gas lift is applicable to certain inclination angles and can be installed in a specific inclined section of deviated, highly deviated, or horizontal wells for fluid drainage. Currently, plunger gas lift theory is based on vertical well simulation experiments, which differ significantly from the wellbore trajectories of highly deviated and horizontal wells, and does not consider the influence of the well inclination angle. Therefore, it cannot be directly applied to the plunger gas lift process design for highly deviated and horizontal wells. Furthermore, empirical models cannot consider the impact of the follow-through process from the perspective of gas outflow rate.

[0053] To accurately describe the dynamic characteristics of plunger gas lift technology in highly deviated and horizontal wells and to discover the changing patterns of relevant parameters during the process, this paper utilizes methods such as the law of conservation of mass, considering the well inclination angle, the initial and final states of the plunger operation, and the stress conditions, to study the entire process of plunger gas lift motion in highly deviated and horizontal wells and establish its dynamic model. This lays a solid foundation for the analysis and optimization design of the lifting capacity of plunger gas lift, ultimately achieving the goal of efficient liquid discharge and gas production.

[0054] Example 1

[0055] See Figure 1This embodiment provides a method for constructing an edge computing model based on RTU integration for plunger process control and ground collaboration, including the following steps:

[0056] Step 1: Real-time acquisition and transmission of multi-source data

[0057] The wellhead sensors are connected via the RS485 interface of the RTU edge computing device deployed at the well site. Data including wellhead oil pressure, casing pressure, gas production, fluid production, temperature and surface pipeline pressure are collected. Linear interpolation compensation is performed on the tampered data and real-time alarms are triggered for the configuration error data.

[0058] LoRa wireless networking is used to aggregate data from multiple wells to the RTU edge computing device, and the data is transmitted to the edge computing layer in real time via Modbus TCP / IP protocol;

[0059] In this embodiment, static data is pre-stored and dynamic data is cleaned during the data acquisition process, wherein:

[0060] Static data pre-storage: Well trajectory, casing inner diameter, and gas-liquid physical property parameters are stored in the SQLite database of the RTU edge computing device;

[0061] Dynamic data cleaning: Sliding window mean filtering is used to process data with sudden pressure jumps, with a filter window length of 5 sampling periods.

[0062] Step 2: Building the Edge Computing Model

[0063] Single-well dynamic model construction:

[0064] The plunger acceleration is calculated based on Newton's second law, and the well inclination angle θ and friction correction coefficient K are introduced to correct the working conditions of highly deviated wells.

[0065] The plunger's operating cycle is divided into four stages: upward movement, continuation, downward movement, and pressure recovery.

[0066] Ascending phase: Calculate the gravity gradient of the liquid column and the gas flow rate of the wellhead choke valve;

[0067] The follow-through phase and all other phases: the formation gas production is updated using the mass conservation equation;

[0068] Pressure recovery phase: Dynamically adjust shut-in time based on casing pressure recovery rate;

[0069] Then the bottom hole flowing pressure is updated using the gas state equation and the static gas column pressure gradient;

[0070] Multi-well collaborative model construction:

[0071] The dual objectives are to maximize the platform's total gas production and minimize production fluctuations.

[0072] The inflow curves of the well group are generated using the node analysis method, taking into account the pressure interference between wells;

[0073] By fusing the objective function (fitness = α × total gas production / benchmark value + (1-α) × 1 / volatility coefficient) with the dynamic weighting factor α, the generation logic of the dynamic weighting factor α is as follows:

[0074] Initial value α = 0.7 (production priority);

[0075] When a pressure fluctuation difference in the pipeline network is detected to be greater than 0.3 MPa, α is automatically reduced to 0.4 (increasing the stability weight);

[0076] When the liquid accumulation height in a single well exceeds 30% of the tubing length, the emergency drainage mode with α=0.9 is triggered.

[0077] Construct a pipe network topology with the separator as the root node, and calculate the pressure-flow relationship of the nodes using multiphase pipe flow theory;

[0078] A piecewise linearization method is used to handle nonlinear constraints, transforming the MINLP (mixed-integer nonlinear programming) problem into a MILP (mixed-integer linear programming) problem for solution;

[0079] Step 3: Generation and Issuance of Coordinated Scheduling Instructions

[0080] Using the switching time offset as the decision variable, a staggered peak scheduling scheme is generated, with constraints including:

[0081] Minimum shut-in time for a single well ≥ plunger drop time;

[0082] Pipeline pressure fluctuation threshold ≤ 0.5 MPa;

[0083] The platform's total gas production is ≥ 100% of the historical average.

[0084] The RTU edge computing device converts commands into solenoid valve control signals via the Modbus TCP / IP protocol, with a response delay of <200ms. When the RTU edge computing device communication is interrupted, it automatically switches to the preset timed shutdown mode. When the solenoid valve response times out, it triggers the pressure safety threshold to shut down the well.

[0085] Step 4: Cloud-based policy iteration and monitoring

[0086] Upload the optimization parameters of edge computing to the cloud platform to optimize long-term production strategies;

[0087] In this embodiment, the cloud platform optimizes the long-term production system through a genetic algorithm and distributes it to the RTU edge computing device to update the edge terminal device. The RTU edge computing device uploads the optimized running data package (including the average and extreme values ​​of key indicators) every 24 hours according to the settings.

[0088] The operation status and scheduling results of the well group are displayed in real time through a visual interface;

[0089] The visual interface includes:

[0090] The platform displays a pressure map along the pipeline, showing the real-time pressure distribution of the pipeline network.

[0091] Optimize the scheduling diagram to show the staggered peak arrangement scheme.

[0092] The hardware configuration of the RTU edge computing device used in this embodiment is as follows:

[0093] RK3568 quad-core processor;

[0094] 2GB DDR4 memory;

[0095] Linux 4.19 system;

[0096] Also includes:

[0097] Expand the MINI-PCIE interface to support 4G / 5G backup communication;

[0098] The SD card slot supports local storage of up to 64GB of historical data;

[0099] The CAN bus interface is connected to the wellhead emergency shut-off device.

[0100] The specific steps for multi-well collaborative optimization of the platform in this embodiment are as follows:

[0101] S1: Obtain real-time production data and basic static parameters of each well within the platform;

[0102] S2: Based on the single-well dynamic simulation model, construct a production capacity database for each well under different switching regimes;

[0103] S3: Establish a comprehensive fitness function with the objectives of maximizing the platform's total gas production and minimizing production fluctuations;

[0104] S4: Employ particle swarm optimization algorithm to generate a collaborative scheduling scheme that includes the switching regime and start time offset of each well.

[0105] S5: Based on node analysis, calculate the platform's total gas production and production fluctuation under each scheduling scheme, and evaluate its adaptability, as detailed below:

[0106] For a specific well and under a specific system, after random arrangement within a day, the random range is the random time range before well opening within the periodic well shut-in time, such as... Figure 2 The system shown.

[0107] The optimized scheduling of multiple wells, based on the single-well random arrangement method, yields the following optimized arrangement results for multiple wells on the platform over a day: Figure 3 As shown.

[0108] The resulting optimized layout of multiple wells on the platform is one of the particles. Optimization is performed using the particle swarm optimization algorithm, and the optimization method is as follows: Figure 4 As shown.

[0109] The most crucial calculation is the fitness of each particle. The method for calculating particle fitness is as follows:

[0110] For any platform optimization scheduling scheme, every change in each well on the platform represents a different platform system, such as... Figure 5 As shown, the gas production and liquid production rates need to be calculated according to the following calculation process.

[0111] Suppose there is a plunger gas lift well production system consisting of n wells and a separator, such as Figure 6 As shown.

[0112] Considering the surface gathering and transportation pipelines, there is mutual interference between oil wells. The separator pressure is constant, serving as a boundary condition. When the surface gathering and transportation pipeline pressure fluctuates, it affects the gas (oil) well production (liquid) output. This relationship can be represented by the curves showing the relationship between wellhead pressure and gas (liquid) production under different plunger switching regimes, such as... Figure 7 Based on the principle of node analysis, this curve is the inflow curve of the plunger with the wellhead as the node.

[0113] Plot the inflow curves of each well with the wellhead as the node under different plunger switching regimes;

[0114] For any solution (Schedule1, Schedule2, ..., Scheduleen) (of a certain well), the inflow curve at the wellhead of each plunger gas lift well is obtained using interpolation. The fluid at node A comes from well 1 and well 2 (the plunger switching regime in well 2 is staggered with the regime in well 1 at different times, resulting in different outcomes). For well 1, a series of points are taken on the inflow curve, and based on multiphase pipe flow theory, the inflow curve of well 1 with respect to node A is calculated, yielding the inflow curve of well 1 with respect to node A. Similarly, the inflow curve of well 2 with respect to node A can be obtained, as follows... Figure 8 As shown.

[0115] When the pressure at node A is PAI, the gas (liquid) flow rates q1i and q2i from wells 1 and 2 into node A can be obtained from the graph. The gas flow rate at node A is qAi = q1i + q2i. Using this method, a series of (PA1, qA1), (PA2, qA2)...(PAm, qAm) can be obtained, thus yielding the inflow curve for node A. For example... Figure 9 As shown.

[0116] Based on the above method, the inflow curve of each node and the inflow curve with the separator as the node can be further obtained, such as... Figure 10 As shown. Based on the set separator pressure P s The total gas (liquid) volume Q can be obtained from the inflow curve of the separator. Then, the target component with small (stable) production fluctuations can be calculated. Finally, the multi-objective calculation result can be obtained, that is, the fitness of the solution (the results are different because the switching times of each plunger well in the well group are staggered).

[0117] S6: Iterative optimization, outputting the optimal cooperative scheduling scheme;

[0118] S7: Distribute the above scheme to the RTU edge computing devices of each well for execution control.

[0119] Example 2

[0120] This embodiment provides a method for simulating and calculating the production process of a single well, namely the single-well optimization process in Embodiment 1, which includes the following steps:

[0121] Step 1: Data Acquisition and Processing

[0122] Real-time data collection of wellhead oil pressure (P_t), casing pressure (P_c), gas production (Q_g), and fluid production (Q_l) is used. Based on principles such as the static gas column pressure gradient method, key parameters such as formation pressure (P_e), gas production index (J_g), and fluid production index (J_l) are updated and calculated using casing pressure data.

[0123] Step 2: Full-cycle dynamic simulation modeling

[0124] A refined physical model of a complete cycle of plunger gas lift was established, including the plunger upward stage, the continuous flow production stage, the plunger downward stage, and the pressure recovery stage. The model fully considers factors such as well inclination angle, fluid transfer in the annulus, multiphase pipe flow friction, and gas state equation.

[0125] Step 3: Calculation of well gas production under different switching regimes

[0126] By setting different switching regimes and calling dynamic simulation models to calculate their gas production, the well opening time (T_on) and well shut-in time (T_off) can be optimized by comparing the gas production.

[0127] Example 3

[0128] Based on Examples 1 and 2, this example provides a multi-well collaborative optimization method for a platform to solve the problem of inter-well pressure interference, maximize the platform's total gas production, and stabilize production. The method includes the following steps:

[0129] Step 1: System Initialization and Data Preparation

[0130] Input: Obtain the basic static parameters (well structure, pipeline topology, equipment parameters, etc.) and real-time production data (P_t, P_c, Q_g, Q_l) of all single wells within the platform;

[0131] Construct a single-well production capacity database: For each well, call its single-well optimization model to calculate its expected gas production and liquid production under various switching regimes (T_on, T_off), forming a "regulation-production capacity" query database for that well;

[0132] Step 2: Define the platform-level multi-objective optimization function

[0133] Objective 1 (F1): Maximize the total gas production (Q_total) of all wells on the platform within the scheduling cycle (T_cycle);

[0134] Objective 2 (F2): Minimize the stability of the platform's total gas production, which is quantified by calculating the standard deviation or mean absolute difference between the production output and the average production output in each time period;

[0135] The comprehensive fitness function (F) transforms a multi-objective problem into a single-objective optimization problem, employing a weighted summation method.

[0136] F=α*(Q_total / Q_max)+(1-α)*(1 / Stability)

[0137] Where α is a configurable weighting coefficient (0 < α < 1), used to balance output and stability;

[0138] Step 3: Cooperative scheduling scheme encoding (particle representation)

[0139] A scheduling scheme (particle) consists of the system combination of all wells on the platform, and the position vector of each particle is represented as: X = [T_on1, T_off1, Δt1, T_on2, T_off2, Δt2, ..., T_onN, T_offN, ΔtN]

[0140] Where T_oni and T_offi are the switching times of the i-th well, and Δti is the start time offset of the well's regime execution. Introducing Δti is the key to achieving staggered production and reducing inter-well interference.

[0141] Step 4: Fitness Assessment Based on Node Analysis (Core)

[0142] For each particle X (i.e., a candidate scheduling scheme), its fitness value F is calculated according to the following procedure:

[0143] a. Determine the wellhead inflow curve

[0144] Based on the system (T_oni, T_offi) of each well, the relationship curve between wellhead pressure and gas production (IPR curve) is obtained by interpolation from the database in step one.

[0145] b. Perform node analysis from the wellhead to the separator.

[0146] The production pipeline network is viewed as a node network consisting of wellheads, manifolds, separators, etc.

[0147] Starting from the wellhead, and using a multiphase pipe flow model, the pressure at the downstream node (separator pressure) is calculated back to the upstream wellhead.

[0148] Based on the IPR curve at the wellhead, calculate the actual gas production of the well under this back pressure;

[0149] At nodes (such as manifolds), the flow and pressure from multiple branches are superimposed and coupled for calculation.

[0150] c. Calculate total output and volatility

[0151] By analyzing nodes, the total gas production of the platform at each moment during the entire scheduling cycle is obtained, and then Q_total and Stability are calculated.

[0152] d. Obtain fitness

[0153] Substitute Q_total and Stability into the comprehensive fitness function in step two to obtain the fitness value F of particle X;

[0154] Step 5: Iterative optimization solution

[0155] A set of scheduling schemes (particle swarm optimization) is initialized using the particle swarm optimization algorithm (PSO).

[0156] Perform fitness assessment in step four for each particle;

[0157] Based on the particle's fitness and historical best position, update the particle's velocity and position to generate a new scheduling scheme;

[0158] Repeat the iterations until the termination condition is met (the maximum number of iterations is reached or the fitness converges);

[0159] Step Six: Output and Execution of the Optimal Solution

[0160] The optimal particle is decoded into specific scheduling instructions, including the switching time regime (T_oni, T_offi) and execution start time (Δti) for each well;

[0161] The RTU edge computing device distributes instructions to each well, controlling it to operate in staggered shifts according to an optimized schedule;

[0162] Step 7: Closed-loop feedback optimization

[0163] The system runs continuously and automatically re-triggers the entire optimization process periodically or based on changes in operating conditions (such as significant fluctuations in pressure or output), achieving dynamic closed-loop optimization.

[0164] Calculation examples are as follows

[0165] (I) Basic Data

[0166] Taking the XXX platform as an example, the platform has 4 plunger gas lift wells, and the specific parameters are shown in Table 1 below.

[0167] Table 1 Basic Data of Four Wells on the XXX Platform

[0168]

[0169]

[0170] (II) Gas production of different systems in each well

[0171] Based on the single-well system optimization model, different systems were optimized for each well, and the optimization results are shown in Table 2 below. As can be seen from the table, even for single-well optimization, there is still some room for improvement for each well. From the platform optimization perspective, the main approach is to interpolate the data to obtain the production output under different switching systems, and then optimize the well layout and scheduling to minimize back pressure and maximize the overall gas production of the oil wells and the platform.

[0172] Table 2 Simulated Production of Four Wells on XXX Platform under Different Systems

[0173]

[0174]

[0175] (III) Overall Platform Optimization

[0176] Based on the established platform collaborative optimization method, simulation calculations were performed to optimize the scheduling. The optimization results are as follows: Figure 11 The figures show the start time, well opening time, and well shut-in time, respectively. It can be seen that through scheduling, the production of a single well can be maximized, while the production of wells is staggered to ensure uniform gas production of the system, effectively reducing back pressure, increasing production, and achieving the optimization goal.

[0177] First, before optimization, the production rate of each well and the total production rate of the platform were calculated. Then, through single-well simulation verification, the production rate of each well (Table 3) and the total production rate of the platform were calculated. Finally, after coordination optimization, the production rate of each well and the total production rate were calculated, as shown in Table 3. It can be seen that the total production rate of the platform increased after optimization, from 6.086×10⁴ m³ / d to 6.231×10⁴ m³ / d, indicating that the optimization was effective and feasible.

[0178] Table 3 Comparison before and after optimization

[0179]

[0180] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimized scheduling method for plunger process control and ground coordination based on RTU integration, characterized in that, Includes the following steps: Step 1: Real-time acquisition of dynamic data of plunger gas lift wells through RTU edge computing devices deployed at the well site, linear interpolation compensation for data loss, and real-time alarms for configuration error data; Step 2: Construct an edge computing model including a single-well dynamic model and a multi-well collaborative model, and run edge computing optimization on the RTU edge computing device, wherein: The single-well dynamic model divides the plunger operation cycle into four stages: upward movement, continuous flow, downward movement, and pressure recovery. Based on Newton's second law and the gas law, the plunger motion trajectory is calculated, and the well inclination angle θ and friction correction coefficient K are introduced to correct the working conditions of highly deviated wells. The multi-well collaborative model takes maximizing the total gas production of the platform and minimizing production fluctuations as its dual objectives. It integrates the objectives through a dynamic weighting factor α and uses node analysis to generate the well group inflow curve. Step 3: Using the switching time offset as the decision variable, generate a staggered peak scheduling scheme, with constraints including: Minimum shut-in time for a single well ≥ plunger drop time; Pipeline pressure fluctuation threshold ≤ 0.5 MPa; Based on the model optimization results, the instructions are converted into solenoid valve control signals through the Modbus TCP / IP protocol of the RTU edge computing device; Step 4: Upload the optimized parameters of edge computing to the cloud platform to optimize long-term production strategies, and display the well group operation status and scheduling results in real time through a visual interface.

2. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step one, during the data acquisition process, static data including well trajectory, casing inner diameter, and gas-liquid physical parameters are stored in the SQLite database of the RTU edge computing device, and pressure jump data are processed using sliding window mean filtering with a filtering window length of 5 sampling periods.

3. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step two, in the single-well dynamic model, the gravity gradient of the liquid column and the gas flow rate of the wellhead choke valve are calculated during the upward stage. The formation gas production is updated through the mass conservation equation in each stage. During the pressure recovery stage, the shut-in time is dynamically adjusted based on the casing pressure recovery rate.

4. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step two, the multi-well collaborative model constructs a pipeline topology with the separator as the root node, calculates the pressure-flow relationship of the nodes through multiphase pipe flow theory, and uses a piecewise linearization method to handle nonlinear constraints, transforming the mixed integer nonlinear programming problem into a mixed integer linear programming problem for solution.

5. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step two, the logic for generating the dynamic weighting factor α is as follows: Initial value α = 0.7; When a pressure fluctuation difference in the pipeline network is detected to be greater than 0.3 MPa, α is automatically lowered to 0.

4. When the liquid accumulation height in a single well exceeds 30% of the tubing length, an emergency drainage mode with α=0.9 is triggered.

6. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step three, the constraints also include: The platform's total gas production is ≥ 100% of the historical average.

7. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step three, when the communication of the RTU edge computing device is interrupted, it automatically switches to the preset timed shutdown mode. When the solenoid valve response times out, it triggers the pressure safety threshold to shut down the well.

8. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: In step four, the cloud platform optimizes the long-term production system through a genetic algorithm and distributes it to the RTU edge computing device to update the terminal device. The RTU edge computing device uploads the optimized running data packet once every 24 hours according to the settings.

9. The optimized scheduling method for plunger process control and ground coordination based on RTU integration according to claim 1, characterized in that: The specific steps for platform multi-well collaborative optimization using the multi-well collaborative model are as follows: S1: Obtain real-time production data and basic static parameters of each well within the platform; S2: Based on the single-well dynamic simulation model, construct a production capacity database for each well under different switching regimes; S3: Establish a comprehensive fitness function with the objectives of maximizing the platform's total gas production and minimizing production fluctuations; S4: Use an optimization algorithm to generate a collaborative scheduling scheme that includes the switching regime and start time offset of each well; S5: Based on node analysis, calculate the total gas production and production fluctuation of the platform under each scheduling scheme, and evaluate its adaptability; S6: Iterative optimization, outputting the optimal cooperative scheduling scheme; S7: Distribute the above scheme to the RTU edge computing devices of each well for execution control.

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