A platform cyclic gas lift intelligent optimization and edge control collaborative method

Abstract

The application discloses a platform circulating gas lifting intelligent optimization and edge control coordination method, which comprises the following steps: step one, single-well gas lifting dynamic curve drawing; step two, multi-target optimization model establishment; step three, gas injection parameter optimization and dynamic adjustment; and step four, circulating gas lifting intelligent control. Through the construction of an integrated technical system of "single-well dynamic modeling-multi-well coordinated optimization-edge intelligent control", the global optimal distribution of the gas injection amount is realized through a two-stage optimization framework and a particle swarm algorithm, the coupling interference problem existing in the multi-well platform is solved, real-time response is realized through edge control, and the production reduction and shutdown caused by liquid accumulation are avoided. The application can realize the accurate control of the circulating gas lifting process and the maximum yield target, and provides a reliable technical path for intelligent drainage gas production of shale gas fields.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, and in particular to a collaborative method for intelligent optimization and edge control of platform circulating gas lift. Background Technology

[0002] In the mid-to-late stages of gas well development, fluid accumulation in gas wells is a key issue restricting production efficiency. Gas lift and drainage gas production technologies are the core means to solve fluid accumulation. Currently, related technologies have formed a certain system, but there are still significant limitations in multi-well collaborative optimization and real-time dynamic control.

[0003] Existing technologies have achieved breakthroughs in single-well parameter design, process selection, or single-point control in the fields of gas lift and drainage gas production, but none of them have solved the problem of multi-well platform circulating gas lift. At the same time, multi-well coupling interference has not been effectively handled, and there is a lack of platform-level gas injection volume collaborative optimization mechanism. The influence of formation gas production on gas injection volume has not been included in the calculation model, resulting in insufficient gas injection volume accuracy. The lack of a closed-loop control of "real-time data acquisition - edge-side optimization - command execution" makes it difficult to cope with dynamic changes in production parameters. Therefore, this invention proposes a platform circulating gas lift intelligent optimization and edge control collaborative method to solve the problems existing in the prior art. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to propose a platform circulating gas lift intelligent optimization and edge control collaborative method. This method involves plotting a gas lift dynamic curve that considers formation gas production, establishing a multi-well collaborative optimization model, and constructing a closed-loop control system using edge computing technology. This accurately determines the gas injection volume, eliminates multi-well coupling interference, and achieves intelligent optimization and real-time control of the platform circulating gas lift, thereby improving gas well production efficiency and gas field development benefits.

[0005] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a platform-based circulating airlift intelligent optimization and edge control collaborative method, comprising the following steps:

[0006] Step 1: Plot the dynamic curve of gas lift in a single well. Calculate the formation pressure and production parameters, then fit the production index and gas production index, and consider the formation gas production. Plot the dynamic curve of gas lift with the injection volume as the abscissa and the production volume and gas production volume as the ordinate.

[0007] Step 2: Establish a multi-objective optimization model. With the goal of maximizing the total gas production of the platform, and the upper and lower limits of gas injection volume of a single well and the total gas injection volume of the platform as constraints, an optimization model is constructed, which is expressed by the following formula.

[0008]

[0009] In the formula q gi The gas distribution rate for each well, i.e., the gas injection rate, qi (q gi ) represents the injection rate q for each well. gi The corresponding gas production volume is below, excluding the gas injection volume, Q. SumMax This represents the maximum total gas production of the platform, with units of 10. 4 m 3 / d, Q gTotal q represents the total gas injection volume of the platform. gimin Let q be the minimum gas injection rate for the i-th well. gimax This represents the maximum gas injection rate of the i-th well, with units of 10. 3 m 3 / d;p s For separator pressure, P s The set separator pressure, all in MPa; Dest Fitness For target fitness;

[0010] Step 3: Optimization and dynamic adjustment of gas injection parameters. Solve the model in Step 2. Take the gas distribution scheme of the platform's multi-well as one of the particles, and use the particle swarm optimization algorithm to calculate the fitness of each particle for optimization and dynamic adjustment.

[0011] Step 4: Intelligent control of circulating gas lift. Build an intelligent control system for circulating gas lift, collect process and production data, and then, based on the particle swarm optimization algorithm, solve the multi-objective optimization model in real time with the goal of maximizing gas production to obtain the optimal gas injection rate. Subsequently, the optimized parameters are sent to the intelligent controller through the production network to adjust the opening of the gas lift valve and provide real-time feedback of the execution results to complete closed-loop control.

[0012] Further improvements are as follows: In step one, the formation pressure calculation is specifically based on real-time casing pressure data, using the static gas column gradient method to calculate the bottom hole flowing pressure, and then estimating the formation pressure. The calculation formula is: Formation pressure = Bottom hole flowing pressure + 10MPa, where 10MPa is the production pressure difference. The production volume calculation is specifically based on real-time wellhead oil pressure, gas production, and the obtained bottom hole flowing pressure, using the multiphase pipe flow calculation formula. The production index and gas production index are specifically calculated by fitting the two points (X, 0) and (Y, Z or M) to obtain the production index and gas production index, where X is the formation pressure, Y is the bottom hole flowing pressure, Z is the production volume, and M is the gas production volume.

[0013] A further improvement lies in the following: the plotting of the airlift dynamic curve in step one is specifically as follows:

[0014] S1. Based on the gas well production index and gas production index, firstly calculate the maximum production volume using the liquid IPR method, and divide it into n equal parts to form a set of decreasing arithmetic series of production volumes Q1, ..., Qi, ..., Qn, where Q1 = Qmax and Qn = Qmax / n; based on the series of production volumes, calculate a series of corresponding bottom hole flowing pressures, and then calculate a series of corresponding gas production volumes Qg1, ..., Qgi, ..., Qgn using the bottom hole flowing pressure and gas IPR method;

[0015] S2. Based on a series of liquid production rates and corresponding gas production rates and corresponding bottom hole pressures, calculate from the bottom of the well to the injection point using multiphase flow to obtain a series of pressure values ​​at the injection point.

[0016] S3. Based on the given gas injection volume Qg, and considering the gas and liquid produced in S2, calculate the flowing pressure value from the gas injection point to the wellhead, i.e. the wellhead oil pressure, and obtain a series of wellhead oil pressures. According to the node analysis principle, calculate the liquid production and gas production corresponding to the wellhead oil pressure Pwh, and then obtain the liquid production and gas production corresponding to the gas injection volume Qg.

[0017] S4. Similarly, a series of liquid production and gas production rates corresponding to different gas injection rates can be obtained. With the gas injection rate as the horizontal axis and the liquid production rate and gas production rate as the vertical axis, the gas lift dynamic curve can be obtained.

[0018] The further improvement lies in the following: the fitness calculation in step three is specifically as follows:

[0019] S1. First, draw the inflow curve of each well with the wellhead as the node under different gas injection rates;

[0020] S2. For any solution The inflow curve at the wellhead of each gas lift well is obtained using interpolation.

[0021] S3. Take a node A on the inflow curve, when the pressure at node A is P. Ai The liquid flow rate q at node A of the gas lift well can be obtained from the inflow curve. ni (n = 1, 2, 3, ...), the liquid volume q at node A Ai =q 1i +q 2i +…+q ni From this, a series of (P) can be derived. A1 q A1 ), (P A2 q A2 )……(P Am q Am This yields the inflow curve for node A;

[0022] S4. Similarly, the inflow curve for each node and the inflow curve with the separator as the node can be further obtained, based on the set separator pressure P. s The total liquid volume Q and gas production can be obtained from the inflow curve of the separator, which is the solution under this platform regime.

[0023] S5. Calculate the solutions under all platform regimes, which are the fitness of the particles.

[0024] Further improvements are made in the following aspects: Step 3 optimization includes node analysis and well group optimization calculation. Node analysis specifically involves setting the separator as a key node, establishing a multiphase pipe flow model from the wellhead to the separator, combining the IPR curves of each well with the pipeline equations, and solving the coupling relationship between pressure and production. Well group optimization calculation specifically involves importing the single-well model, automatically running the particle swarm optimization algorithm, and outputting the optimized gas injection rate for each well.

[0025] The dynamic adjustment mechanism determines that when the casing pressure change exceeds a set threshold, it indicates a liquid accumulation state or a state after partial liquid discharge, meaning the well state has changed dynamically. In this case, the formation pressure and production / gas production index are recalculated, triggering the particle swarm optimization algorithm to update the gas distribution scheme.

[0026] Further improvements are made in the following: The circulating gas lift intelligent control system in step four includes a data acquisition and transmission module, an edge-side optimization calculation module, an instruction execution and feedback module, and a cloud monitoring module. The data acquisition and transmission module collects process parameters and production data based on the interconnection between the RTU edge computing terminal and the well site PLC system and intelligent control valves. The edge-side optimization calculation module constructs a multi-objective optimization model based on the PSO algorithm through the RTU edge computing terminal, with the goal of maximizing gas production, and solves for the optimal gas injection rate in real time. The instruction execution and feedback module is used to send the optimization parameters to the intelligent controller through the production network, adjust the opening of the gas lift valve, and feed back the execution results to the edge terminal in real time to form a closed-loop control. The cloud monitoring module is used to synchronously upload all data to the POC platform to realize remote monitoring and analysis.

[0027] A further improvement lies in the following: the specific control process in step four includes:

[0028] S1, RTU edge control terminal performs real-time data acquisition of oil, casing pressure, pressure and temperature before throttling of air nozzle, pressure and temperature after throttling of air nozzle, instantaneous gas volume and instantaneous liquid volume.

[0029] S2. A large amount of real-time data is processed by the circulating gas lift optimization algorithm module built into the RTU edge control terminal to solve for the optimal gas injection rate;

[0030] S3. When the gas injection volume is outside the pre-set threshold, record the current data for analysis by experts and technicians, and provide example data for subsequent algorithm optimization. When the gas injection volume is within the threshold, send it to the POC platform for confirmation or select automatic execution (after testing for a period of time and deeming it reliable).

[0031] S4. In non-automatic execution mode, after the POC confirms the optimized gas injection parameters and confirms that they can be sent, the RTU edge computing terminal sends the data to the on-site intelligent control valve for gas injection optimization.

[0032] S5. Synchronously upload all data to the POC platform to achieve remote monitoring and analysis.

[0033] The beneficial effects of this invention are as follows: This invention constructs an integrated technical system of "single-well dynamic modeling - multi-well collaborative optimization - edge intelligent control", and achieves the global optimal allocation of gas injection volume through a two-stage optimization framework and particle swarm algorithm, solving the coupling interference problem existing in multi-well platforms. Real-time response is achieved through edge control, avoiding production reduction and shutdown caused by liquid accumulation. This invention can achieve precise control and production maximization of the circulating gas lift process, providing a reliable technical path for intelligent drainage and gas production in shale gas fields. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention.

[0035] Figure 2 This is a flowchart illustrating the plotting of the airlift dynamic curve of this invention.

[0036] Figure 3 This is a flowchart of the optimization process for the circulating airlift particle swarm in the platform of this invention.

[0037] Figure 4 This is a schematic diagram of the platform optimization scheduling scheme of the present invention.

[0038] Figure 5 This is a diagram of the gas lift well production system architecture of the present invention.

[0039] Figure 6 This is the inflow curve diagram of the present invention.

[0040] Figure 7 The diagram shows the optimized gas distribution results for this invention.

[0041] Figure 8 This is a data transmission flow diagram of the system of the present invention.

[0042] Figure 9 This is the control flowchart of the system of the present invention. Detailed Implementation

[0043] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0044] according to Figures 1-9 As shown, this embodiment provides a collaborative method for intelligent optimization and edge control of platform circulating airlift, including the following steps:

[0045] Step 1: Plotting the dynamic curve of gas lift in a single well:

[0046] Calculate formation pressure and production parameters, then fit the production index and gas production index, and consider formation gas production. Plot the gas lift dynamic curve with the injection volume as the abscissa and the production volume and gas production volume as the ordinate.

[0047] Formation pressure is calculated by using real-time casing pressure data and the static gas column gradient method to calculate the bottom hole flowing pressure, and then estimating the formation pressure. The calculation formula is: Formation pressure = Bottom hole flowing pressure + 10MPa, where 10MPa is the production pressure difference.

[0048] The production rate is specifically calculated based on the real-time wellhead oil pressure, gas production, and the obtained bottom hole flowing pressure, using the multiphase pipe flow calculation formula.

[0049] The production index and gas production index are specifically calculated by fitting two points (X, 0) and (Y, Z or M) to obtain the production index and gas production index, where X is the formation pressure, Y is the bottom hole flowing pressure, Z is the production rate and M is the gas production rate.

[0050] The process for drawing the air lift dynamic curve is as shown in the attached instruction manual. Figure 2 As shown, specifically:

[0051] S1. Based on the gas well production index and gas production index, firstly calculate the maximum production volume using the liquid IPR method, and divide it into n equal parts to form a set of decreasing arithmetic series of production volumes Q1, ..., Qi, ..., Qn, where Q1 = Qmax and Qn = Qmax / n; based on the series of production volumes, calculate a series of corresponding bottom hole flowing pressures, and then calculate a series of corresponding gas production volumes Qg1, ..., Qgi, ..., Qgn using the bottom hole flowing pressure and gas IPR method;

[0052] S2. Based on a series of liquid production rates and corresponding gas production rates and corresponding bottom hole pressures, calculate from the bottom of the well to the injection point using multiphase flow to obtain a series of pressure values ​​at the injection point.

[0053] S3. Based on the given gas injection volume Qg, and considering the gas and liquid produced in S2, calculate the flowing pressure value from the gas injection point to the wellhead, i.e. the wellhead oil pressure, and obtain a series of wellhead oil pressures. According to the node analysis principle, calculate the liquid production and gas production corresponding to the wellhead oil pressure Pwh, and then obtain the liquid production and gas production corresponding to the gas injection volume Qg.

[0054] S4. Similarly, a series of liquid production and gas production rates corresponding to different gas injection rates can be obtained. With the gas injection rate as the horizontal axis and the liquid production rate and gas production rate as the vertical axis, the gas lift dynamic curve can be obtained.

[0055] In actual production, the formation usually produces gas when gas lift is used to drain the liquid. Therefore, the gas lift injection rate needs to be optimized according to the actual formation gas production situation.

[0056] Step 2: Establishing a multi-objective optimization model:

[0057] Based on the analysis of the dynamic characteristic curve of gas lift, and considering the coupling interference effect of multiple wells on the platform, an intelligent gas distribution optimization model is constructed with the goal of maximizing gas production. Due to the influence of the dynamic balance of the formation-wellbore-pipeline system, each well has an optimal gas injection threshold, and exceeding this value will lead to a decrease in gas production. Based on this, a two-stage optimization framework is established: first, at the single-well level, the optimal gas injection threshold for each well is determined through IPR-VLP coupling analysis; second, at the platform level, each well is connected to the same separator through gathering and transportation pipelines. Changes in the gas injection or production of a certain well will cause fluctuations in the separator pressure Ps, which in turn will affect the wellhead oil pressure and bottomhole flowing pressure of other wells.

[0058] Using the nodal analysis principle, the separator is set as the key node, and a multiphase pipe flow model from the wellhead to the separator is established. By combining the inflow dynamic curve (IPR curve), outflow dynamic curve (VLP) of each well and the pipeline flow equation, the coupling relationship between pipeline pressure and the production of each well is solved.

[0059] When the total gas injection capacity of the platform is limited, increasing the gas injection capacity of a certain well may lead to insufficient gas injection pressure in other wells or exceed the total gas volume limit. Particle swarm optimization algorithm is introduced, with the gas injection capacity of each well as the particle dimension. With the goal of maximizing the total gas production of the platform, the optimal combination of gas injection capacity is searched through iteration, while satisfying the gas injection pressure limit of a single well. With the goal of maximizing the total gas production of the platform, the upper and lower limits of gas injection capacity of a single well and the total gas injection capacity of the platform are the constraints. The optimization model is constructed and expressed by the following formula.

[0060]

[0061] In the formula q gi The gas distribution rate for each well, i.e., the gas injection rate, q i (q gi ) represents the injection rate q for each well. gi The corresponding gas production volume is below, excluding the gas injection volume, Q. SumMax This represents the maximum total gas production of the platform, with units of 10. 4 m 3 / d, Q gTotal q represents the total gas injection volume of the platform. gimin Let q be the minimum gas injection rate for the i-th well.gimax This represents the maximum gas injection rate of the i-th well, with units of 10. 3 m 3 / d;p s For separator pressure, P s The set separator pressure, all in MPa; Dest Fitness The target fitness.

[0062] Step 3: Optimization and Dynamic Adjustment of Injection Parameters:

[0063] To solve the model in step two, the gas distribution scheme of the platform's multiple wells is used as one of the particles. The particle swarm optimization algorithm is employed to calculate the fitness of each particle for optimization and dynamic adjustment. The optimization process is shown in the attached manual. Figure 3 As shown;

[0064] The most crucial calculation is the fitness of each particle; the calculation method for particle fitness is as follows: For any platform and a given time period, the optimized scheduling scheme is shown in the appendix of the manual. Figure 4 As shown; the production conditions for each well in the scheme need to be calculated according to the following calculation process.

[0065] The corresponding gas and liquid production rates for each well under the injection rate, considering the mutual interference between gas lift wells, can be solved using the gas lift dynamic curve based on the principle of node analysis. For example, suppose there is a gas lift production system consisting of n wells and a separator, as shown in the attached manual. Figure 5 As shown.

[0066] Considering the surface gathering and transmission pipelines, there is mutual interference between wells. The separator pressure is constant, serving as a boundary condition. When the surface gathering and transmission pipeline pressure fluctuates, it affects the gas production and liquid production of the gas wells. This relationship can be represented by the curves showing the relationship between wellhead pressure and liquid production under different injection rates, as shown in the appendix to the instruction manual. Figure 6 Figure a shows the inflow curve of the i-th wellhead. According to the principle of node analysis, this curve is the inflow curve with the wellhead as the node.

[0067] Fitness calculation is specifically as follows:

[0068] S1. First, draw the inflow curve of each well with the wellhead as the node under different gas injection rates;

[0069] S2. For any solution The inflow curve at the wellhead of each gas lift well is obtained using interpolation.

[0070] The fluid at node A originates from wells 1 and 2. For well 1, a series of points are taken on the inflow curve. Based on multiphase pipe flow theory, the inflow curve of well 1 to node A is calculated. Similarly, the inflow curve of well 2 to node A can be obtained, as shown in the appendix to the instruction manual. Figure 6 As shown in Figure b, this is the inflow curve of well 1 and well 2 at node A;

[0071] S3. Take a node A on the inflow curve, when the pressure at node A is P. Ai The liquid flow rate q at node A of the gas lift well can be obtained from the inflow curve. ni (n = 1, 2, 3, ...), the liquid volume q at node A Ai =q 1i +q 2i +…+q ni From this, a series of (P) can be derived. A1 q A1 ), (P A2 q A2 )……(P Am q Am This yields the inflow curve for node A, as shown in the attached instruction manual. Figure 6 As shown in Figure c, this is the inflow curve for node A;

[0072] S4. Similarly, the inflow curve for each node and the inflow curve with the separator as the node can be further obtained, as shown in the attached diagram. Figure 6 As shown in Figure d, this is the separator inflow curve, based on the set separator pressure P. s The total liquid volume Q and gas production can be obtained from the inflow curve of the separator, which is the solution under this platform regime.

[0073] S5. Calculate the solutions under all platform regimes, which are the fitness of the particles.

[0074] The final product is as shown in the instruction manual. Figure 7 The optimized gas distribution results are shown.

[0075] The optimization process includes node analysis and well group optimization calculations:

[0076] Node analysis: The separator is set as the key node. A multiphase pipe flow model from the wellhead to the separator is established. The IPR curves of each well are combined with the pipeline equations to solve the coupling relationship between pressure and production.

[0077] Well group optimization calculation: After importing the single-well model, the particle swarm optimization algorithm is automatically run to output the optimized gas injection volume for each well.

[0078] The dynamic adjustment mechanism determines that when the casing pressure change exceeds a set threshold, it is in a state of liquid accumulation. The formation pressure and the liquid / gas production index are recalculated, and the particle swarm optimization algorithm is triggered to update the gas distribution scheme.

[0079] Step 4: Intelligent Control of Circulating Air Lift:

[0080] A remote support center (POC) for gas production technology has been established on-site, enabling dynamic tracking of the entire gas well production lifecycle, diagnosis of gas production process anomalies, and parameter optimization. To meet the intelligent control requirements of the shale gas platform's circulating gas lift process, the system design must consider the following core elements:

[0081] Firstly, at the centralized management level: unified control of gas lift parameters across multiple wells is achieved through intelligent controllers; gas lift efficiency is monitored in real time based on a high-precision sensor network (pressure, flow rate, valve position, etc.), and a closed-loop feedback mechanism is constructed.

[0082] Second, at the intelligent algorithm level: deploy an intelligent optimization model based on particle swarm optimization (PSO) to automatically generate optimization instructions for key parameters such as gas injection volume and timing, combined with real-time operating conditions.

[0083] A circulating gas lift intelligent control system is built to collect process and production data. Then, based on the particle swarm optimization algorithm, with the goal of maximizing gas production, a multi-objective optimization model is solved in real time to obtain the optimal gas injection rate. Subsequently, the optimization parameters are sent to the intelligent controller through the production network to adjust the opening of the gas lift valve. The execution results are fed back in real time to complete the closed-loop control.

[0084] The circulating air lift intelligent control system includes a data acquisition and transmission module, an edge-side optimization calculation module, an instruction execution and feedback module, and a cloud monitoring module.

[0085] The data acquisition and transmission module, based on the RTU edge computing terminal, interconnects with the well site PLC system and intelligent control valves via Modbus TCP / IP protocol to acquire process parameters (regulating valve opening, gas injection volume, etc.) and production data (instantaneous gas production, oil / casing pressure, etc.), using the existing production network transmission channel to ensure data compatibility and security.

[0086] The edge-side optimization calculation module constructs a multi-objective optimization model based on the PSO algorithm through the RTU edge computing terminal, with the goal of maximizing gas production, and solves the optimal gas injection rate in real time.

[0087] The instruction execution and feedback module is used to send optimization parameters to the intelligent controller through the production network, adjust the opening of the air lift valve, and feed the execution results back to the edge terminal in real time to form a closed-loop control.

[0088] The cloud monitoring module is used to synchronously upload all data to the POC platform to achieve remote monitoring and analysis.

[0089] The system's data transmission flow diagram is shown in the attached manual. Figure 8 As shown in Table 1, the data that the entire intelligent control system needs to collect and the implementation path are as follows.

[0090] Table 1 Data Acquisition Items and Control Equipment

[0091]

[0092] The system control flow is as shown in the attached manual. Figure 9 As shown, it specifically includes:

[0093] S1, RTU edge control terminal performs real-time data acquisition of oil, casing pressure, pressure and temperature before throttling of air nozzle, pressure and temperature after throttling of air nozzle, instantaneous gas volume and instantaneous liquid volume.

[0094] S2. A large amount of real-time data is processed by the circulating gas lift optimization algorithm module built into the RTU edge control terminal to solve for the optimal gas injection rate;

[0095] S3. When the gas injection volume is outside the pre-set threshold, record the current data for analysis by experts and technicians, and provide example data for subsequent algorithm optimization. When the gas injection volume is within the threshold, send it to the POC platform for confirmation.

[0096] S4. After the POC confirms the optimized gas injection parameters and confirms that they can be sent, the RTU edge computing terminal sends the data to the on-site intelligent control valve for gas injection optimization.

[0097] S5. Synchronously upload all data to the POC platform to achieve remote monitoring and analysis.

[0098] The RTU edge computing terminal consists of four parts: a power module, a communication module, a control module, and an algorithm module.

[0099] The main equipment list is as follows:

[0100] Power supply PS1: 120W, input 220V, output 24V; includes air circuit breaker QM1 and surge module SP1;

[0101] Communication module: SW1 4-port industrial Ethernet switch;

[0102] Control module and algorithm module: The integrated RTU edge computing control device IOT1 includes an RTU module and a 4TOPs computing power module.

[0103] The control principle is as follows:

[0104] The connection between the wellhead communication equipment and the RTU at the physical layer is an RS4J interface: the communication equipment data between multiple wells is received and sent through the application layer Modbus TCP protocol to achieve the purpose of real-time bidirectional data transmission between multiple wells.

[0105] The RTU's functionalities were studied. The RTU was selected based on the aarch64 kernel and the Linux operating system platform. Using the cross-compilation tool aarch64-linux-gnu-g++, an adaptation program was written to store the real-time data received from the RS485 in a database, providing raw data for edge computing algorithms. The libmodbus C library was called to write a Modbus TCP host context program to read data from the RTU slave's registers. Then, the sqlite3 C library was called to write the raw data to a .db database file. The edge computing algorithm program can read this database file, read the required data, perform corresponding calculations, and write the results back to the database. The RTU receives the results and sends instructions to the wellhead control equipment to execute actions.

[0106] This study utilizes domestically developed RTUs and edge computing capabilities to build a Linux-based algorithm environment, conducting research on multi-well group fusion control optimization algorithms, and realizing a multi-condition operation mode of local data acquisition, local algorithm optimization, and local equipment execution.

[0107] Example

[0108] This embodiment uses the AAA platform and the BBB platform to conduct intelligent optimization control experiments for circulating air lift, as detailed below:

[0109] I. Intelligent Optimization Solution for AAA Platform Circulating Air Lift and its On-site Implementation

[0110] (1) Basic Information on Gas Wells

[0111] The AAA platform has a total of 6 producing wells. To fully utilize the gas production potential of each well, a refined gas extraction process combining "pressurization + gas lift + foam drainage" is planned to be implemented. Before the implementation of this process, the predicted increase in gas production for each well is 0 to 5,000 cubic meters per MPa. The basic information of each well is as follows:

[0112] ① AAA-1 well

[0113] Well AAA-1 was put into production in November 2021. In June 2022, a 23 / 8" tubing was run under pressure (to a depth of 2855m). In February 2023, the bubble lift process was implemented. Initially, the daily gas production was 50,000 cubic meters and the daily water production was 42 cubic meters. Later, it was gradually reduced to 30,000 cubic meters of gas and 3 cubic meters of water per day. The gas lift frequency was 1 to 5 times per year (no gas lift since November 2024).

[0114] The well has a horizontal section length of 2000m, a capacity of EUR 120 million, and a cumulative production of EUR 45 million. The current downstream pressure is 2.2MPa, casing pressure is 3.6MPa, oil pressure is 2.4MPa, daily gas production is 29,000 cubic meters, and daily water production is 3 cubic meters. It uses bubble drainage production, and the PIPESIM calculation shows a downhole fluid level of 0m. Based on the predicted effects of booster wells on other platforms, a 1.0MPa reduction in transmission pressure would result in a maximum production increase of 5,000 to 6,000 cubic meters per well. However, based on the predicted concentrated booster effect in June 2023 for this well, no production increase is expected for this well.

[0115] ② AAA-2 well

[0116] Well AAA-2 was put into production in November 2021. In July 2022, a 23 / 8" tubing was run under pressure (to a depth of 3129m). In February 2023, the bubble lift process was implemented. In the early stage, the daily gas production was 57,000 cubic meters and the daily water production was 30 cubic meters. Later, it gradually decreased to 31,000 cubic meters of gas production and 2 cubic meters of water production per day. The gas lift frequency was 25 to 51 times per year (there were 2 gas lifts since November 2024).

[0117] The well has a horizontal section length of 1800m, a EUR 150 million reserve, and a cumulative production of EUR 58 million. The current downstream pressure is 2.2MPa, casing pressure is 4.2MPa, oil pressure is 2.4MPa, daily gas production is 30,000 cubic meters, and daily water production is 2 cubic meters. The well is currently in bubble drainage production. PIPESIM calculates a downhole fluid level of 50m. Based on the predicted effects of implemented booster wells, a 1.0MPa reduction in transmission pressure would increase production by 5,000-6,000 cubic meters per well. Based on the predicted concentrated booster effect in June 2023, a conservative production increase of 5,000 cubic meters per MPa per well is expected.

[0118] ③ AAA-4 well

[0119] Well AAA-4 was put into production in November 2021. In June 2022, a 23 / 8" tubing was run under pressure (to a depth of 2878m). In February 2023, the bubble lift process was implemented. In the early stage, the daily gas production was 74,000 cubic meters and the daily water production was 28 cubic meters. Later, it gradually decreased to 33,000 cubic meters of gas production and 2 cubic meters of water production per day. The gas lift frequency was 5 to 20 times per year (10 gas lifts since November 2024).

[0120] The well has a horizontal section length of 1810m, a production capacity of EUR 124 million, and a cumulative production of EUR 52 million. The current downstream pressure is 2.2MPa, casing pressure is 3.8MPa, and oil pressure is 2.3MPa. Daily gas production is 20,000 cubic meters, and daily water production is 2 cubic meters. The well is currently in bubble drainage production. PIPESIM calculates a downhole fluid level of 30m. Based on the predicted effects of implemented booster wells, a 1.0MPa reduction in transmission pressure would increase production by 5,000–6,000 cubic meters per well. Based on the predicted concentrated booster effect in June 2023, a conservative production increase of 1,500 cubic meters per MPa is expected per well.

[0121] ④ AAA-5 well

[0122] Well AAA-5 was put into production in June 2022. In August 2022, a 23 / 8" tubing was run under pressure (to a depth of 2922m). In April 2023, the bubble lift process was implemented. In the early stage, the daily gas production was 62,000 cubic meters and the daily water production was 11 cubic meters. Later, it gradually decreased to 34,000 cubic meters of gas production and 3 cubic meters of water production per day. The gas lift frequency was 5 to 11 times per year (there were 2 gas lifts since November 2024).

[0123] The well has a horizontal section length of 1600m, a production of EUR 0.89 billion, and a cumulative production of EUR 0.41 billion. The current downstream pressure is 2.2MPa, casing pressure is 4.3MPa, and oil pressure is 2.4MPa. Daily gas production is 30,000 cubic meters, and daily water production is 2 cubic meters. The well is currently in bubble drainage production. PIPESIM calculates a downhole fluid level of 60m. Based on the predicted effects of implemented booster wells, a 1.0MPa reduction in transmission pressure would increase production by 0.5-0.6 million cubic meters per well. Based on the predicted concentrated booster effect in June 2023, a conservative production increase of 0.06 million cubic meters per MPa is expected per well.

[0124] The platform was officially put into operation on March 9, 2025, using an integrated pressurized gas lift and foam drainage process. One month before the operation, the platform's total gas production had decreased to 128,000 cubic meters per day. One month after the operation, the production increase was significant, with the platform's total gas production rising to 153,000 cubic meters per day. The process implementation was as follows: (1) Pressurization: Four wells were connected, and the wellhead oil pressure decreased from 2.0 MPa to 1.1-1.3 MPa; (2) Gas lift: Wells AAA-1, 4, and 5 were assisted with gas injection of 10,000-12,000 cubic meters per day, respectively; Well AAA-2 was injected with 25,000 cubic meters per day after water flooding recovery. (3) Foam drainage: Well AAA-2 used a composite foam drainage process, with 50 kg injected each time and a dilution ratio of 1:6.

[0125] (2) Optimization scheme for circulating air lift

[0126] Based on the current production status of wells AAA-1, 2, 4, and 5, the estimated current formation pressures for each well are 19.4, 24.54, 19.87, and 19.36 MPa, respectively. The gas production indices for each well are 0.01, 0.0014, 0.0077, and 0.007, respectively, and the liquid production indices for each well are 0.3, 0.2, 0.1, and 0.2, respectively. The specific parameters for each well are shown in Table 2 below.

[0127] Table 2 Basic Parameters of Each Well on the Platform

[0128]

[0129] By plotting the gas lift dynamic curves of a single well, the optimal gas injection rates for wells AAA-1, 2, 4, and 5 were calculated to be 0, 3.50, 1.50, and 2.0 million cubic meters per day, respectively.

[0130] (3) Application effect

[0131] Based on the optimization simulation results, combined with the previous production status of each well and the current process operation conditions, from April 17th to 22nd, the gas injection volume of wells AAA-2 and AAA-4 will be increased to approximately 35,000 and 15,000 cubic meters per day, respectively.

[0132] Through optimization of gas injection volume, well AAA-2 resumed normal production, with the average daily gas production increasing from 5,000 cubic meters per day to 10,000 cubic meters per day. Well AAA-4 maintained an average daily gas production of approximately 26,000 cubic meters per day, but its daily water production increased significantly, reaching a maximum of 4 cubic meters per day. During the period of gas injection volume optimization, the total gas production of the AAA platform increased from 137,000 cubic meters per day to 148,000 cubic meters per day, representing an 8% increase in the platform's total gas production, demonstrating the effectiveness of gas lift parameter optimization.

[0133] II. BBB Platform Circulating Air Lift Intelligent Optimization Solution and On-site Implementation

[0134] (1) Basic Information on Gas Wells

[0135] The BBB platform has four producing wells and is the first deep shale gas platform to test the circulating gas lift technology. Before the circulating gas lift technology was put into operation, wells 2 and 3 relied on truck-mounted gas lift to maintain normal production. Since the circulating gas lift technology was put into operation on December 20, 2023, truck-mounted gas lift has not been used. In the early stage of the technology implementation, the average daily gas production of a single well was stable at 30,000 to 35,000 cubic meters. However, as the pressure and production further decreased, the total production of the platform had dropped to about 45,000 cubic meters per day by May 2025.

[0136] ①BBB-1 well

[0137] Well BBB-1 was put into production in April 2022. In February 2023, a 27 / 8" tubing was run under pressure (75.7m deep). In January 2024, the circulating gas lift process was implemented. Before the process was put into operation, the casing pressure was 2.08-2.20MPa, the oil pressure was 1.15-1.23MPa, the daily gas production was 25,100-29,200 cubic meters, and the daily water production was 3-5 cubic meters. The vehicle-mounted gas lift was hardly used. After the process was put into operation, stable production was maintained. By May 2025, the daily gas production had dropped to 14,000 cubic meters.

[0138] ②BBB-2 well

[0139] Well BBB-2 was put into production in April 2022. In February 2023, a 27 / 8" tubing was run under pressure (70.6m deep). In December 2023, the circulating gas lift process was implemented. Before the process was put into operation, the casing pressure was 2.09-2.35MPa, the oil pressure was 1.12-1.25MPa, the daily gas production was 24,800-29,000 cubic meters, the daily water production was 3-4 cubic meters, and the frequency of vehicle-mounted gas lift was 8.58 times / month. After the process was put into operation, stable production was maintained. By May 2025, the daily gas production had dropped to 12,000 cubic meters.

[0140] ③BBB-3 well

[0141] Well BBB-3 was put into production in April 2022. In February 2023, a 27 / 8" tubing was run under pressure (70.13m deep). In December 2023, the circulating gas lift process was implemented. Before the process was put into operation, the casing pressure was 2.33-2.76MPa, the oil pressure was 1.19-1.27MPa, the daily gas production was 11,100-49,000 cubic meters, the daily water production was 0-5 cubic meters, and the frequency of vehicle-mounted gas lift was 8.41 times / month. After the process was put into operation, stable production was maintained. As of May 2025, the daily gas production dropped to 6,000 cubic meters.

[0142] ④ BBB-4 well

[0143] Well BBB-4 was put into production in April 2022. In March 2023, a 27 / 8" tubing was run under pressure (74.77m deep). In January 2024, a circulating gas lift process was implemented. Before the process was put into operation, the casing pressure was 2.76-2.82MPa, the oil pressure was 1.18-1.27MPa, the daily gas production was 38,100-47,700 cubic meters, and the daily water production was 3-6 cubic meters. Truck-mounted gas lift was not used. After the process was put into operation, stable production was maintained. By May 2025, the daily gas production had dropped to 13,000 cubic meters.

[0144] (2) Optimization scheme for circulating air lift

[0145] Based on the existing facilities of the BBB platform (separator, compressor, intelligent control valve, etc.), an RTU edge computing terminal is added as the core control unit to build a closed-loop control system of "data acquisition-optimization calculation-instruction execution".

[0146] This experiment consists of four steps:

[0147] Phase 1 (1-2 weeks): Equipment installation and commissioning. The RTU edge computing terminal will be installed in the skid-mounted room of the BBB platform station. Network testing and joint commissioning with the gas lift intelligent control equipment will be carried out to ensure that the RTU edge computing terminal can successfully read the real-time production data and real-time gas injection parameters of the four wells on the BBB platform from the PLC system. At the same time, it will be ensured that the gas injection volume can be controlled by issuing gas injection volume commands using the RTU edge computing terminal.

[0148] Phase 2 (3-4 weeks): Artificial optimization test, based on PSO algorithm to optimize gas injection volume of single well, and verify the accuracy of model by manually adjusting gas injection volume.

[0149] Phase 3 (5-6 weeks): Automatic optimization test, activation of the circulating gas lift intelligent control system, dynamic adjustment of gas injection volume in 4 wells, and comparison of changes in total platform production before and after optimization.

[0150] Phase Four: Summary and Evaluation, Analysis and Summary of the Adaptability of Circulating Air Lift Intelligent Control Technology.

[0151] (3) Application effect

[0152] Through integrated design, the RTU edge computing terminal is integrated into a standard 4U vertical space and installed in the original cabinet. The system installation and connection are achieved by connecting the backup UPS circuit power supply and the production network switch.

[0153] The RTU edge computing terminal collects key real-time data such as oil jacket pressure, pressure before and after throttling, temperature, and air injection volume of intelligent control valves from the station control PLC system. The online real-time optimization results of the circulating air lift intelligent optimization algorithm can be sent to the intelligent control valves to achieve centralized and unified control of the station process.

[0154] During manual control, based on the optimization model calculations, the gas injection rates of wells BBB-1, 2, 3, and 4 were adjusted from 20,000, 18,000, 15,000, and 15,000 m³ / day to 4,000,900, 16,000, and 10,000 m³ / day, respectively. One day later, the platform's total gas production increased from 45,000 m³ / day to 59,000 m³ / day, showing a significant increase in production. However, well BBB-1 subsequently experienced wellbore fluid accumulation. After resuming production by increasing the gas injection rate of well BBB-1 to 20,000 m³ / day, a trial injection of 10,000 m³ / day was conducted, but wellbore fluid accumulation reappeared. Furthermore, due to the failure to remove the accumulated fluid before reducing the injection rate and the lack of timely optimization of holiday schedules, severe fluid accumulation led to water flooding and production shutdown. Production was only restored after increasing the gas injection rate of this well to 35,000 m³ / day. Through manual control of gas injection, it was determined that the gas injection volume of the BBB-1 well should not be lower than 20,000 cubic meters per day. Therefore, in order to ensure the normal production of the gas well, a safety threshold control was set, that is, a lower limit for the gas injection volume threshold was set. When the gas injection volume calculated by optimization is lower than the lower limit for the gas injection volume threshold, the set lower limit for the gas injection volume threshold is executed and an alarm is issued.

[0155] During the automatic control period, wells BBB-1, 2, 3, and 4 maintained stable production, the rate of decline in daily gas production decreased by 27%, and the daily gas production of the platform stabilized at over 50,000 cubic meters per day, achieving the production increase target.

[0156] III. Summary

[0157] The reliability of the circulating gas lift optimization model and intelligent control system was verified through field tests. Based on the actual parameters of the BBB and AAA platforms, a single-well and multi-well collaborative circulating gas lift optimization model was constructed. The optimal gas distribution scheme solved using the particle swarm optimization algorithm achieved significant results after field implementation: after optimizing the gas lift injection rate on both the AAA0 and BBB platforms, the total gas production of the platforms was improved.

[0158] The optimized gas injection rate closely matches the theoretical value of the gas lift dynamic curve (error <5%), which not only confirms the reliability of the optimized model but also verifies the feasibility of the circulating gas lift process control system based on intelligent algorithms in actual production. The experimental results fully demonstrate that by establishing an accurate multi-well coupling model and employing intelligent optimization algorithms, precise control and maximum production targets for the circulating gas lift process can be achieved, providing a reliable technical path for intelligent drainage and gas production in shale gas fields.

[0159] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative method for intelligent optimization and edge control of platform circulating airlift, characterized in that, Includes the following steps: Step 1: Plot the dynamic curve of gas lift in a single well. Calculate the formation pressure and production parameters, then fit the production index and gas production index, and consider the formation gas production. Plot the dynamic curve of gas lift with the injection volume as the abscissa and the production volume and gas production volume as the ordinate. Formation pressure calculation is specifically based on real-time casing pressure data. The bottom hole flowing pressure is calculated using the static gas column gradient method, and then the formation pressure is estimated. The calculation formula is: Formation pressure = Bottom hole flowing pressure + Production pressure differential. The production rate is calculated based on the real-time wellhead oil pressure, gas production, and bottom hole flowing pressure using a multiphase pipe flow calculation formula. The production index and gas production index are calculated by fitting the production rate and gas production based on the formation pressure and bottom hole flowing pressure. The specific steps for plotting the airlift dynamic curve are as follows: S1. Based on the liquid production index and gas production index of the gas well, firstly calculate the maximum liquid production using the liquid IPR method, and divide it into n equal parts to form a set of decreasing arithmetic series of liquid production Q1, ..., Qi, ..., Qn, where Q1 = Qmax and Qn = Qmax / n; based on the series of liquid production, calculate a series of corresponding bottom hole flowing pressures, and then calculate a series of corresponding gas production Qg1, ..., Qgi, ..., Qgn based on the bottom hole flowing pressures and the gas IPR method; S2. Based on a series of liquid production rates and corresponding gas production rates and corresponding bottom hole pressures, calculate from the bottom of the well to the injection point using multiphase flow to obtain a series of pressure values ​​at the injection point. S3. Based on the given gas injection volume Qg, and considering the gas and liquid produced in S2, calculate the flowing pressure value from the gas injection point to the wellhead, i.e. the wellhead oil pressure, and obtain a series of wellhead oil pressures. According to the node analysis principle, calculate the liquid production and gas production corresponding to the wellhead oil pressure Pwh, and then obtain the liquid production and gas production corresponding to the gas injection volume Qg. S4. Similarly, a series of liquid production and gas production under different gas injection conditions can be obtained. With the gas injection rate as the horizontal axis and the liquid production and gas production as the vertical axis, the gas lift dynamic curve can be obtained. Step 2: Establish a multi-objective optimization model. With the goal of maximizing the total gas production of the platform, and the upper and lower limits of gas injection volume of a single well and the total gas injection volume of the platform as constraints, an optimization model is constructed, which is expressed by the following formula. ； In the formula q gi The gas distribution volume for each well, i.e., the gas injection volume, is expressed in units of 10. 4 m 3 / d,qi(q gi ) represents the injection rate q for each well. gi The corresponding gas production is shown below, in units of 10. 4 m 3 / d, excluding gas injection volume, Q SumMax This represents the maximum total gas production of the platform, expressed in units of 10. 4 m 3 / d;Q gTotal This refers to the total gas injection volume of the platform, expressed in units of 10. 3 m 3 / d, q gimin The minimum gas injection rate for the i-th well, expressed in units of 10. 3 m 3 / d, q gimax This represents the maximum gas injection rate of the i-th well, in units of 10. 3 m 3 / d;p s The pressure of the separator is expressed in MPa (P). s The set separator pressure, in MPa, Dest Fitness For target fitness; Step 3: Optimization and dynamic adjustment of gas injection parameters. Solve the model in Step 2. Take the gas distribution scheme of the platform's multi-well as one of the particles, and use the particle swarm optimization algorithm to calculate the fitness of each particle for optimization and dynamic adjustment. Step 4: Intelligent control of circulating gas lift. Build an intelligent control system for circulating gas lift, collect process and production data, and then, based on the particle swarm optimization algorithm, solve the multi-objective optimization model in real time with the goal of maximizing gas production to obtain the optimal gas injection rate. Subsequently, the optimized parameters are sent to the intelligent controller through the production network to adjust the opening of the gas lift valve and provide real-time feedback of the execution results to complete closed-loop control.

2. The platform circulating airlift intelligent optimization and edge control collaborative method according to claim 1, characterized in that: The fitness calculation in step three is specifically as follows: S1. First, draw the inflow curve of each well with the wellhead as the node under different gas injection rates; S2, for any solution ( Using interpolation, the inflow curve at the wellhead of each gas lift well is obtained; S3. Take a node A on the inflow curve, when the pressure at node A is P. Ai The liquid flow rate q at node A of the gas lift well can be obtained from the inflow curve. ni (n=1, 2, 3, ...), the liquid volume q of node A Ai =q 1i +q 2i +…+q ni From this, a series of (P) can be derived. A1 q A1 ), (P A2 q A2 )……(P Am q Am That is, the inflow curve of node A is obtained; S4. Similarly, the inflow curve for each node and the inflow curve with the separator as the node can be further obtained, based on the set separator pressure P. s The total liquid volume Q and gas production can be obtained from the inflow curve of the separator, which is the solution under the corresponding platform regime. S5. Calculate the solutions under all platform regimes, which are the fitness of the particles.

3. The platform circulating airlift intelligent optimization and edge control collaborative method according to claim 1, characterized in that: The optimization process in step three includes node analysis and well group optimization calculation. Node analysis specifically involves setting the separator as a key node, establishing a multiphase pipe flow model from the wellhead to the separator, combining the IPR curves of each well with the pipeline equations, and solving the coupling relationship between pressure and production. Well group optimization calculation specifically involves importing the single-well model and automatically running the particle swarm optimization algorithm to output the optimized gas injection rate for each well. The dynamic adjustment mechanism determines that when the casing pressure change exceeds a set threshold, it indicates a liquid accumulation state or a state after partial liquid discharge, meaning the well state has changed dynamically. In this case, the formation pressure and production / gas production index are recalculated, triggering the particle swarm optimization algorithm to update the gas distribution scheme.

4. The platform circulating airlift intelligent optimization and edge control collaborative method according to claim 1, characterized in that: The circulating gas lift intelligent control system in step four includes a data acquisition and transmission module, an edge-side optimization calculation module, an instruction execution and feedback module, and a cloud monitoring module. The data acquisition and transmission module collects process parameters and production data based on the interconnection between the RTU edge computing terminal and the well site PLC system and intelligent control valves. The edge-side optimization calculation module constructs a multi-objective optimization model based on the PSO algorithm through the RTU edge computing terminal, with the goal of maximizing gas production, and solves for the optimal gas injection rate in real time. The instruction execution and feedback module is used to send the optimization parameters to the intelligent controller through the production network, adjust the opening of the gas lift valve, and feed back the execution results to the edge terminal in real time to form a closed-loop control. The cloud monitoring module is used to synchronously upload all data to the POC platform to realize remote monitoring and analysis.

5. The platform circulating airlift intelligent optimization and edge control collaborative method according to claim 1, characterized in that: The specific control process in step four includes: S1, RTU edge control terminal performs real-time data acquisition of oil, casing pressure, pressure and temperature before throttling of air nozzle, pressure and temperature after throttling of air nozzle, instantaneous gas volume and instantaneous liquid volume. S2. A large amount of real-time data is processed by the circulating gas lift optimization algorithm module built into the RTU edge control terminal to solve for the optimal gas injection rate; S3. When the gas injection volume is outside the pre-set threshold, record the current data for analysis by experts and technicians, and provide example data for subsequent algorithm optimization. When the gas injection volume is within the threshold, send it to the POC platform for confirmation or select automatic execution. S4. In non-automatic execution mode, after the POC confirms the optimized gas injection parameters and confirms that they can be sent, the RTU edge computing terminal sends the data to the on-site intelligent control valve for gas injection optimization. S5. Synchronously upload all data to the POC platform to achieve remote monitoring and analysis.

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