Intelligent control method for recovery and purification of oil well casing gas

Abstract

The invention discloses an intelligent control method for recovery and purification of oil well casing gas, and particularly relates to the technical field of gas purification. Extracting a pollution factor trajectory and a residual energy flow trend, and constructing a pollution-energy path diagram containing a gas coupling relationship and heat value distribution; calculating a load response index based on the path diagram and the operation state of the purification device, and constructing a nonlinear dynamic regulation and control model to recognize the stability state of the system; when the system is in an unstable state, a multi-stage purification device operation sequence is generated according to the pollutant priority, the purification cost function and the path accessibility, and a control instruction is fed back to the field execution unit; dynamically adjusting the weight of the control strategy in combination with historical energy consumption and purification efficiency; if component mutation or coupling instability is detected, generating an emergency purification path and an energy flow buffer strategy based on a map high-order differential vector; according to the method, the pollution identification precision, the control response efficiency and the system adaptive capacity can be remarkably improved, and the method is suitable for intelligent upgrading of a complex well site casing gas treatment system.

Description

Technical Field

[0001] This invention relates to the field of gas purification technology, specifically to an intelligent control method for oil well casing gas recovery and purification. Background Technology

[0002] As a byproduct of oil and gas extraction, casing gas in oil wells has a complex composition and strong volatility. Existing control methods mostly adopt strategies such as fixed threshold triggering and local parameter driving, which cannot respond to dynamic changes in casing gas composition and sudden events, resulting in problems such as low purification efficiency, frequent system overload or false alarms.

[0003] Existing technologies often employ static control methods when dealing with dynamic changes in gas mixtures, which cannot achieve synergistic optimization and regulation of key pollutants (such as hydrogen sulfide, benzene, and nitrogen oxides) and energy recovery pathways, thus limiting their adaptability in complex geological and gas flow unsteady environments.

[0004] Furthermore, there is currently a lack of methods for jointly modeling and intelligently identifying the residual energy flow path of casing gas and the evolution process of pollutants, making it impossible to achieve adjustable, predictable, and visualized linkage control of the casing gas purification process.

[0005] Therefore, there is an urgent need for an intelligent control method with the capabilities of multi-dimensional information fusion, energy flow trajectory analysis, and gas component spectrum identification to solve the problems of identification lag, coarse control, and path rigidity in existing technologies, and to achieve dynamic perception and efficient purification control of the entire process of oil well casing gas. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent control method for the recovery and purification of casing gas in oil wells. By introducing asynchronous multi-point sensing, gas spectrum recognition, path diagram modeling and nonlinear intelligent control technology, high-precision identification and response control of the dynamic changes of multiple components in casing gas are achieved.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control method for oil well casing gas recovery and purification, comprising: Real-time composition data of casing gas in oil wells is acquired, and a sequence of composition changes within an asynchronous time window is constructed using distributed sensors. The component change sequence is input into the gas spectrum recognition model to extract the change trajectory of pollutants and the direction of residual energy flow, and to construct a pollution-energy path map that includes gas coupling relationship, calorific value distribution and flow path. Based on the pollution-energy path diagram, a load response index is constructed, and combined with the operating status of the purification unit, a nonlinear dynamic control model is constructed to identify whether the purification system is in a steady state. When in an unstable state, a multi-level operation sequence of the purification device is dynamically generated based on pollutant priority, purification cost function and path accessibility. The multi-level operation sequence is fed back to the field execution unit as control commands, and the control strategy weights are dynamically adjusted based on historical energy consumption and purification efficiency. When a component mutation event or gas coupling instability is detected, a high-order difference vector between the current spectrum and the stable reference spectrum is calculated based on the mutation spectrum difference model, and an emergency purification path and energy flow buffer strategy are dynamically generated.

[0008] Preferably, constructing a pollution-energy pathway map includes: Using a graph modeling method based on Bayesian structure learning, a coupling relationship network of concentration correlation and covariation characteristics is constructed with each gas component as a node; By combining the unit calorific value of each component with actual flow data, the path edge weights are quantified using an energy weighting algorithm to generate a calorific value flow direction map. Construct a pollution trajectory function from the concentration change rate and its first derivative; The coupling relationship diagram, energy distribution diagram, and pollution trajectory diagram are merged to generate a pollution-energy path diagram.

[0009] Preferably, the load response index constructed based on the pollution-energy path diagram includes: For each pollution pathway in the path diagram, the product of component concentration, unit calorific value, and treatment cost is calculated to form a path load intensity index; Collect the real-time operating status of each purification device, including load rate, energy efficiency coefficient and remaining treatment margin, and normalize it to express it as a state factor; By inputting the path load and device status into the load response index model, the load response index is constructed.

[0010] Preferably, if the load response index rises continuously and exceeds 1.2, and there is a highly fluctuating pollution path in the purification path, it is determined to be a high load instability state. If the load response index fluctuates between 0.8 and 1, and the equipment is operating well, it is considered to be in a steady state. If the load response index is below 0.5, the low load merging mode is triggered.

[0011] Preferably, the multi-stage operation sequence of the dynamically combined purification device includes: A pollutant priority index is constructed based on the pollutant's toxicity level, concentration change rate, and historical fluctuation risk score. Calculate the purification cost function for each purification device based on the current load, response delay, and unit processing energy consumption. By combining the accessibility of the pollution-energy pathway with the availability of the equipment, a pollution-equipment mapping matrix is ​​formed. By using a multi-objective linear programming algorithm, the minimum total cost path combination is generated, and the operation sequence of the multi-level purification device is output in order of priority.

[0012] Preferably, the weights of the control strategy dynamically adjusted based on historical energy consumption and purification efficiency include: The operating sequence is encoded into a standardized set of control instructions according to device type and start-up level, and then sent to the corresponding device controller via industrial communication protocol; Real-time collection of execution feedback from each device, including operating status, energy consumption data, and pollution removal efficiency indicators, and storage in a historical performance database; A device performance scoring model was constructed, and the weight of its control strategy was evaluated by combining the unit purification energy consumption and purification efficiency fluctuations.

[0013] Preferably, the dynamic generation of emergency purification paths and energy flow buffering strategies based on the mutation map differential model includes: constructing the pollution-energy path map at the current moment and extracting the reference map under the stable operation phase to construct a map comparison benchmark; performing first-order and second-order differential analysis on the node and coupling edge attributes of each component in the current map to form a high-order differential vector set; based on the impact range of the mutation path, selecting backup treatment units in the device library and reorganizing the shortest risk avoidance path to form an emergency purification sequence; and simultaneously planning an energy flow buffering strategy to guide high-calorific-value pollution to low-load equipment or cold standby treatment modules.

[0014] The beneficial effects of this invention are: 1. This invention achieves high-precision identification and response control of dynamic changes in multi-component gas in the casing by introducing asynchronous multi-point sensing, gas spectrum recognition, path diagram modeling, and nonlinear intelligent control technologies. Compared with traditional setpoint control and static process configuration, this method can accurately capture sudden changes in pollution sources, quickly generate the optimal purification path, and achieve energy consumption-efficiency synergistic optimization, significantly improving the stability and purification efficiency of the system.

[0015] 2. This invention, through differential analysis of mutation maps and a historical feedback learning mechanism, endows the system with adaptive evolutionary capabilities, enabling rapid reconfiguration of control strategies and emergency response under complex operating conditions and sudden events. While ensuring equipment safety, reducing energy waste, and improving the flexibility of pollution control, this technology also provides a new path for the green recovery and intelligent utilization of associated oil and gas resources, possessing broad engineering application prospects and industry promotion value. Attached Figure Description

[0016] Figure 1 This is a mind map of the intelligent control method for oil well casing gas recovery and purification according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, the intelligent control method for oil well casing gas recovery and purification mentioned in this invention includes: Real-time composition data of casing gas in oil wells is acquired, and a sequence of composition changes within an asynchronous time window is constructed using distributed sensors. The component change sequence is input into the gas spectrum recognition model to extract the change trajectory of pollutants and the direction of residual energy flow, and to construct a pollution-energy path map that includes gas coupling relationship, calorific value distribution and flow path. Based on the pollution-energy path diagram, a load response index is constructed, and combined with the operating status of the purification unit, a nonlinear dynamic control model is constructed to identify whether the purification system is in a steady state. When in an unstable state, a multi-level operation sequence of the purification device is dynamically generated based on pollutant priority, purification cost function and path accessibility. The multi-level operation sequence is fed back to the field execution unit as control commands, and the control strategy weights are dynamically adjusted based on historical energy consumption and purification efficiency. When a component mutation event or gas coupling instability is detected, a high-order difference vector between the current spectrum and the stable reference spectrum is calculated based on the mutation spectrum difference model, and an emergency purification path and energy flow buffer strategy are dynamically generated.

[0019] To address the problems of component monitoring delay, low information resolution, and lag in identifying abnormal changes in existing oil well casing gas recovery processes, this invention proposes a real-time component monitoring method based on distributed sensing and asynchronous time window mechanism. This method can construct a complete gas component change sequence and support subsequent spectrum modeling and intelligent control.

[0020] The key to this invention lies in achieving multi-component, asynchronous, and high-frequency dynamic sensing of oil well casing gas through a high-precision gas sensor network deployed at multiple points, and constructing a data sequence with a three-dimensional "component-time-space" structure to capture transient changes, fluctuation patterns, and sudden events in gas composition, thereby providing a real-time data foundation for subsequent purification path optimization and control strategy reconstruction.

[0021] At key nodes of the oil well casing gas recovery system (including the gas inlet, before condensation separation, compressor inlet, and front end of the purification equipment), distributed sensor units with gas identification capabilities (optional models include TDLAS, MOS, or NDIR industrial sensors) are deployed. Each unit detects the following parameters via a local microcontroller: Gas volume fractions: methane (CH4), ethane (C2H6), hydrogen sulfide (H2S), carbon dioxide (CO2), benzene (C6H6), etc. Temperature and pressure: used to correct gas volume and concentration calculations; Detection frequency: Set the sampling period from 1 to 5 seconds according to the location, and allow dynamic adjustment.

[0022] Each sensor node operates asynchronously, which avoids data congestion caused by unified sampling and allows for adaptive frequency optimization based on local fluctuation characteristics, significantly improving system response speed and data validity.

[0023] The raw data output from the sensor is preprocessed (including Kalman filtering and sample repair) and then aggregated to the edge server according to timestamps. A data reconstruction algorithm is then used to construct a component change sequence in the following form: S i (t)={C i1 (t1),C i2 (t2),…,C in (t n )};where S i (t) represents the concentration sequence of the i-th gas at different time points, C ij Let t be the concentration value at the j-th sampling point. n Its timestamp.

[0024] The aforementioned component change sequence is fed into the central control system or edge AI module for subsequent pollution path identification, energy flow modeling, and control decisions.

[0025] This invention further proposes a spectrum recognition and coupled path modeling method based on component change sequence. In response to the problems of complex multi-component interactions, difficulty in assessing calorific value utilization potential, and uncontrollable pollution evolution process in oil well casing gas, a pollution-energy path map construction mechanism is designed.

[0026] This mechanism integrates four types of factors: component concentration, gas coupling relationship, residual calorific value, and flow trend, forming for the first time a visualized, quantifiable, and predictable pollution energy flow synergy map structure, providing core support for purification path scheduling and control optimization.

[0027] Based on the aforementioned constructed component change sequence S i (t) This invention employs a graph recognition algorithm to perform high-dimensional modeling. Specifically, it includes the following steps: Pollution trajectory identification: Based on the concentration change rate, fluctuation period, and abrupt change location of various pollutants (such as H2S, CO2, benzene, etc.) in the time series, a trajectory function P is constructed. k (t). Behavioral characteristics such as concentration ramp-up, decay, and stable range were extracted through first-order and second-order derivative analysis.

[0028] The construction of the pollution trajectory map specifically includes: analyzing the time-varying rate of change (i.e., the first derivative) and fluctuation frequency of the component concentration sequence, combined with mutation point detection algorithms (such as Z-score or CUSUM), to determine the behavioral trajectory nodes of the pollutants. Each node contains attributes such as pollutant type, concentration increase / decrease trend, and mutation occurrence time, forming a sequence graph structure for use as input for path graph modeling.

[0029] Energy flow direction extraction: Using the gas calorific value database (e.g., methane 55.5 MJ / kg, ethane 51.9 MJ / kg, etc.) and actual flow data, the instantaneous thermal energy contribution of each type of gas is calculated. Combined with the system fluid dynamics model, the flow direction and distribution intensity of each energy component under different paths are mapped. Specifically, using the energy flow distribution model, based on the gas calorific value database and real-time flow velocity data, the momentum-heat coupling calculation method is used to calculate the residual energy flow transfer direction and heat load value on different paths, and the path graph is embedded in the directed edge manner.

[0030] Gas coupling relationship modeling: A structure learning method based on correlation coefficient matrix and Bayesian network is adopted to identify the generation, inhibition or synergistic relationship between various gas components. For example, when methane is at high concentration, it is often accompanied by a decrease in CO2 and an increase in H2S. A coupling graph model G=(V,E) is constructed, where V is the gas component node and E is the coupling edge weight.

[0031] Pollution-Energy Path Graph Generation: The three sub-models (pollution trajectory, energy flow direction, and component coupling) are merged to construct a Pollution-Energy Path Graph (PEPG). This graph is a weighted directed graph, where each path contains: Starting point: A source of a high-concentration pollutant component; Path segment: represents the coupling and energy changes during its migration and transformation process in the system; End point: The treatment node or discharge port of the purification device; Edge weights: taking into account parameters such as the rate of change of component concentration, unit calorific value, treatment priority, and cost function.

[0032] The final generated pollution-energy path graph is a weighted directed graph G = (V, E, W), where V represents gas component nodes, E represents the path edge set, and W represents the weight set. The weights include multi-dimensional factors such as pollution intensity, calorific value distribution, and treatment priority. The graph structure is stored in a graph database and updated in real time to control model inference.

[0033] Path visualization and real-time updates: This PEPG can be continuously updated as a graph database and visualized on the central control platform, enabling users to understand the current pollution diffusion and energy recovery paths, and providing graph structure input for subsequent path optimization control.

[0034] To achieve dynamic adaptation and precise control of the oil well casing gas purification system under varying pollution conditions, this invention designs a load response index construction method based on the pollution-energy path diagram, and establishes a nonlinear dynamic control model accordingly for intelligent identification and adjustment of the purification system's operating status.

[0035] This method can comprehensively consider factors such as gas composition fluctuations, calorific load distribution, and device response capabilities to make real-time judgments on whether the system is in a stable operating state, a high-load state, or an unbalanced critical state, thereby providing a basis for subsequent path optimization and control strategy decisions.

[0036] In this invention, the following key variables are obtained through the aforementioned Pollution-Energy Path Map (PEPG): Load weight L for each pollution pathway i Based on the product of the concentration of pollutants, unit calorific value, and treatment cost in the pathway, the load of each pathway can be expressed as: ; where C ij (t) represents the concentration of the j-th component on path i, H ij For its calorific value, D ij To address energy consumption / difficulty, α j is the weighting factor, and n is the total group score.

[0037] Real-time operating status indicator R of the purification unit k This includes the device's start-up and shutdown status, current processing load and load margin, such as the remaining proportion of the desulfurization tower's adsorption capacity, the compressor's load rate, and the heat exchanger's temperature difference efficiency, all of which are uniformly normalized and represented as the [0,1] interval.

[0038] Load response index Λ(t): Defined as a combination of the current load of all pollution paths and the processing capacity of the equipment: ; where C k This represents the rated processing capacity of purification unit k.

[0039] If Λ(t) > 1, it indicates system overload; if Λ(t) < 0.8, it indicates system redundancy; the optimal stable operating range is between 0.8 and 1.

[0040] Establish the following state determination logic: If Λ(t) rises continuously and exceeds 1.2, and there is a highly fluctuating pollution path in the purification path, it is determined to be a high-load instability state. At this time, the system automatically calls the graph difference analysis module to extract abnormal path segments, and combines the device redundancy capacity and priority scoring function to quickly screen the shortest risk mitigation path through the constraint optimization model and form an emergency operation instruction. If Λ(t) fluctuates between 0.8 and 1, and the equipment is operating well, it is considered to be in a steady state. If Λ(t) remains below 0.5 for an extended period, it indicates a waste of system resources and may trigger a low-load merging mode.

[0041] Model implementation: The control model uses a neural network (such as LSTM or GRU structure) or a fuzzy controller to train stability prediction capability based on historical load response curves, and integrates nonlinear influencing factors such as path priority changes and equipment delay response.

[0042] The model output serves as the input to the path reconstruction and equipment scheduling module, determining the startup sequence, adjustment range, and emergency response method of subsequent purification units.

[0043] Table 1 Comparison of dynamic changes in the system load response index Λ(t)

[0044] As shown in the table, traditional control causes Λ(t) to rise significantly after a disturbance, and the system is prone to entering a high-load state, while intelligent control can quickly stabilize.

[0045] To address the issues of delayed response and rigid control paths in traditional casing gas purification systems under load fluctuations or sudden pollution changes, this invention proposes a dynamic purification path combination method based on pollution priority, purification cost function, and path reachability analysis to achieve optimized scheduling and operation sequence reconstruction of multi-stage purification devices.

[0046] Using this method, when the system is identified as being in an unstable state, it can quickly assess the severity of pollutants and the current load on the purification device, and generate a device operation sequence that is highly targeted, rationally allocated with resources, and minimizes path risks, thereby effectively improving the response efficiency and stability of the purification system in complex environments.

[0047] When the system is determined to be in an unstable state (such as high load, component mutation, etc.), the control module is automatically triggered to execute the following dynamic path generation process: Pollutant Prioritization: Based on the toxicity level, emission limits, economic value, and current concentration trends of each pollutant component, pollutant P is assigned a priority level. i A priority rating π i Priority rating example: H2S: highly toxic, score 9; CO2: strong greenhouse effect, score 7; methane: combustible gas with energy recovery value, score 6.

[0048] Purification cost function calculation f j Each purification device D j The difficulty of treating the target pollutant is expressed as a cost function, which includes: f j =E j +τ j +δ j E j Energy consumption per unit of gas purification; τ j To handle delays; δ j It is the reciprocal of the current load margin of the device (the higher the load, the greater the cost).

[0049] Using the path accessibility matrix A in the pollution-energy pathway diagram ij , indicating pollutant P i Can it be purified by device D? j The process involves constructing a set of feasible paths based on the current operating status of the equipment (faults, maintenance, etc.).

[0050] Construction of a multi-objective optimization scheduling model: Taking into account pollutant priority, purification cost, and path accessibility, the following optimization objective function is established: The optimal pollutant-device matching combination is obtained by solving the problem using a greedy algorithm, a genetic algorithm, or integer linear programming.

[0051] Multi-level operation sequence generation: The above combination is transformed into an equipment execution sequence, that is, the following order is determined according to the urgency of pollution treatment, energy efficiency, and process connectivity: Level 1 start-up equipment (immediate response, handling the highest priority pollutants); Level 2 standby equipment (minor contaminants or backup routes); Level 3 suppression device (temporarily suspended to avoid system load).

[0052] The final execution sequence is uploaded to the central control system and distributed to the controllers of each field device, enabling / adjusting the parameters of the corresponding devices, and continuously feeding back information such as device status and processing effect during the execution process for adaptive optimization by the model.

[0053] To achieve efficient closed-loop control of the oil well casing gas purification process, this invention designs a multi-level operation sequence feedback control mechanism. By combining the real-time response of the field equipment and historical operation data, the weight parameters in the control strategy are dynamically adjusted, enabling the system to adaptively optimize during continuous operation and possess learning and energy efficiency adaptability.

[0054] This mechanism can dynamically evolve control weights based on historical energy consumption performance and purification efficiency, enabling the device operation strategy to iteratively converge from the initial setting to a long-term optimized state, thereby improving energy utilization and pollution treatment effectiveness.

[0055] Once the optimal multi-level operating sequence is generated, the control system performs the following operations: Control command formatting: The equipment operation sequence and operating parameters (such as flow setpoint, temperature threshold, and pressure regulation curve) are encoded into a standard control command set and sent to each field execution unit via an industrial fieldbus (such as Modbus, Profibus, or OPC UA).

[0056] Execution unit response mechanism: Each purification unit controller starts / adjusts its own operating status according to the received instructions and provides feedback including: start-up success / failure status; actual operating power; current processing flow rate and concentration changes, etc. Energy consumption and purification efficiency monitoring: The system collects the following historical operating metrics: Energy consumption index E j (t): Power or heat consumption of the device per unit time; Purification efficiency η j (t): Purification ratio calculated from the comparison of component concentrations before and after input; operational stability coefficient σ j (t): Data such as frequency of operation interruption and intensity of fluctuation.

[0057] Control strategy weight adjustment: The cost function weights corresponding to each device within the control model are dynamically updated according to the following evolution mechanism: In the formula, Indicates the initial reference energy consumption; β1 and β2 represent the initial expected purification efficiency; w represents the empirical weighting adjustment factors. j (t+1) represents the updated cost function weights, w j (t) represents the weights of the cost function before the update.

[0058] If a device has consistently low energy efficiency and reduced purification efficiency, its corresponding strategy weight will be reduced, thus it will be preferentially excluded in subsequent multi-level path combinations.

[0059] The control system periodically performs strategy reassessment, combining the energy consumption and purification performance of all devices within the historical operating window, and dynamically iteratively adjusts path preferences and startup priorities to form a self-evolving strategy set with environmental adaptability and system learning capabilities.

[0060] Table 2 Comparison of purification efficiency and unit energy consumption (taking H2S as an example)

[0061] As shown in the table, the intelligent control method significantly reduces unit energy consumption while improving purification efficiency, demonstrating a synergistic optimization effect.

[0062] To address unsteady-state operating conditions such as sudden changes in contaminant composition and breakage of coupling relationships in oil well casing gas components under conditions of sudden geological disturbances, equipment malfunctions, or multi-well coupled production, this invention proposes an emergency purification path generation method based on differential analysis of mutation maps. By comparing the multi-level differences between the current contamination map and the preset stable operating condition map, sudden characteristic changes are extracted, and adaptive purification paths and energy flow buffering strategies are quickly derived.

[0063] In actual operation, the emergency response mechanism will be triggered when the system identifies any of the following abnormal states through the aforementioned modules: The rate of increase of single component concentration exceeds the set threshold per unit time (e.g., H2S increase exceeds 50 ppm / min). The correlation coefficient between any two highly coupled gases (such as CH4 and CO2) falls below the critical value (e.g., r < 0.3). Purification efficiency drops by more than 20% for three consecutive cycles, or energy consumption / load increases abnormally.

[0064] Based on real-time component change sequences, a pollution-energy pathway graph Gnow(t) is constructed at the current moment to characterize the latest changes in pollution factors and energy flow pathways, including: Node set: pollutant components and their concentration status; Edge set: Coupling relationships (orientation, strength) between components; Edge weights: indicators such as unit path heat load and cost.

[0065] Historical map data Gref from a steady-state system is selected as the normal baseline state and updated using a sliding window mechanism to ensure adaptability to different well conditions.

[0066] Define the difference operator Δ n (G) Perform first-order and second-order differences on node attributes (concentration, calorific value) and edge attributes (coupling strength, heat flow path) respectively to construct a higher-order difference vector set: ;in: ; Eij V represents the edge attributes between nodes, indicating the coupling relationship between components; i The attribute vector represents the i-th node (gas component) in the spectrum; the median is used for dynamic transition filtering; the magnitude and direction of the difference vector are used to identify the abrupt change path and coupling break path of the pollution source.

[0067] Based on the high-order difference results, path segments that significantly deviate from the conventional pattern are identified, and a "mutation impact map" is constructed to mark potential high-load segments, unstable segments, and propagation paths.

[0068] Based on the impact diagram of the sudden change, purification devices with backup capacity are scheduled from the equipment pool to form the shortest risk mitigation path and the lowest cost energy flow buffer strategy. Key technologies include: Path selection prioritizes excluding cleanup sequences corresponding to mutation edge sets; If the calorific value concentration is too high, the path will be automatically split and guided to the parallel processing module; Activate some redundant equipment (such as backup desulfurization towers and pressure regulating devices) to enter "rapid response state".

[0069] All emergency response paths will be synchronously written back to the control graph library as data samples for subsequent model training and stability enhancement, supporting the model's self-evolutionary reinforcement learning.

[0070] Table 3 Comparison of Experimental Results of System Purification Control Methods

[0071] The table shows: Pollutant identification accuracy: based on the test comparison results of known samples; Response delay time: the time from the occurrence of pollution mutation to the triggering of control commands; Purification efficiency: measured by the removal rate of typical pollutants (such as H2S); Unit energy consumption: the electrical or thermal energy consumed to purify a unit of pollutant; System stability score: assessed based on the fluctuation of the system load response index; Emergency response success rate: the percentage of cases that recover to a stable state under emergency conditions.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent control of gas recovery and purification in oil well casing, characterized in that: The method comprises the following steps: acquiring real-time component data of oil well casing gas, and constructing a component change sequence within an asynchronous time window through a distributed sensor; inputting the component change sequence into a gas spectrum identification model, extracting a change trajectory of a pollution factor and a residual energy flow direction, and constructing a pollution-energy path graph containing a gas coupling relationship, a heat value distribution, and a flow direction path; based on the pollution-energy path graph, constructing a load response index, and combining an operating state of a purification unit to construct a nonlinear dynamic control model for identifying whether the purification system is in a stable state; when it is not in a stable state, dynamically combining a multi-level operation sequence of the purification device according to a pollution priority, a purification cost function, and path accessibility; feeding the multi-level operation sequence as a control instruction to an on-site execution unit, and dynamically adjusting a control strategy weight based on historical energy consumption and purification efficiency; when a component mutation event or a gas coupling relationship instability is detected, calculating a high-order difference vector between a current spectrum and a stable reference spectrum based on a mutation spectrum difference model, and dynamically generating an emergency purification path and an energy flow buffering strategy.

2. The intelligent control method for oil well casing gas recovery and purification according to claim 1, characterized in that: The pollution-energy path graph is constructed by: using a graph modeling method based on Bayesian structure learning to construct a coupling relationship network of concentration correlation and covariation characteristics of each gas component as a node; combining unit heat values and actual flow data of each component to quantize path edge weights through an energy weighting algorithm to generate a heat value flow direction graph; constructing a pollution trajectory function for the concentration change rate and its first derivative; fusing the coupling relationship graph, the energy distribution graph, and the pollution trajectory graph to generate the pollution-energy path graph.

3. The intelligent control method for oil well casing gas recovery and purification according to claim 1, characterized in that: The load response index is constructed based on the pollution-energy path graph by: calculating the product of component concentration, unit heat value, and processing cost for each pollution path in the path graph to form a path load intensity index; collecting real-time operating states of each purification device, including load rate, energy efficiency coefficient, and residual processing margin, and normalizing them into state factors; inputting the path load and device state into a load response index model to construct the load response index.

4. The intelligent control method for oil well casing gas recovery and purification according to claim 3, characterized in that: If the load response index continuously rises and breaks through 1.2, and there is a high fluctuation pollution path in the purification path, it is determined to be a high load instability state; if the load response index fluctuates between 0.8 and 1, and the equipment operating state is good, it is determined to be a stable state; if the load response index is lower than 0.5, a low load merging mode is triggered.

5. The intelligent control method for oil well casing gas recovery and purification according to claim 1, characterized in that: The multi-level operation sequence of the purification device is dynamically combined by: constructing a pollution priority index based on the toxicity grade, concentration change rate, and historical fluctuation risk score of the pollution; calculating a purification cost function for each purification device according to the current load, response delay, and unit processing energy consumption; combining the path accessibility in the pollution-energy path graph and the available state of the device to filter and form a pollution-device mapping matrix; generating a minimum total cost path combination through a multi-objective linear programming algorithm, and outputting a multi-level purification device operation sequence layered by priority.

6. The intelligent control method for oil well casing gas recovery and purification according to claim 1, characterized in that: The control strategy weight is dynamically adjusted based on historical energy consumption and purification efficiency by: encoding the operation sequence into a standardized control instruction set according to the device type and start-up level, and sending it to the corresponding device controller through an industrial communication protocol; Real-time acquisition of each device's execution feedback, including running status, energy consumption data and pollution removal efficiency indicators, and stored in the historical performance database; Constructing device performance scoring model, combining with unit purification energy consumption and purification efficiency fluctuation, to evaluate its control strategy weight.

7. The intelligent control method for oil well casing gas recovery and purification according to claim 1, characterized in that: Based on the differential model of mutation map, dynamic generation of emergency purification path and energy flow buffer strategy includes: constructing the pollution-energy path map at the current time, and extracting the reference map under the stable running stage to build the map comparison benchmark; performing first-order and second-order difference analysis on the attributes of each component node and coupled edge in the current map to form a high-order difference vector set; based on the influence range of the mutation path, screening the standby processing unit in the device library, and reorganizing the shortest risk avoidance path to form the emergency purification sequence; at the same time, planning the energy flow buffer strategy to guide the high-calorific value pollution to the low-load equipment or cold standby processing module.

Images