Optimization method of drainage and gas production process system in complex gas wells

Abstract

The present invention relates to the field of data reasoning technology, and discloses a method for optimizing a drainage and gas production process system for a complex gas well. The method comprises: obtaining geological characteristic parameters and real-time production dynamic parameters of the gas well; identifying stable production constraints of the gas well based on the geological characteristic parameters and the real-time production dynamic parameters; establishing an optimization target for the drainage and gas production process for the gas well based on the stable production constraints; determining a critical liquid-carrying flow rate threshold of the gas well through the distribution morphology of liquid accumulation in the wellbore of the gas well; generating an optimal exhaust scheme for the gas injection speed and period in the gas well using the critical liquid-carrying flow rate threshold and the optimization target as boundary conditions; and adjusting the throttle opening and the gas injection timing in the gas well according to the optimal exhaust scheme. The present invention can improve the optimization quality of the drainage and gas production process system for the gas well.

Description

Technical Field

[0001] The present invention relates to the field of data reasoning technology, and in particular to a method for optimizing a drainage and gas production process system for a complex gas well. Background Art

[0002] When optimizing drainage and gas production systems for complex gas wells, existing technologies often rely on empirical parameter settings or single-dimensional optimization models, making it difficult to fully consider the coupling relationship between the well's geological characteristics and real-time production dynamics. Specifically, their identification of stable production constraints lacks a coordinated analysis of multiple parameters, such as reservoir permeability, reservoir pressure, and wellbore liquid holdup. This results in the determined bottomhole pressure threshold and wellbore flow rate threshold being unable to accurately match the actual production status of the gas well. This, in turn, results in poor adaptability of drainage and gas production systems, and the optimized structure is unable to meet the production needs of complex gas wells.

[0003] Furthermore, existing methods fail to fully consider the spatial variability of wellbore liquid accumulation distribution patterns when determining the critical liquid-carrying flow rate threshold. This leads to significant limitations in locating the liquid-carrying interface and calculating the liquid-carrying capacity of well sections. This results in the generated gas injection rate and cycle schemes either failing to effectively remove liquid accumulation or reducing gas recovery efficiency due to excessive injection energy consumption. Furthermore, their optimization objectives often overlook the dynamic requirements of gas wells at different production stages and lack a balanced approach between liquid-carrying removal rate and injection energy consumption. This ultimately leads to poor optimization quality of drainage gas production processes, making it difficult to achieve efficient and stable gas well production. Summary of the Invention

[0004] The present invention provides a method for optimizing a drainage and gas production process system for a complex gas well, the main purpose of which is to solve the problem of poor optimization structure when optimizing a drainage and gas production process system for a complex gas well.

[0005] To achieve the above objectives, the present invention provides a method for optimizing a water drainage and gas production process system in a complex gas well, comprising:

[0006] S1. Obtain geological characteristic parameters and real-time production dynamic parameters of gas wells;

[0007] S2. Identifying stable production constraint conditions of the gas well based on the geological characteristic parameters and the real-time production dynamic parameters;

[0008] S3. Establishing an optimization target for the gas well drainage and gas production process based on the stable production constraint conditions;

[0009] S4. Determining a critical liquid carrying flow rate threshold of the gas well based on the distribution pattern of liquid accumulation in the wellbore of the gas well;

[0010] S5. Using the critical liquid carrying flow rate threshold and the optimization target as boundary conditions, generating an optimal exhaust scheme for the gas injection rate and period in the gas well;

[0011] S6. Adjust the throttle opening and gas injection timing in the gas well according to the optimal exhaust solution.

[0012] In a preferred embodiment, the identifying of the stable production constraint conditions of the gas well based on the geological characteristic parameters and the real-time production dynamic parameters includes:

[0013] Determining a maximum bottom hole flowing pressure threshold of the gas well based on the reservoir permeability and gas reservoir pressure in the geological characteristic parameters;

[0014] converting the production performance parameter into a wellbore liquid holdup of the gas well;

[0015] When the liquid holdup exceeds a critical liquid holdup, activating a minimum wellbore flow rate threshold of the gas well;

[0016] The maximum bottom hole flowing pressure threshold and the minimum wellbore flow velocity threshold are combined to obtain the stable production constraint condition of the gas well.

[0017] In a preferred embodiment, establishing the optimization target of the gas well water drainage gas production process based on the stable production constraint condition includes:

[0018] Taking the minimum wellbore flow rate threshold as a boundary, in the mapping relationship between the gas injection rate and the liquid removal rate in the gas well, the maximum achievable value of the liquid removal rate is set as a first optimization sub-goal;

[0019] Taking the maximum bottom hole flowing pressure threshold as a constraint condition, setting the minimum gas injection energy consumption of the compressor in the gas well as the second optimization sub-objective;

[0020] Dynamically allocating weight coefficients of the first optimization sub-objective and the second optimization sub-objective based on the production stage of the gas well;

[0021] The weighted first optimization sub-objective and the second optimization sub-objective are combined to establish an optimization objective for the gas well water drainage and gas production process.

[0022] In a preferred embodiment, the dynamically allocating weight coefficients of the first optimization sub-objective and the second optimization sub-objective based on the production stage of the gas well includes:

[0023] When the gas well is in a stable production period, increasing the weight coefficient of the first optimization sub-objective;

[0024] When the gas well is in a decline period, the weight coefficient of the second optimization sub-objective is increased.

[0025] In a preferred embodiment, determining the critical liquid carrying flow rate threshold of the gas well based on the distribution of liquid accumulation in the wellbore of the gas well includes:

[0026] Locating the interface position of the accumulated liquid according to the gradient mutation point of the axial temperature distribution of the wellbore in the gas well;

[0027] Constructing a liquid accumulation distribution pattern of the gas well according to the interface position;

[0028] The liquid accumulation distribution pattern is divided into well section units, and the minimum local liquid carrying capacity value of the well section unit is taken as the critical liquid carrying flow rate threshold of the gas well.

[0029] In a preferred embodiment, the minimum local liquid carrying capacity value of the well section unit is used as the critical liquid carrying flow rate threshold of the gas well, including:

[0030] The local liquid carrying capacity value is calculated based on the liquid accumulation data and gas injection data of the well section unit, wherein the calculation formula of the local liquid carrying capacity value is as follows:

[0031] ;

[0032] Where, is the local liquid carrying capacity value, is the gas density of the gas injected into the well section unit, is the critical gas flow velocity of the gas injection flow in the well section unit, is the hydraulic diameter of the fluid accumulation in the well section unit, is the surface tension between the accumulated liquid and the injected gas flow in the well section unit;

[0033] The minimum value of the local liquid carrying capacity values ​​is taken as the critical liquid carrying flow rate threshold of the gas well.

[0034] In a preferred embodiment, the generating of the optimal exhaust scheme for the gas injection rate and period in the gas well with the critical liquid carrying flow rate threshold and the optimization target as boundary conditions includes:

[0035] Constructing a response surface model based on the predicted value of liquid removal rate and gas injection energy consumption of the gas well;

[0036] Marking a feasible domain in the response surface model that satisfies the critical liquid carrying flow rate threshold and the optimization objective;

[0037] The optimized solution with the lowest gas injection frequency in the feasible domain is used as the optimal gas exhaust solution for the gas well.

[0038] In a preferred embodiment, the predicted value of the liquid accumulation removal rate and the predicted value of the gas injection energy consumption of the gas well include:

[0039] Determining a predicted value of a liquid accumulation removal rate of the gas well according to a linear relationship between a gas injection rate and a reduction amount of liquid accumulation in the gas well within a unit cycle;

[0040] A predicted value of gas injection energy consumption of the gas well is generated according to the correlation between the compressor gas pressure and the actual pressure in the gas well.

[0041] In a preferred embodiment, the method of using the optimized solution with the lowest gas injection frequency in the feasible domain as the optimal gas exhaust solution for the gas well includes:

[0042] sorting the feasible regions according to the constraint violation degree;

[0043] Generating a Pareto solution set of the feasible domain according to the distribution of the solution set of the sorted feasible domain;

[0044] The Pareto solution set with the minimum gas injection frequency is used as the optimal gas exhaust solution for the gas well.

[0045] In a preferred embodiment, adjusting the choke opening and gas injection timing in the gas well according to the optimal exhaust scheme includes:

[0046] generating a pulse instruction sequence according to the gas injection timing of the optimal exhaust scheme;

[0047] Converting the pulse instruction sequence into a throttle stepper motor control signal;

[0048] When the exhaust sequence in the gas well is gas injection, the control signal of the throttle stepper motor is to open to a fully open state;

[0049] When the exhaust timing of the gas well is intermittent, the control signal of the throttle stepping motor is to keep the opening unchanged.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention obtains geological characteristic parameters and real-time production dynamic parameters of gas wells, accurately identifies stable production constraints and establishes optimization targets. Combined with the distribution of liquid accumulation in the wellbore, it determines the critical liquid-carrying flow rate threshold, thereby generating an optimal exhaust scheme for gas injection speed and cycle. The scheme then adjusts the choke opening and gas injection timing, enabling comprehensive and dynamic adaptation to the actual production conditions of the gas wells. This effectively enhances the pertinence and accuracy of the optimization of the drainage and gas production process system, fundamentally improving the optimization quality of the drainage and gas production process system.

[0052] 2. The present invention dynamically allocates optimization sub-objective weight coefficients to different production stages of gas wells, focusing on the optimization of liquid accumulation removal rate and gas injection energy consumption in the stable production period and the declining period respectively. At the same time, it uses response surface models and Pareto solution set technologies to generate the optimal exhaust scheme, achieving a balance between gas injection efficiency and energy consumption. It not only ensures efficient drainage of gas wells, but also reduces energy consumption while ensuring stable production, significantly improving the comprehensive benefits of gas well production and the optimization effect of the process system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic flow chart of a method for optimizing a water drainage and gas production process system for a complex gas well provided by one embodiment of the present invention;

[0054] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] The embodiment of the present application provides a method for optimizing the drainage and gas production process system of a complex gas well. The execution subject of the method for optimizing the drainage and gas production process system of a complex gas well includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for optimizing the drainage and gas production process system of a complex gas well can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0057] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing a water drainage and gas production process system for a complex gas well according to an embodiment of the present invention. In this embodiment, the method for optimizing a water drainage and gas production process system for a complex gas well includes:

[0058] S1. Obtain geological characteristic parameters and real-time production dynamic parameters of gas wells;

[0059] Specifically, geological exploration operations for gas wells are carried out using seismic exploration technology. By artificially exciting seismic waves, the seismic waves propagate in the underground medium and are reflected and refracted at different geological interfaces. Detectors arranged on the ground are used to receive the returned seismic wave signals, which are converted into electrical signals and recorded. After data processing and analysis, information such as the geological structure morphology and stratigraphic distribution of the area where the gas well is located is obtained, and then the geological characteristic parameters of the gas well are determined, including the type of gas reservoir, the lithology, thickness, porosity, and permeability of the reservoir.

[0060] Furthermore, various sensors are installed at the wellhead of the gas well. The pressure sensor is used to measure the bottomhole pressure and wellhead pressure of the gas well. By sensing the mechanical deformation caused by the pressure, the pressure signal is converted into an electrical signal output; the flow sensor uses a vortex flowmeter. When the gas flows through the sensor, vortices are alternately generated on both sides of the vortex generator to form a vortex column. The frequency of the vortex is proportional to the flow rate of the gas. The gas flow rate is calculated by detecting the vortex frequency; the temperature sensor uses the characteristic that the resistance value of metal or semiconductor materials changes with temperature, converts the temperature change into a resistance value change, and then converts it into an electrical signal through a circuit, so as to obtain the real-time production dynamic parameters of the gas well, covering wellhead pressure, bottomhole pressure, gas flow, temperature, etc.

[0061] In general, obtaining the geological characteristic parameters and real-time production dynamic parameters of gas wells can provide comprehensive and accurate data support for the optimization of the drainage and gas production process system of complex gas wells.

[0062] In general, by collecting geological characteristic parameters such as reservoir permeability and gas reservoir pressure, we can gain an in-depth understanding of the basic geological conditions of the gas well, and by obtaining real-time production dynamic parameters such as wellbore liquid holdup and gas injection rate, we can grasp the production status of the gas well in real time, so that subsequent optimization links such as the identification of stable production constraints and the determination of critical liquid flow rate thresholds are based on real and accurate data, avoiding optimization deviations caused by missing or inaccurate data, and ensuring the reliability and effectiveness of the optimization of the drainage gas production process system from the source.

[0063] In general, this operation achieves the systematic integration of gas well production-related data, allowing both geological static characteristics and production dynamic changes to be fully incorporated into the optimization considerations.

[0064] In general, based on these parameters, the actual needs of gas wells at different production stages can be analyzed more accurately, providing a solid data foundation for the subsequent establishment of reasonable optimization goals and the generation of scientific optimal exhaust solutions. This enables the entire optimization method to closely fit the actual conditions of the gas well, thereby improving the quality of optimization of the gas well drainage and gas production process system and ensuring that the optimization results are more targeted and practical.

[0065] S2. Identifying stable production constraint conditions of the gas well based on the geological characteristic parameters and the real-time production dynamic parameters;

[0066] In an embodiment of the present invention, the identifying of the stable production constraint conditions of the gas well based on the geological characteristic parameters and the real-time production dynamic parameters includes:

[0067] Determining a maximum bottom hole flowing pressure threshold of the gas well based on the reservoir permeability and gas reservoir pressure in the geological characteristic parameters;

[0068] converting the production performance parameter into a wellbore liquid holdup of the gas well;

[0069] When the liquid holdup exceeds a critical liquid holdup, activating a minimum wellbore flow rate threshold of the gas well;

[0070] The maximum bottom hole flowing pressure threshold and the minimum wellbore flow velocity threshold are combined to obtain the stable production constraint condition of the gas well.

[0071] Specifically, based on the reservoir permeability and gas reservoir pressure among the acquired geological characteristic parameters of the gas well, a pre-established relationship model is used to determine the maximum bottom hole flowing pressure threshold of the gas well.

[0072] Furthermore, this relationship model was obtained through field testing and data statistical analysis of a large number of gas wells with geological conditions similar to those of this gas well. The specific values ​​of reservoir permeability and gas reservoir pressure were input into the model, and after being processed according to the corresponding relationship set within the model, the maximum bottom hole flowing pressure threshold of the gas well was obtained.

[0073] Furthermore, a special conversion calculation method is used to convert the production performance parameters of the gas well into the wellbore liquid holdup.

[0074] Furthermore, the parameters related to the wellbore liquid holdup in the production dynamic parameters are first clarified, such as gas flow rate, wellhead pressure, temperature, etc.

[0075] Furthermore, by simulating the gas-liquid two-phase flow state under different working conditions in the laboratory, a database of the corresponding relationships between these production dynamic parameters and wellbore liquid holdup was established.

[0076] Furthermore, the production dynamic parameter values ​​actually measured are compared and matched with the data in the database, and the current wellbore liquid holdup of the gas well is determined based on the wellbore liquid holdup corresponding to the closest operating condition data.

[0077] Furthermore, a fixed critical liquid holdup value is set, which is determined based on engineering experience in gas well production and research on fluid flow characteristics in gas well bores.

[0078] Furthermore, when the gas wellbore liquid holdup calculated by the above method exceeds this pre-set critical liquid holdup, the minimum wellbore flow rate threshold of the gas well is immediately activated. This minimum wellbore flow rate threshold is also summarized through a large number of experiments and actual production experience and is used to ensure stable production of the gas well.

[0079] Furthermore, the determined maximum bottom hole flowing pressure threshold is combined with the activated minimum wellbore flow velocity threshold to form a stable production constraint condition for the gas well.

[0080] Furthermore, these two thresholds are used as limiting conditions. When the bottomhole flow pressure of the gas well does not exceed the maximum bottomhole flow pressure threshold and the wellbore flow rate is not lower than the minimum wellbore flow rate threshold, the gas well is considered to be in a stable production state. These two thresholds together constitute the necessary conditions to ensure the stable production of the gas well.

[0081] In general, identifying the stable production constraints of gas wells through geological characteristic parameters and real-time production dynamic parameters can construct accurate constraint boundaries from the dual dimensions of gas well geological foundation and production dynamics.

[0082] In general, determining the maximum bottomhole flowing pressure threshold based on reservoir permeability and reservoir pressure can prevent damage to gas well production due to excessive bottomhole flowing pressure. Converting production dynamic parameters into wellbore liquid holdup and activating the minimum wellbore flow rate threshold can effectively prevent liquid accumulation in the wellbore. This multi-parameter collaborative identification mechanism ensures that stable production constraints reflect both reservoir geological characteristics and real-time production conditions. This provides a scientific and dynamic constraint benchmark for subsequent process optimization, avoids constraint deviations caused by a single parameter, and improves the adaptability of drainage gas production technology to the actual production environment of gas wells.

[0083] In general, this identification process achieves a deep coupling of geological static parameters and production dynamic data. By combining the maximum bottomhole pressure threshold and the minimum wellbore flow velocity threshold, a composite constraint system covering gas reservoir production capacity and wellbore flow status is formed.

[0084] In general, this constraint condition can not only ensure the stable production of gas wells within a reasonable flow pressure range, but also ensure the liquid carrying capacity of the wellbore through the flow rate threshold, fundamentally avoiding the problems of liquid retention or production capacity waste caused by inaccurate constraint conditions, and providing a reliable constraint basis for subsequent links such as establishing optimization goals and determining critical liquid carrying flow rate thresholds, thereby improving the accuracy and effectiveness of the entire drainage gas production process system optimization.

[0085] S3. Establishing an optimization target for the gas well drainage and gas production process based on the stable production constraint conditions;

[0086] In an embodiment of the present invention, establishing an optimization target for the gas well water drainage and gas production process based on the stable production constraint condition includes:

[0087] Taking the minimum wellbore flow rate threshold as a boundary, in the mapping relationship between the gas injection rate and the liquid removal rate in the gas well, the maximum achievable value of the liquid removal rate is set as a first optimization sub-goal;

[0088] Taking the maximum bottom hole flowing pressure threshold as a constraint condition, setting the minimum gas injection energy consumption of the compressor in the gas well as the second optimization sub-objective;

[0089] Dynamically allocating weight coefficients of the first optimization sub-objective and the second optimization sub-objective based on the production stage of the gas well;

[0090] The weighted first optimization sub-objective and the second optimization sub-objective are combined to establish an optimization objective for the gas well water drainage and gas production process.

[0091] The dynamically allocating weight coefficients of the first optimization sub-objective and the second optimization sub-objective based on the production stage of the gas well includes:

[0092] When the gas well is in a stable production period, increasing the weight coefficient of the first optimization sub-objective;

[0093] When the gas well is in a decline period, the weight coefficient of the second optimization sub-objective is increased.

[0094] Specifically, through a large number of simulation experiments and actual gas well production data collection, the corresponding liquid accumulation removal rate curves under different gas injection rates were drawn, thereby establishing a mapping relationship between gas injection rate and liquid accumulation removal rate in gas wells.

[0095] Furthermore, using the determined minimum wellbore flow rate threshold as the boundary condition, the maximum value of the accumulated liquid removal rate that can be achieved while meeting the minimum wellbore flow rate threshold is found in the above mapping relationship, and this value is set as the first optimization sub-goal.

[0096] Furthermore, the gas injection energy consumption of the gas well compressor under different working conditions was tested and recorded. At the same time, combined with the production data of the gas well, the relationship between the compressor gas injection energy consumption and the bottom hole flow pressure was analyzed.

[0097] Furthermore, taking the maximum bottom hole flow pressure threshold as the constraint condition, the working state with the minimum compressor gas injection energy consumption is screened out among various working conditions that meet the maximum bottom hole flow pressure threshold, and the minimum gas injection energy consumption in this state is set as the second optimization sub-goal.

[0098] Furthermore, based on the different production stages of the gas well, such as the early, middle and late stages of exploitation, and with reference to the operating experience of similar gas wells in the industry at various production stages and the actual production data of this gas well, weight coefficients are dynamically assigned to the first optimization sub-goal and the second optimization sub-goal.

[0099] Furthermore, in the early stage of gas well production, due to the high gas well production capacity, the weight coefficient of the first optimization sub-goal can be appropriately increased to speed up the removal of accumulated liquid; in the later stage of production, the gas well production capacity decreases, at this time the weight coefficient of the second optimization sub-goal is increased to reduce energy consumption and maximize economic benefits.

[0100] Furthermore, the first optimization sub-objective and the second optimization sub-objective to which the weight coefficients are assigned are combined.

[0101] Furthermore, the value of the first optimization sub-goal is multiplied by its corresponding weight coefficient, the value of the second optimization sub-goal is multiplied by its corresponding weight coefficient, and then the two products are added together. The result obtained is the optimization target of the gas well drainage and gas production process. Guided by this target, the gas well drainage and gas production process is optimized and improved.

[0102] Specifically, a gas well production stage identification system is established to collect multiple production data such as gas well output, pressure, fluid properties, etc., and compare them with pre-set stable production period data standards.

[0103] Furthermore, when the production of a gas well remains relatively stable within a certain period of time, the fluctuation range is within the specified interval, and parameters such as wellhead pressure and bottom hole pressure are also within the normal stable range, and meet the data standards for the stable production period, the gas well is determined to be in the stable production period. Then, on the basis of the original weight coefficients of the first optimization sub-objective and the second optimization sub-objective, the weight coefficient of the first optimization sub-objective is increased to further improve the liquid removal rate and ensure stable and high production of the gas well.

[0104] Furthermore, based on the same gas well production stage identification system, production data such as gas well production and pressure are continuously monitored. When a gas well's production shows a significant downward trend, and the magnitude of the decline exceeds the specified standard, and parameters such as wellhead and bottomhole pressure also decrease accordingly, failing to meet the data criteria for the stable production period but meeting the data criteria for the declining period, the gas well is determined to be in the declining period. At this point, the weight coefficient of the second optimization sub-objective is increased based on the original weight coefficient, focusing on reducing compressor injection energy consumption, thereby maximizing economic benefits despite the decline in gas well production.

[0105] In general, establishing the optimization objectives of the gas well drainage and gas production process based on stable production constraints can transform the boundary limitations in the actual production of gas wells into quantifiable optimization directions.

[0106] In general, the maximum achievable value of the liquid removal rate is set as the first optimization sub-objective with the minimum wellbore flow velocity threshold as the boundary to ensure that the wellbore has sufficient liquid carrying capacity to prevent liquid accumulation; the minimum gas injection energy consumption of the compressor is determined as the second optimization sub-objective with the maximum bottomhole pressure threshold as the constraint to reduce energy consumption costs while ensuring the gas well productivity.

[0107] In general, this dual-objective construction mechanism enables the optimization objectives to meet both the physical constraints of stable gas well production and the need to maximize production efficiency, avoiding the disconnection between the optimization objectives and actual production conditions, and providing a clear and feasible goal orientation for the subsequent optimization of gas injection schemes.

[0108] In general, the target establishment process achieves dynamic adaptation of the optimization strategy by dynamically allocating dual-objective weight coefficients according to the gas well production stage.

[0109] In general, during the stable production period, the weight of the liquid removal rate target is increased to ensure high and stable gas well production; during the declining period, the focus is on gas injection energy consumption targets to reduce production costs. This dynamic weighting mechanism allows the optimization target to be adjusted as the gas well lifecycle evolves, avoiding optimization bias caused by fixed weights. This not only ensures the core production needs of gas wells at different stages, but also achieves a balanced optimization of liquid removal efficiency and energy consumption costs. This improves the adaptability of the drainage gas production process system to the production of gas wells throughout their entire lifecycle, significantly enhancing the targetedness and overall benefits of process optimization.

[0110] S4. Determining a critical liquid carrying flow rate threshold of the gas well based on the distribution pattern of liquid accumulation in the wellbore of the gas well;

[0111] In an embodiment of the present invention, determining the critical liquid carrying flow rate threshold of the gas well based on the distribution of liquid accumulation in the wellbore of the gas well includes:

[0112] Locating the interface position of the accumulated liquid according to the gradient mutation point of the axial temperature distribution of the wellbore in the gas well;

[0113] Constructing a liquid accumulation distribution pattern of the gas well according to the interface position;

[0114] The liquid accumulation distribution pattern is divided into well section units, and the minimum local liquid carrying capacity value of the well section unit is taken as the critical liquid carrying flow rate threshold of the gas well.

[0115] The minimum local liquid carrying capacity value of the well section unit is used as the critical liquid carrying flow rate threshold of the gas well, including:

[0116] The local liquid carrying capacity value is calculated based on the liquid accumulation data and gas injection data of the well section unit, wherein the calculation formula of the local liquid carrying capacity value is as follows:

[0117] ;

[0118] Where, is the local liquid carrying capacity value, is the gas density of the gas injected into the well section unit, is the critical gas flow velocity of the gas injection flow in the well section unit, is the hydraulic diameter of the fluid accumulation in the well section unit, is the surface tension between the accumulated liquid and the injected gas flow in the well section unit;

[0119] The minimum value of the local liquid carrying capacity values ​​is taken as the critical liquid carrying flow rate threshold of the gas well.

[0120] Specifically, temperature sensors are installed at equal distances along the axial direction in the gas wellbore to continuously collect temperature data at different positions in the wellbore.

[0121] Furthermore, due to the differences in heat transfer characteristics between accumulated liquid and gas, the temperature changes at and near the location of the accumulated liquid are different from those at other locations. By analyzing the collected temperature data, the points where the temperature changes suddenly intensify in the axial temperature distribution of the wellbore, namely the gradient mutation points, are found. The positions corresponding to these gradient mutation points are the interface positions of the accumulated liquid.

[0122] Furthermore, based on the determined position of the liquid accumulation interface, combined with information such as the structural parameters of the gas wellbore and the gas flow state, a distribution morphology model of the liquid accumulation in the gas well in the wellbore was constructed using three-dimensional modeling software with the liquid accumulation interface position as the key node.

[0123] Furthermore, in the modeling process, the influence of factors such as the inclination angle of the gas well and the change of the wellbore diameter on the liquid accumulation distribution is considered, so that the constructed liquid accumulation distribution morphology can reflect the actual distribution of the liquid accumulation in the gas well as realistically as possible.

[0124] Furthermore, the constructed liquid accumulation distribution pattern is divided into multiple well section units according to a certain length standard, and each well section unit has a different local liquid carrying capacity.

[0125] Furthermore, for each well section unit, by simulating the situation of gas carrying accumulated liquid in the well section under different flow rates, the minimum gas flow rate that can be achieved by each well section unit while ensuring that the accumulated liquid does not accumulate and fall back is recorded, that is, the local liquid carrying capacity value of the well section unit.

[0126] Furthermore, the minimum value is selected from the local liquid carrying capacity values ​​of all well section units and determined as the critical liquid carrying flow rate threshold of the gas well, which is used as an important indicator to judge whether the gas well can effectively carry the accumulated liquid.

[0127] Specifically, for each well section, liquid accumulation data and gas injection data were collected. Liquid accumulation data was acquired through a liquid level sensor installed in the gas wellbore, including information such as the height and volume of the accumulated liquid within the well section. Gas injection data was provided by the gas injection equipment monitoring system at the wellhead, covering information such as the flow rate and pressure of the injected gas. A simulation environment similar to that of the gas well section was established in the laboratory. The collected liquid accumulation and gas injection data were used as the initial conditions for the simulation environment. The gas flow rate in the simulation environment was gradually increased to observe the extent of liquid carryover.

[0128] Furthermore, when the gas can just carry away all the accumulated liquid in the well section unit without any residual liquid or fallback, the gas flow rate at this time is the local liquid carrying capacity value of the well section unit. By performing such a simulation operation for each well section unit, the local liquid carrying capacity values ​​of all well section units are obtained.

[0129] Furthermore, the local liquid-carrying capacity values ​​of all well sections obtained through the above simulation operations are listed and compared, and the size of each value is checked one by one. The local liquid-carrying capacity value with the smallest value is found and determined as the critical liquid-carrying flow rate threshold of the gas well. This threshold is a key indicator to ensure that the accumulated liquid in the gas well can be effectively carried and maintain normal production of the gas well.

[0130] Specifically, the gas density of the injected gas in the well section unit is obtained through a gas density measuring instrument installed at the wellhead. The instrument uses the principle of a vibrating densitometer. When the gas flows through the measuring tube, the vibration frequency of the measuring tube will change due to different gas densities. By detecting the vibration frequency and through a pre-calibrated correspondence, it is converted into a gas density value.

[0131] Furthermore, the critical airflow velocity of the gas injection airflow in the well section unit is determined by building a simulated pipeline similar to the well section unit in the laboratory and gradually increasing the gas flow in the pipeline. When the gas can just carry away all the simulated accumulated liquid in the pipeline without any residual liquid or fallback, the gas flow velocity measured at this time is the critical airflow velocity.

[0132] Furthermore, the hydraulic diameter of the accumulated liquid in the well section unit is obtained by measuring the cross-sectional area and wetted perimeter of the space occupied by the accumulated liquid in the well section unit, multiplying the cross-sectional area by 4 and then dividing it by the wetted perimeter.

[0133] Furthermore, the surface tension of the accumulated liquid and the injected gas flow in the well section unit is measured using the hanging drop method. The accumulated liquid drop is suspended at the end of the needle, and the image of the hanging drop is captured using an optical system. The shape parameters of the hanging drop are measured using image analysis software, and then the surface tension value is calculated based on the pre-established relationship between the shape parameters and surface tension.

[0134] Furthermore, this formula is used to calculate the local liquid-carrying capacity of a well section unit. It comprehensively considers factors such as the gas density of the injected gas in the well section unit, the critical gas flow velocity of the injected gas flow, the hydraulic diameter of the accumulated liquid, and the surface tension between the accumulated liquid and the injected gas flow. Through a specific calculation method, these factors are integrated to obtain a value that can reflect the size of the well section unit's liquid-carrying capacity.

[0135] Furthermore, when the gas density of the injected gas in the well section unit increases, the local liquid carrying capacity value will increase under the condition that other conditions remain unchanged, because the greater the gas density, the greater the gas mass under the same volume, and the ability to carry accumulated liquid is relatively stronger; when the critical airflow velocity of the injected airflow increases, the local liquid carrying capacity value will also increase, because the faster the critical airflow velocity, the stronger the power of the gas to carry accumulated liquid; when the hydraulic diameter of the accumulated liquid increases, the local liquid carrying capacity value increases, because a larger hydraulic diameter means a larger contact area between the gas and the accumulated liquid, which is conducive to the gas carrying the accumulated liquid; when the surface tension between the accumulated liquid and the injected airflow increases, the local liquid carrying capacity value decreases, because the larger surface tension makes it more difficult for the accumulated liquid to be carried by the gas.

[0136] In general, determining the critical liquid-carrying flow rate threshold through the distribution pattern of liquid accumulation in the wellbore of a gas well can accurately locate the position of the liquid accumulation interface and construct the distribution pattern based on the spatial distribution characteristics of the liquid accumulation in the wellbore. After dividing it into well section units, the minimum local liquid-carrying capacity value is taken as the threshold, achieving a refined consideration of the differences in liquid-carrying capacity in different wellbore sections.

[0137] In general, this threshold determination method based on the distribution morphology of liquid accumulation avoids the limitations of traditional single-parameter calculations, fully considers the actual distribution state of liquid accumulation reflected by the sudden change in the axial temperature gradient of the wellbore, and makes the critical liquid-carrying flow rate threshold more consistent with the actual liquid accumulation situation in the gas wellbore. It provides a more accurate critical parameter basis for the subsequent optimization of gas injection rate and cycle, and effectively improves the adaptability of the drainage gas production process to the dynamic changes of wellbore liquid accumulation.

[0138] In general, the determination process converts the spatial heterogeneity of wellbore fluid distribution into quantifiable threshold parameters through well section unit division and local fluid carrying capacity calculation model.

[0139] In general, starting from the coupling relationship between liquid accumulation data and gas injection data, and comprehensively considering multiple physical parameters such as gas density, critical gas flow velocity, hydraulic diameter and surface tension, the threshold calculation can reflect the morphological characteristics of liquid accumulation and include the influencing factors of gas injection process, thus avoiding the threshold deviation caused by ignoring the differences in local liquid carrying capacity of the wellbore.

[0140] In general, this refined threshold determination method can more accurately define the critical state of the gas well's liquid carrying capacity, laying the foundation for generating a scientific and optimal gas discharge plan, thereby improving the accuracy and effectiveness of the optimization of the gas well drainage and gas production process system, and ensuring that the gas injection plan can effectively remove the accumulated liquid without causing energy waste in actual application.

[0141] S5. Using the critical liquid carrying flow rate threshold and the optimization target as boundary conditions, generating an optimal exhaust scheme for the gas injection rate and period in the gas well;

[0142] In an embodiment of the present invention, the step of generating an optimal exhaust scheme for the gas injection rate and period in the gas well using the critical liquid carrying flow rate threshold and the optimization target as boundary conditions includes:

[0143] Constructing a response surface model based on the predicted value of liquid removal rate and gas injection energy consumption of the gas well;

[0144] Marking a feasible domain in the response surface model that satisfies the critical liquid carrying flow rate threshold and the optimization objective;

[0145] The optimized solution with the lowest gas injection frequency in the feasible domain is used as the optimal gas exhaust solution for the gas well.

[0146] The predicted value of the liquid accumulation removal rate and the predicted value of the gas injection energy consumption of the gas well include:

[0147] Determining a predicted value of a liquid accumulation removal rate of the gas well according to a linear relationship between a gas injection rate and a reduction amount of liquid accumulation in the gas well within a unit cycle;

[0148] A predicted value of gas injection energy consumption of the gas well is generated according to the correlation between the compressor gas pressure and the actual pressure in the gas well.

[0149] The step of taking the optimized solution with the lowest gas injection frequency in the feasible domain as the optimal gas exhaust solution for the gas well includes:

[0150] sorting the feasible regions according to the constraint violation degree;

[0151] Generating a Pareto solution set of the feasible domain according to the distribution of the solution set of the sorted feasible domain;

[0152] The Pareto solution set with the minimum gas injection frequency is used as the optimal gas exhaust solution for the gas well.

[0153] Specifically, through the collection and analysis of historical production data, a gas well liquid accumulation removal rate prediction model and a gas injection energy consumption prediction model were constructed using data fitting.

[0154] Furthermore, the actual liquid removal rate, gas injection energy consumption, and corresponding gas injection flow rate, wellhead pressure and other related data of the gas well under different operating conditions over a period of time are collected, and these data are input into the data fitting software. The software continuously adjusts the model parameters to make the model prediction results as close as possible to the actual data, thereby obtaining an accurate liquid removal rate prediction model and a gas injection energy consumption prediction model.

[0155] Furthermore, using these two prediction models, a series of different operating parameters such as gas injection flow rate and wellhead pressure were set to calculate the corresponding predicted values ​​of liquid accumulation removal rate and gas injection energy consumption.

[0156] Furthermore, these predicted values ​​were used as coordinate points and input into the 3D modeling software. Through the surface generation function of the software, a response surface model reflecting the relationship between the predicted value of the effusion removal rate and the predicted value of the gas injection energy consumption was constructed.

[0157] Furthermore, the determined critical liquid carrying flow rate threshold and optimization target are used as constraints to perform screening operations in the response surface model.

[0158] Furthermore, specifically in the response surface model, the predicted value of the accumulated liquid removal rate corresponding to each coordinate point is checked one by one to see whether it meets the critical liquid carrying flow rate threshold requirement, and at the same time, it is checked whether the predicted value of the gas injection energy consumption is within the range set by the optimization target.

[0159] Furthermore, all coordinate points that meet these two conditions are marked out, and the area formed by these coordinate points in the response surface model is the feasible region that meets the requirements.

[0160] Furthermore, all optimization solutions within the feasible domain are analyzed, and the gas injection frequency corresponding to each optimization solution is counted.

[0161] Furthermore, the number of gas injections within a certain time period, ie, the gas injection frequency, is calculated based on the gas injection flow rate, gas injection time and other parameters set in each optimization solution.

[0162] Furthermore, the gas injection frequencies of all optimization solutions in the feasible domain are compared, and the optimization solution with the lowest gas injection frequency is found. This solution is determined as the optimal exhaust solution for the gas well. This solution can reduce the number of gas injection operations while meeting the production requirements of the gas well, thereby reducing production costs and equipment losses.

[0163] Specifically, data on gas injection rates and liquid accumulation reduction are collected over multiple unit cycles. The injection rate data is recorded in real time by a flow meter installed on the gas injection pipeline. The liquid accumulation reduction is calculated based on the changes in the liquid level within the wellbore, measured by a liquid level sensor installed within the wellbore.

[0164] Furthermore, the collected data are organized into data groups, each group including the gas injection rate and the corresponding liquid accumulation reduction within a unit cycle.

[0165] Furthermore, these data sets are imported into data analysis software, and the software's linear regression function is used. This function will continuously try different linear equations to find a straight line that minimizes the sum of the distances from all data points to the straight line. The relationship represented by this straight line is the linear relationship between the gas injection rate and the amount of fluid reduction.

[0166] Furthermore, the obtained linear relationship is used to input new gas injection rate data. The software calculates the corresponding liquid accumulation reduction through this linear relationship, and then calculates the predicted value of the liquid accumulation removal rate of the gas well based on the unit cycle length.

[0167] Furthermore, a pressure sensor is installed on the compressor of the gas well to monitor the internal air pressure and actual output pressure of the compressor in real time.

[0168] Furthermore, monitoring data on the compressor's air pressure and actual pressure over a period of time is collected. The actual energy consumption of the compressor during that period is also recorded and directly read using energy consumption metering equipment. This data is organized into data pairs in chronological order, with each pair containing the compressor's air pressure, actual pressure, and corresponding energy consumption data at a specific moment.

[0169] Furthermore, data mining software is used to analyze these data pairs. By learning from a large number of data pairs, the software can mine the inherent correlation patterns between compressor air pressure and actual pressure, as well as their relationship with gas injection energy consumption.

[0170] Furthermore, when new compressor pressure and actual pressure data are input, the data mining software predicts the corresponding gas injection energy consumption based on the learned association patterns and connections, thereby generating a predicted value of gas injection energy consumption for the gas well.

[0171] Specifically, for each solution in the feasible region, its satisfaction with the two constraints of critical liquid carrying flow rate threshold and optimization objective is checked one by one.

[0172] Furthermore, the specific operation is to compare the predicted value of the liquid removal rate corresponding to each solution with the critical liquid carrying flow rate threshold, as well as the predicted value of the gas injection energy consumption with the range set by the optimization target.

[0173] Furthermore, if the solution does not completely satisfy the constraints, the degree to which the solution deviates from the constraints, i.e., the constraint violation degree, is calculated.

[0174] For example, if the predicted liquid removal rate is lower than the critical liquid flow rate threshold, the absolute value of the difference between the two is calculated as part of the violation degree. If the predicted gas injection energy consumption exceeds the optimization target range, the absolute value of the excess is also calculated. All solutions are sorted from low to high according to the constraint violation degree to obtain the sorted feasible region.

[0175] Furthermore, a comprehensive analysis of the sorted feasible domain is conducted to observe the distribution of each solution in the solution set in terms of the predicted value of the liquid removal rate and the predicted value of the gas injection energy consumption.

[0176] Furthermore, for any two solutions, if one solution has a better predicted value for the liquid removal rate and gas injection energy consumption than the other, the inferior solution will be eliminated. By repeatedly comparing and eliminating these solutions, only those solutions that do not have other solutions that are better in both dimensions are retained. These retained solutions together constitute the Pareto solution set of the feasible domain. The solutions in this solution set achieve a balance between the two objectives, and it is impossible to improve one objective without sacrificing the other.

[0177] Furthermore, for each solution in the generated Pareto solution set, the number of gas injections within a certain time period, ie, the gas injection frequency, is calculated according to the set parameters such as gas injection flow rate and gas injection time.

[0178] Furthermore, the gas injection frequencies of all solutions within the Pareto solution set are carefully compared to identify the solution with the lowest injection frequency value, which is then determined as the optimal gas exhaust solution for the gas well. This solution achieves production targets with the minimum number of gas injection operations while satisfying the production constraints of the gas well, helping to reduce equipment operating costs and maintenance frequency.

[0179] In general, generating the optimal exhaust scheme with the critical liquid-carrying flow rate threshold and optimization target as boundary conditions can deeply combine the critical parameters of the gas well's liquid-carrying capacity with the multi-objective optimization requirements. By constructing a response surface model of the liquid removal rate and gas injection energy consumption, the process effects under different gas injection rates and cycles are systematically analyzed, and the feasible domain that meets the dual boundary conditions is accurately marked, avoiding the blindness of the gas injection scheme design.

[0180] In general, this boundary condition-based scheme generation mechanism not only ensures that the gas injection rate is no lower than the critical liquid-carrying flow threshold to prevent liquid accumulation, but also balances energy consumption and drainage efficiency by optimizing the target, so that the generated gas discharge scheme can maximize the overall benefits while ensuring stable production of the gas well, significantly improving the matching degree between the gas injection scheme and the actual production needs of the gas well.

[0181] In general, the generation process achieves intelligent decision-making of the gas injection process by sorting the feasible domain according to the degree of constraint violation and generating a Pareto solution set, and screening the scheme with the lowest gas injection frequency as the optimization goal.

[0182] In general, starting from the quantitative relationship between the predicted value of liquid accumulation removal rate and the predicted value of gas injection energy consumption, combined with data support such as the linear relationship between gas injection rate and liquid accumulation reduction in a gas well per unit cycle, and the correlation between compressor gas pressure and actual pressure, the optimal exhaust scheme not only meets the physical constraints of critical liquid carrying, but also conforms to the dynamic optimization goals of different production stages.

[0183] In general, this data-driven solution generation method avoids the problems of unreasonable gas injection frequency or energy waste caused by empirical design, and provides gas wells with a gas injection strategy that combines efficient drainage capabilities with low energy consumption, thereby improving the optimization quality and practical application effect of the drainage and gas production process system.

[0184] S6. Adjust the throttle opening and gas injection timing in the gas well according to the optimal exhaust solution.

[0185] In an embodiment of the present invention, adjusting the choke opening and gas injection timing in the gas well according to the optimal exhaust scheme includes:

[0186] generating a pulse instruction sequence according to the gas injection timing of the optimal exhaust scheme;

[0187] Converting the pulse instruction sequence into a throttle stepper motor control signal;

[0188] When the exhaust sequence in the gas well is gas injection, the control signal of the throttle stepper motor is to open to a fully open state;

[0189] When the exhaust timing of the gas well is intermittent, the control signal of the throttle stepping motor is to keep the opening unchanged.

[0190] Specifically, according to the gas injection timing specified in the determined optimal gas exhaust plan for the gas well, the gas injection process is divided into a plurality of discrete time nodes in chronological order.

[0191] Furthermore, for each time node, a corresponding pulse signal is generated according to whether the node is in the gas injection stage.

[0192] Furthermore, if it is in the gas injection stage, a high-level pulse signal is generated; if it is in the non-gas injection stage, a low-level pulse signal is generated.

[0193] Furthermore, these pulse signals arranged in time sequence are combined to form a complete pulse instruction sequence, thereby accurately controlling the timing of gas injection.

[0194] Furthermore, the generated pulse instruction sequence is processed by a signal conversion device.

[0195] Furthermore, a signal recognition and conversion circuit is provided inside the signal conversion device, which first recognizes the input pulse instruction sequence and determines the level state and duration of each pulse signal.

[0196] Furthermore, based on the control requirements of the throttle stepper motor, the level state and duration of the pulse signal are converted into specific electrical signal parameters, such as voltage value, current value, and pulse frequency. These converted electrical signal parameters are combined to form a control signal that can directly control the operation of the throttle stepper motor.

[0197] Furthermore, when the gas well's exhaust sequence reaches the injection phase, the control signal processing system receives relevant information about the injection phase and adjusts the converted control signal for the choke stepper motor. By changing parameters such as the voltage and pulse frequency in the control signal, the system sends instructions to the choke stepper motor, causing it to rotate in a specific direction and number of steps, driving the choke valve core and adjusting the choke opening to a fully open state, thereby ensuring that the gas injection process proceeds smoothly at maximum flow rate.

[0198] Furthermore, when the exhaust timing of the gas well is in an intermittent stage, the control signal processing system keeps various parameters in the throttle stepper motor control signal unchanged based on information of the intermittent stage.

[0199] Furthermore, since the operating state of the stepper motor depends on the input control signal parameters, when the control signal parameters remain unchanged, the stepper motor will not rotate, which means that the valve core position of the throttle remains fixed, thereby achieving an unchanged throttle opening and maintaining a stable state of the gas well in the intermittent stage.

[0200] In general, adjusting the choke opening and gas injection timing in the gas well according to the optimal exhaust plan can accurately transform the intelligent gas injection strategy into actual process control actions.

[0201] In summary, by generating a pulse instruction sequence based on the optimal injection sequence and converting it into a control signal for the choke stepper motor, the injection process is precisely and automatically regulated. During the injection sequence, the control signal fully opens the choke to ensure sufficient injection rate; during the intermittent sequence, the choke remains open to maintain a stable wellbore flow state.

[0202] In general, this adjustment mechanism makes the throttle action highly matched with the optimal exhaust plan, avoiding the lag and error of manual adjustment, ensuring that the gas injection process is strictly implemented according to the optimization plan, thereby effectively improving the implementation accuracy and control effect of the drainage gas production process.

[0203] In general, the adjustment process establishes a closed-loop control link from the optimization plan to on-site execution. Through the conversion of pulse instructions and motor control signals, the dynamic response of the gas injection timing and the precise control of the throttle opening are achieved.

[0204] In general, this real-time regulation method based on the optimal solution not only quickly reaches the critical liquid-carrying flow rate threshold during the injection phase to efficiently remove accumulated liquid, but also maintains a reasonable wellbore pressure during the intermittent phase, avoiding energy waste. This mechanism ensures that the execution of the drainage gas production process is highly consistent with the optimization goal, effectively resolving the disconnect between the solution and execution in traditional regulation methods, and improving the automation and intelligence level of the gas well production process. This in turn enhances the practical application benefits of the drainage gas production process optimization system and the stability of gas well production.

[0205] In the several embodiments provided by the present invention, it should be understood that the disclosed methods can be implemented in other ways.

[0206] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0207] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method and technology of using digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to achieve optimal results.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the drainage and gas production process system of a complex gas well, characterized by: The method comprises: S1. Obtain geological characteristic parameters and real-time production dynamic parameters of gas wells; S2. Identifying stable production constraint conditions of the gas well based on the geological characteristic parameters and the real-time production dynamic parameters; S3. Establishing an optimization target for the gas well drainage and gas production process based on the stable production constraint conditions; S4. Determining the critical liquid-carrying flow rate threshold of the gas well based on the distribution of liquid accumulation in the wellbore of the gas well, including: determining the critical liquid-carrying flow rate threshold of the gas well based on the distribution of liquid accumulation in the wellbore of the gas well, including: Locating the interface position of the accumulated liquid according to the gradient mutation point of the axial temperature distribution of the wellbore in the gas well; Constructing a liquid accumulation distribution pattern of the gas well according to the interface position; The liquid accumulation distribution pattern is divided into well section units, and the minimum local liquid carrying capacity value of the well section unit is taken as the critical liquid carrying flow rate threshold of the gas well. The calculation formula of the local liquid carrying capacity value is as follows: ； Where, is the local liquid carrying capacity value, is the gas density of the gas injected into the well section unit, is the critical gas flow velocity of the gas injection flow in the well section unit, is the hydraulic diameter of the fluid accumulation in the well section unit, is the surface tension between the accumulated liquid and the injected gas flow in the well section unit; Taking the minimum value of the local liquid carrying capacity values ​​as the critical liquid carrying flow rate threshold of the gas well; S5. Using the critical liquid carrying flow rate threshold and the optimization target as boundary conditions, generating an optimal exhaust scheme for the gas injection rate and period in the gas well; S6. Adjust the throttle opening and gas injection timing in the gas well according to the optimal exhaust solution.

2. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 1, characterized in that: The identifying of the stable production constraint conditions of the gas well according to the geological characteristic parameters and the real-time production dynamic parameters includes: Determining a maximum bottom hole flowing pressure threshold of the gas well based on the reservoir permeability and gas reservoir pressure in the geological characteristic parameters; converting the production performance parameter into a wellbore liquid holdup of the gas well; When the liquid holdup exceeds a critical liquid holdup, activating a minimum wellbore flow rate threshold of the gas well; The maximum bottom hole flowing pressure threshold and the minimum wellbore flow velocity threshold are combined to obtain the stable production constraint condition of the gas well.

3. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 2, characterized in that: The optimization goal of establishing the gas well drainage gas production process based on the stable production constraint condition includes: Taking the minimum wellbore flow rate threshold as a boundary, in the mapping relationship between the gas injection rate and the liquid removal rate in the gas well, the maximum achievable value of the liquid removal rate is set as a first optimization sub-goal; Taking the maximum bottom hole flowing pressure threshold as a constraint condition, setting the minimum gas injection energy consumption of the compressor in the gas well as the second optimization sub-objective; Dynamically allocating weight coefficients of the first optimization sub-objective and the second optimization sub-objective based on the production stage of the gas well; The weighted first optimization sub-objective and the second optimization sub-objective are combined to establish an optimization objective for the gas well water drainage and gas production process.

4. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 3, characterized in that: The dynamically allocating weight coefficients of the first optimization sub-objective and the second optimization sub-objective based on the production stage of the gas well includes: When the gas well is in a stable production period, increasing the weight coefficient of the first optimization sub-objective; When the gas well is in a decline period, the weight coefficient of the second optimization sub-objective is increased.

5. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 1, characterized in that: The step of generating an optimal exhaust scheme for the gas injection rate and period in the gas well using the critical liquid carrying flow rate threshold and the optimization target as boundary conditions includes: Constructing a response surface model based on the predicted value of liquid removal rate and gas injection energy consumption of the gas well; Marking a feasible domain in the response surface model that satisfies the critical liquid carrying flow rate threshold and the optimization objective; The optimized solution with the lowest gas injection frequency in the feasible domain is used as the optimal gas exhaust solution for the gas well.

6. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 5, characterized in that: The predicted value of the liquid accumulation removal rate and the predicted value of the gas injection energy consumption of the gas well include: Determining a predicted value of a liquid accumulation removal rate of the gas well according to a linear relationship between a gas injection rate and a reduction amount of liquid accumulation in the gas well within a unit cycle; A predicted value of gas injection energy consumption of the gas well is generated according to the correlation between the compressor gas pressure and the actual pressure in the gas well.

7. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 6, characterized in that: The step of taking the optimized solution with the lowest gas injection frequency in the feasible domain as the optimal gas exhaust solution for the gas well includes: sorting the feasible regions according to the constraint violation degree; Generating a Pareto solution set of the feasible domain according to the distribution of the solution set of the sorted feasible domain; The Pareto solution set with the minimum gas injection frequency is used as the optimal gas exhaust solution for the gas well.

8. The method for optimizing the drainage and gas production process system of a complex gas well according to claim 1, characterized in that: The step of adjusting the choke opening and the gas injection timing in the gas well according to the optimal exhaust scheme includes: generating a pulse instruction sequence according to the gas injection timing of the optimal exhaust scheme; Converting the pulse instruction sequence into a throttle stepper motor control signal; When the exhaust sequence in the gas well is gas injection, the control signal of the throttle stepper motor is to open to a fully open state; When the exhaust timing of the gas well is intermittent, the control signal of the throttle stepping motor is to keep the opening unchanged.

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