Gas well production management method and management system based on internet of things

Abstract

This invention discloses an IoT-based gas well production management method and system. The method includes connecting to an IoT sensor network to collect pressure, temperature, flow rate, and gas composition information of the target gas well; then constructing a multi-dimensional data fusion model based on the acquired data to evaluate the dynamic changes in gas well production; furthermore, selecting production equipment that meets preset standards based on the gas composition information, generating an equipment resource set, and generating a set of operators corresponding to each piece of equipment; finally, performing dynamic optimization analysis on the multi-dimensional data fusion model based on the equipment resource set and the operator set, and outputting production management strategies and optimization decision results. The system includes a data acquisition module, a model construction module, an equipment management module, a personnel management module, and an optimization analysis module. This invention solves the technical problem of the inability to achieve accurate gas well production management in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of gas well production management technology, specifically relating to a gas well production management method and management system based on the Internet of Things. Background Technology

[0002] In the gas well production process, the rationality and accuracy of production management directly determine development efficiency, equipment safety, and operating costs. Effective production management requires dynamic adaptation of equipment to real-time operating conditions, precise matching of operators' needs with equipment requirements, and rapid adjustment of strategies based on production changes. This relies on the effective utilization of gas well operating data and the systematic support of management processes.

[0003] However, existing gas well production management methods have the following significant limitations:

[0004] 1. Insufficient equipment and operating condition adaptability: The selection and scheduling of production equipment (such as compressors, separators, etc.) often rely on experience-based judgments without taking into account key data such as real-time pressure, temperature, and gas composition (such as sulfur content and methane concentration) of the gas well for targeted analysis. This often results in the equipment's pressure resistance, temperature resistance, or gas compatibility not matching the actual operating conditions, leading to increased energy consumption, accelerated equipment wear and tear, and even safety risks.

[0005] 2. Inefficient matching of personnel and equipment: The lack of a standardized mechanism for matching the professional skill level and operating qualifications of operators with equipment requirements can easily lead to resource mismatches such as "highly skilled personnel being idle" or "lowly qualified personnel operating complex equipment", which not only reduces production efficiency but may also cause production accidents due to improper operation.

[0006] 3. Lagging dynamic adjustment capability: In the face of changes in operating conditions during gas well production (such as flow fluctuations and sudden pressure changes), existing management methods lack a dynamic optimization mechanism based on real-time data and rely more on lagging adjustments after manual inspections. This makes it difficult to quickly output suitable production strategies, resulting in management response lagging behind actual needs.

[0007] While some existing technologies offer assessment methods related to gas well production, these methods often focus on single-parameter analysis and lack deep integration with production management processes. For example, the prior art disclosed in CN111191188B mentions a method for determining production time based on salinity. This method determines the peak production time of shale gas wells by monitoring changes in the salinity of the flowback fluid, thus providing a basis for assessing gas well productivity and fracturing effectiveness. However, it fails to provide effective support for management aspects such as equipment allocation and personnel scheduling, ultimately hindering the achievement of precise and efficient gas well production management.

[0008] Therefore, there is an urgent need for a technical solution that can integrate multi-dimensional data, achieve dynamic adaptation of equipment and personnel, and optimize production strategies in real time, in order to solve the problems of insufficient accuracy and efficiency in the existing gas well production management. Summary of the Invention

[0009] The purpose of this invention is to provide a gas well production management method based on the Internet of Things, which solves the technical problem that existing technologies cannot achieve accurate gas well production management.

[0010] Another objective of this invention is to provide an Internet of Things-based gas well production management system.

[0011] The first technical solution adopted in this invention is a gas well production management method based on the Internet of Things, comprising the following steps:

[0012] Step 1: Connect to the Internet of Things (IoT) sensor network to collect pressure, temperature, flow rate, and gas composition information of the target gas well;

[0013] Step 2: Construct a multi-dimensional data fusion model based on the data information obtained in Step 1 to assess the dynamic changes in gas well production;

[0014] Step 3: Based on the gas composition information, select production equipment that meets the preset standards for compatibility and generate a set of equipment resources;

[0015] Step 4: Match the operators' professional skill level, work experience, and equipment operation qualifications to generate a set of operators for each piece of equipment;

[0016] Step 5: Based on the set of equipment resources and the set of operators, perform dynamic optimization analysis on the multi-dimensional data fusion model, and output production management strategies and optimization decision results.

[0017] The first technical solution of the present invention is further characterized in that,

[0018] After obtaining the pressure, temperature, flow rate, and gas composition information of the target gas well in step 1, the following steps are also included:

[0019] Edge computing nodes are used to perform preliminary processing on the collected raw data, removing outliers and supplementing missing data; data collected by different types of sensors are synchronously calibrated, and a unified timestamp is generated through data fusion algorithms.

[0020] The multidimensional data fusion model constructed in step 2 has a safety threshold set. The safety threshold is based on historical data of the pressure, temperature, flow rate and gas composition information of the target gas well during the stable production stage. The normal fluctuation range of each parameter is determined through statistical analysis and set in combination with the standard limit value for safe operation of the gas well.

[0021] If the evaluation metrics of the multidimensional data fusion model exceed the safety threshold, the multidimensional data fusion model will send an alarm message to the designated terminal. The alarm message includes the current production data, the type of anomaly, and the scope of impact.

[0022] After sending the alarm information to the designated terminal, it also includes:

[0023] Receive feedback instructions from the designated terminal, execute the feedback instructions, and store the feedback instructions and corresponding production data in the database;

[0024] When the same type of anomaly is detected again, the stored feedback instructions are automatically recalled as a reference; and a warning signal is issued to on-site personnel through an audible and visual alarm device, prompting them to take appropriate measures.

[0025] If the production data received by the processor comes from a remote control request from a specified terminal, the remote control instructions are executed directly.

[0026] Step 3 specifically involves extracting the sulfur content, corrosive component concentration, and flammable and explosive component concentration from the gas composition as key parameters; presetting the corrosion resistance level, explosion-proof level, and material compatibility threshold of the production equipment; calculating the matching degree between each production equipment and the key parameters using a weighted scoring method; and selecting equipment with a matching degree not lower than the preset threshold as compatible equipment.

[0027] Step 5 involves dynamic optimization analysis of the multidimensional data fusion model. Specifically, a distributed computing architecture is adopted, where the computing task is decomposed into multiple sub-tasks, which are processed in parallel by different computing nodes. Finally, the results are aggregated to generate production management strategies and optimization decisions.

[0028] The production management strategy includes dynamically allocating equipment to different gas wells based on real-time equipment status and production task priorities, and matching suitable operators to each piece of equipment based on the professional skill level and work experience of the operators.

[0029] The optimization decision-making results include real-time analysis of gas well pressure, temperature, and flow rate through a distributed computing architecture, combined with equipment performance curves and historical production data, to generate equipment parameter optimization schemes, and to reallocate energy and materials based on dynamic changes in gas well production.

[0030] The second technical solution adopted in this invention is an Internet of Things-based gas well production management system, comprising:

[0031] The data acquisition module is used to connect to the Internet of Things sensor network and call the gas well monitoring module to obtain data parameters of the target gas well;

[0032] The model building module constructs a multi-dimensional data fusion model based on the acquired data information;

[0033] The equipment management module is used to perform compatibility analysis based on gas composition information and generate a set of equipment resources.

[0034] The personnel management module is used to match the professional skill level, work experience and equipment operation qualifications of operators to generate a set of operators corresponding to each piece of equipment.

[0035] The optimization analysis module, based on the set of equipment resources and the set of operators, performs dynamic optimization analysis on the multi-dimensional data fusion model and outputs production management strategies and optimization decision results.

[0036] The second technical solution of the present invention is further characterized in that,

[0037] The data acquisition module, model building module, equipment management module, personnel management module, and optimization analysis module are connected via an industrial-grade communication bus; and all of these modules are connected to the processor via signals.

[0038] The beneficial effects of this invention are:

[0039] This invention integrates multi-dimensional data, including pressure, temperature, flow rate, and gas composition, collected by IoT sensors, to establish a fusion model for comprehensive assessment of gas well production changes. This effectively overcomes the limitations of traditional methods that rely on single parameters, leading to incomplete assessments. Furthermore, by combining equipment compatibility analysis of gas composition with operator access control, a dynamic optimization mechanism is formed. This mechanism accurately schedules suitable equipment and personnel, addressing pain points such as resource mismatch and delayed response in production management. Additionally, alarms and audible / visual warnings when comprehensive assessment indicators exceed thresholds quickly trigger countermeasures, significantly improving the real-time performance and intelligence of gas well production management. Overall, the method and system of this invention simultaneously improve upon the problems of reliance on single parameters, insufficient real-time performance, and low intelligence. While increasing production efficiency, it also enhances operational safety, forming a closed-loop management advantage from data perception to decision execution. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the gas well production management method based on the Internet of Things of this invention.

[0041] Figure 2 This is a structural block diagram of the gas well production management system based on the Internet of Things of this invention.

[0042] In the diagram, 1. Data acquisition module, 2. Model building module, 3. Equipment management module, 4. Personnel management module, and 5. Optimization analysis module. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] Example 1

[0045] This invention relates to an IoT-based gas well production management method, executed by a processor. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0046] like Figure 1 As shown, it includes the following steps:

[0047] Step 1: Connect to the Internet of Things (IoT) sensor network to collect pressure, temperature, flow rate, and gas composition information of the target gas well;

[0048] Step 2: Construct a multi-dimensional data fusion model based on the data information obtained in Step 1 to assess the dynamic changes in gas well production;

[0049] Step 3: Based on the gas composition information, select production equipment that meets the preset standards for compatibility and generate a set of equipment resources;

[0050] Step 4: Match the operators' professional skill level, work experience, and equipment operation qualifications to generate a set of operators for each piece of equipment;

[0051] Step 5: Based on the set of equipment resources and the set of operators, perform dynamic optimization analysis on the multi-dimensional data fusion model, and output production management strategies and optimization decision results.

[0052] Furthermore, the production management strategy includes dynamically allocating equipment to different gas wells based on real-time equipment status and production task priorities. For example, processing tasks for high-sulfur gas wells are prioritized for corrosion-resistant equipment, and equipment operating parameters are monitored in real time using IoT sensors to ensure production continuity. Additionally, suitable operators are matched to each piece of equipment based on their professional skill level and work experience; for example, maintenance tasks for hydrogen sulfide-containing gas wells are assigned to personnel holding special operation certificates and having handled similar conditions, and personnel scheduling information is updated in real time through an access control system.

[0053] The optimization decision-making process involves real-time analysis of gas well pressure, temperature, and flow rate using a distributed computing architecture. This analysis, combined with equipment performance curves and historical production data, generates optimized equipment parameter plans. For example, when a gas well's production declines, it's recommended to adjust the compressor speed from 1500 rpm to 1800 rpm and simultaneously adjust the cooling system power to maintain equipment stability. Furthermore, energy and materials are reallocated based on dynamic changes in gas well production. For instance, when a gas well enters a stable production phase, 30% of the power resources originally allocated to that well are redirected to higher-yielding wells, with the resource allocation effect monitored in real-time via edge computing nodes.

[0054] Example 2

[0055] Based on Embodiment 1 above, this embodiment further includes, after obtaining the pressure, temperature, flow rate, and gas composition information of the target gas well in step 1, the following:

[0056] Edge computing nodes are used to perform preliminary processing on the collected raw data, removing outliers and supplementing missing data; data collected by different types of sensors are synchronously calibrated, and a unified timestamp is generated through data fusion algorithms.

[0057] Specifically, in the outlier removal process, the edge computing node first invokes preset anomaly detection rules, which are based on the target gas well's historical stable production data and industry-recognized reasonable parameter ranges. For example, for pressure values, the system first calculates the pressure fluctuation range of the gas well during normal production (e.g., 1.2-3.5 MPa), while setting physical constraints (pressure values ​​must not be negative); for temperature values, it refers to the normal operating temperature range of the wellhead equipment (e.g., -20-80℃) and the physical laws of gas throttling and cooling; the flow rate must meet the design upper limit of the pipeline's transport capacity (e.g., not exceeding 5000 m³ / h), and the sum of the concentrations of all components in the gas composition must be 100% (e.g., methane concentration + hydrogen sulfide concentration + carbon dioxide concentration + other component concentrations = 100%).

[0058] In actual processing, the edge computing nodes will verify the raw data collected in real time point by point: if the pressure value of a certain sampling point suddenly drops to -0.5MPa (violating the non-negativity constraint), or the temperature value jumps to 150℃ (far exceeding the equipment's tolerance range), or the total concentration of gas components is 110% (logical contradiction), it will be directly judged as an outlier and removed; for fluctuation data that is close to but does not exceed the reasonable range (such as the pressure rising to 3.6MPa for a short time), the system will combine the historical data in the sliding window (such as the average pressure over the past 10 minutes being 2.8MPa) to perform trend analysis. If the fluctuation deviates significantly from the overall trend (such as deviating from the average by more than 20%), and there is no corresponding operating condition adjustment instruction (such as pressure changes caused by manual valve control), it will also be judged as an outlier and removed, ensuring that the retained data conforms to the physical logic and historical laws of gas well production.

[0059] When supplementing missing data, edge computing nodes select an appropriate supplementation method based on the duration and data type of the missing data. For short-term missing data (such as 1-2 sampling cycles of data missing due to momentary sensor communication interruption), the system will perform interpolation supplementation based on the effective data trend before and after the missing data. For example, if the temperature value is missing at a certain moment, and the temperature at the previous moment is 35℃ and the temperature at the next moment is 37℃, with an interval of 10 seconds between the two moments (sampling cycle of 5 seconds), the temperature value at the missing moment will be supplemented to 36℃ according to a linear trend to ensure data continuity. For long-term missing data (such as data missing for more than 5 minutes due to sensor failure), the system will call historical data of similar operating conditions in the database (such as the pressure change curve of the same gas well in the same production stage of the previous week, and the flow data of the same type of gas well at similar production levels), and fill it in after correction by combining it with the current real-time operating conditions (such as the deviation rate between the current production and historical similar operating conditions), and mark "historical reference supplement" in the data to avoid gaps in subsequent analysis due to missing data.

[0060] In the data synchronization and calibration phase, edge computing nodes need to address the issues of time deviation and inconsistent sampling frequencies caused by differences in hardware characteristics among different types of sensors. Firstly, regarding time deviation, the system uses the local high-precision clock of the edge computing node (with an error controlled within ±1ms) as a benchmark to calibrate the sampling times of devices such as pressure sensors, temperature sensors, flow meters, and gas composition analyzers. For example, if the sampling time of the pressure sensor lags behind the benchmark by 200ms, and the sampling time of the gas composition analyzer is ahead by 150ms, the system will use a timestamp correction algorithm to align the sampling times of all sensors to the benchmark clock, ensuring that the collected parameters at the same physical moment are consistent in the time dimension. Secondly, to address differences in sampling frequencies (e.g., pressure sensors sample 10 times per second, while gas composition analyzers sample once every 30 seconds), the system dynamically interpolates to adjust the sampling frequency, unifying the sampling period of all parameters to 1 second / sample. For high-frequency sampled pressure and temperature data, key feature points are retained, and valid values ​​are extracted at 1-second intervals. For low-frequency sampled gas composition data, supplementary values ​​are uniformly generated based on the changing trend of two consecutive measured values ​​(e.g., hydrogen sulfide concentration slowly increases from 200 ppm to 220 ppm), ensuring that pressure, temperature, flow rate, gas composition, and other data correspond one-to-one on the time axis, avoiding misalignment of multi-source data due to different sampling frequencies.

[0061] Finally, a unified timestamp is generated using a data fusion algorithm. The system integrates the calibrated data in chronological order, generating a unique timestamp for each sampling moment (accurate to the millisecond level), in the format "year-month-day hour:minute:second.millisecond" (e.g., 2025-07-21 10:30:15.234). This timestamp is then associated with and stored with the corresponding pressure, temperature, flow rate, and gas composition information (e.g., hydrogen sulfide concentration 210ppm, methane concentration 95%, carbon dioxide concentration 3%), forming a structured data unit. This unified timestamp ensures strict temporal correspondence between multi-source data, providing consistent and reliable basic data support for subsequent multi-dimensional data fusion models to analyze gas well production changes based on time series, effectively avoiding evaluation biases caused by data asynchrony.

[0062] Example 3

[0063] Based on the above embodiment 2, the multi-dimensional data fusion model constructed in step 2 of this invention has a safety threshold. The safety threshold is based on historical data of the pressure, temperature, flow rate and gas composition information of the target gas well in the stable production stage. The normal fluctuation range of each parameter is determined by statistical analysis and set in combination with the standard limit value for safe operation of the gas well.

[0064] If the evaluation metrics of the multidimensional data fusion model exceed the safety threshold, the multidimensional data fusion model will send an alarm message to the designated terminal. The alarm message includes the current production data, the type of anomaly, and the scope of impact.

[0065] Example 4

[0066] Based on Embodiment 3 above, this embodiment further includes, after sending alarm information to the designated terminal:

[0067] Receive feedback instructions from the designated terminal, execute the feedback instructions, and store the feedback instructions and corresponding production data in the database;

[0068] When the same type of anomaly is detected again, the stored feedback instructions are automatically recalled as a reference; and a warning signal is issued to on-site personnel through an audible and visual alarm device, prompting them to take appropriate measures.

[0069] Furthermore, if the production data received by the processor originates from a remote control request from a designated terminal, the remote control instructions are executed directly, without requiring analysis and evaluation through a multi-dimensional data fusion model.

[0070] Furthermore, the present invention also includes using environmental sensing sensors to determine whether there are abnormal environmental conditions around the target gas well; if abnormal environmental conditions exist, the working mode of the sensor network is adjusted to a high-frequency acquisition mode; if there are no abnormal environmental conditions, the default low-power acquisition mode is maintained.

[0071] Example 5

[0072] Based on Example 4 above, step 3 of this invention specifically involves extracting the sulfur content, corrosive component concentration, and flammable and explosive component concentration from the gas composition as key parameters, such as sulfur content (H2S concentration), gas humidity, corrosive components (such as CO2), and flammability and explosiveness (such as methane concentration). These parameters directly determine the material requirements, sealing standards, corrosion resistance, and safety level of the equipment.

[0073] The corrosion resistance level, explosion-proof level, and material compatibility threshold of the production equipment are preset. For example, corrosion-resistant equipment must meet the compatibility standard of sulfur content ≤500ppm, explosion-proof equipment must be compatible with scenarios with methane concentration ≥90%, and drying equipment must be compatible with working conditions with gas humidity ≤80%. These standards are based on the design parameters of the equipment (material corrosion resistance level, explosion-proof level, processing capacity, etc.).

[0074] Finally, a weighted scoring method is used to calculate the matching degree between each production device and the key parameters, and devices with a matching degree not lower than a preset threshold are selected as suitable devices. For example, if the sulfur content of a gas well is 300 ppm and the sulfur tolerance threshold of a certain device is 500 ppm, then the matching degree of the device in terms of sulfur content parameters is 60%. After combining the matching degree scores of multiple parameters, devices with a total matching degree ≥ the preset threshold (such as 80%) are selected to form a set of equipment resources.

[0075] Furthermore, in step 5, the dynamic optimization analysis of the multidimensional data fusion model specifically involves adopting a distributed computing architecture, where the computing task is decomposed into multiple sub-tasks, which are processed in parallel by different computing nodes, and finally aggregated to generate production management strategies and optimization decision results.

[0076] Specifically, the computational task is first refined by decomposing it into several independent yet interconnected sub-tasks based on the evaluation dimensions of a multi-dimensional data fusion model (equipment adaptability, personnel matching, production parameter stability, etc.). For example, independent sub-tasks are generated for dimensions such as "dynamic evaluation of the adaptability of equipment resource set to real-time gas composition," "efficiency calculation of matching between operator set and equipment requirements," and "influence analysis of fluctuations in parameters such as pressure, temperature, and flow rate on production status." Each sub-task corresponds to a specific evaluation objective. For instance, the equipment sub-task focuses on the decline trend of the operating efficiency of the current equipment resource set under different gas compositions, the personnel sub-task emphasizes the real-time matching changes between operator skills and equipment operation requirements, and the parameter sub-task focuses on the quantitative impact of multi-dimensional parameter co-fluctuations on production stability.

[0077] Subsequently, each subtask was assigned to different computing nodes for parallel processing. These computing nodes were functionally divided (e.g., equipment computing nodes, personnel computing nodes, parameter analysis nodes, etc.), relying on locally stored historical data (e.g., historical equipment failure rates, personnel operation records, parameter fluctuation thresholds) and real-time updated monitoring data (current gas composition, real-time equipment status, personnel on-duty information) to independently complete the subtask calculations. For example, the equipment computing node would call real-time data from the equipment management module to calculate the rate of change in the adaptability of each piece of equipment under the current gas composition (e.g., the rate of decrease in the adaptability of corrosion-resistant equipment when the sulfur content increases); the personnel computing node would combine the permission data from the personnel management module to analyze the dynamic matching efficiency between operators and equipment (e.g., the impact of the arrival time of personnel with emergency operation qualifications on processing efficiency when a piece of equipment suddenly fails); and the parameter analysis node would continuously track the coordinated fluctuations of pressure, temperature, and flow rate, quantifying their impact weight on production efficiency (e.g., the degree of threat to pipeline safety caused by the coupling effect of a sudden increase in pressure and a decrease in flow rate).

[0078] During parallel processing, each computing node exchanges intermediate results in real time via an industrial-grade communication bus to ensure data consistency. For example, when the equipment computing node concludes that "the compatibility of a certain piece of equipment has dropped to a critical value," it will simultaneously push this conclusion to the personnel computing node, triggering a recalculation of personnel matching efficiency and avoiding decision-making biases caused by information lag. Finally, the main computing node summarizes the calculation results of all subtasks and comprehensively evaluates core indicators such as equipment utilization, personnel matching accuracy, and production parameter synergy.

[0079] Example 6

[0080] This invention relates to an IoT-based gas well production management system, such as... Figure 2 As shown, it includes:

[0081] Data acquisition module 1 is used to connect to the Internet of Things sensor network and call the gas well monitoring module to obtain the pressure value, temperature value, flow rate value and gas composition information of the target gas well;

[0082] Model building module 2 is used to obtain the operating condition change nodes of the target gas well, and combine pressure, temperature, flow rate and gas composition information to build a multi-dimensional data fusion model to evaluate the dynamic changes in gas well production.

[0083] Equipment management module 3 is used to perform compatibility analysis based on gas composition information and generate a set of equipment resources;

[0084] Personnel Management Module 4 is used to match the professional skill level, work experience and equipment operation qualifications of operators with permissions, and generate a set of operators corresponding to each piece of equipment in the equipment resource set.

[0085] Optimization Analysis Module 5, based on the set of equipment resources and the set of operators, performs dynamic optimization analysis on the multi-dimensional data fusion model and outputs production management strategies and optimization decision results.

[0086] The data acquisition module 1, model building module 2, equipment management module 3, personnel management module 4, and optimization analysis module 5 are connected via an industrial-grade communication bus; and all of the above modules are connected to the processor via signals.

[0087] The invention will be further explained below in conjunction with specific application scenarios.

[0088] In a large oil and gas field, there are hundreds of target gas wells. Traditional production management methods rely on manual inspections and single-parameter monitoring, which are insufficient to meet the high-efficiency and intelligent requirements of modern gas well production. By deploying the system of this invention, the operating status of gas wells can be comprehensively monitored and resource allocation can be dynamically optimized, thereby significantly improving production efficiency and management level.

[0089] First, various sensor nodes, including pressure sensors, temperature sensors, flow meters, and gas composition analyzers, are installed at key locations of the target gas well. These sensor nodes are positioned near the wellhead, on the outlet pipeline, and at the rear of the separator to ensure that the collected data comprehensively reflects the actual operating status of the gas well. For example, pressure and temperature sensors are installed near the wellhead to monitor pressure and temperature changes within the wellbore; flow meters are located on the outlet pipeline to measure gas flow rate; and the gas composition analyzer is located at the rear of the separator to detect changes in gas composition. Each sensor node transmits the collected pressure, temperature, flow rate, and gas composition information to data acquisition module 1 via a wireless communication protocol. Data acquisition module 1 performs preliminary verification of the received raw data, removing outliers and supplementing missing data. Simultaneously, it performs synchronous calibration on data collected from different types of sensors, generating a unified timestamp to ensure consistency across multiple data sources. This process is completed through edge computing nodes, enabling rapid local data processing and reducing the computational burden on cloud servers.

[0090] Next, the data processed by data acquisition module 1 is transmitted to model building module 2. Model building module 2 establishes a multi-dimensional data fusion model based on the target gas well's operating condition changes, combined with pressure, temperature, flow rate, and gas composition information. This model is time-series based, first normalizing the data across different dimensions to allow comparisons of data with different dimensions on the same scale. Then, model building module 2 uses a weighted algorithm to generate comprehensive evaluation indicators, with the initial values ​​of the weight coefficients derived from statistical analysis of historical data. During actual operation, model building module 2 dynamically adjusts the weight coefficients based on real-time data changes to ensure the model's accuracy and adaptability. For example, when the flow rate fluctuates significantly within a certain time period, model building module 2 automatically increases the weight of the flow rate to more accurately reflect the gas well's production status.

[0091] Equipment Management Module 3 is connected to Model Building Module 2 and is responsible for calling Equipment Status Management Module to perform compatibility analysis based on gas composition information. Equipment Management Module 3 obtains the current gas composition information from Model Building Module 2 and matches it with the equipment operating parameters stored in Equipment Status Management Module. The matching analysis mainly considers the equipment's pressure resistance, temperature control range, and compatibility with specific gas components. For example, when the gas composition information shows a high methane content in the gas well, Equipment Management Module 3 will prioritize selecting equipment that can withstand high methane concentrations. After the compatibility analysis, Equipment Management Module 3 generates a set of equipment resources containing all compliant production equipment. The generation of the equipment resource set requires frequent access to the database in Equipment Status Management Module; therefore, Equipment Management Module 3 and Equipment Status Management Module interact via a high-speed communication interface to ensure real-time and reliable data transmission.

[0092] Furthermore, the personnel management module 4 is connected to the equipment management module 3 and is responsible for calling the operator information management module for permission matching. The personnel management module 4 obtains the equipment resource set from the equipment management module 3 and matches it with the operator information stored in the operator information management module. The matching analysis is mainly based on the operator's professional skill level, work experience, and equipment operation qualifications. For example, for equipment requiring high-precision operation, the personnel management module 4 will prioritize selecting operators with advanced skill levels and relevant equipment operation qualifications. After permission matching, the personnel management module 4 generates an operator set containing all qualified operators. The personnel management module 4 and the operator information management module interact with each other via a secure communication protocol to ensure the security and privacy of operator information.

[0093] Finally, the optimization analysis module 5 is connected to the equipment management module 3 and the personnel management module 4. It is responsible for dynamically optimizing the multi-dimensional data fusion model based on the equipment resource set and the operator set, and outputting optimization decision results. The optimization analysis module 5 obtains the equipment resource set from the equipment management module 3 and the operator set from the personnel management module 4, and combines this information with the comprehensive evaluation indicators in the multi-dimensional data fusion model. The optimization analysis module 5 employs a multi-objective optimization algorithm, comprehensively considering production efficiency, equipment utilization, and operator workload to generate optimal production strategy recommendations. Simultaneously, the optimization analysis module 5 dynamically adjusts the optimization objectives and constraints based on real-time data changes, thereby ensuring the scientific validity and practicality of the optimization decision results. For example, when the gas well flow rate drops significantly within a certain time period, the optimization analysis module will prioritize adjusting the equipment configuration, selecting equipment more suitable for low-flow conditions, and reallocating operator tasks to improve production efficiency.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0095] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A gas well production management method based on the Internet of Things, characterized in that, Includes the following steps: Step 1: Connect to the Internet of Things (IoT) sensor network to collect pressure, temperature, flow rate, and gas composition information of the target gas well; Step 2: Construct a multi-dimensional data fusion model based on the data information obtained in Step 1 to assess the dynamic changes in gas well production; Step 3: Based on the gas composition information, select production equipment that meets the preset standards for compatibility and generate a set of equipment resources; Step 4: Match the operators' professional skill level, work experience, and equipment operation qualifications to generate a set of operators for each piece of equipment; Step 5: Based on the set of equipment resources and the set of operators, perform dynamic optimization analysis on the multi-dimensional data fusion model, and output production management strategies and optimization decision results; The multidimensional data fusion model constructed in step 2 has a safety threshold set. The safety threshold is based on historical data of the pressure, temperature, flow rate and gas composition information of the target gas well during the stable production stage. The normal fluctuation range of each parameter is determined through statistical analysis and set in combination with the standard limit value for safe operation of the gas well. If the evaluation index of the multidimensional data fusion model exceeds the safety threshold, the multidimensional data fusion model will send an alarm message to the designated terminal. The alarm message includes the current production data, the anomaly type, and the scope of impact. Step 3 specifically involves extracting the sulfur content, corrosive component concentration, and flammable and explosive component concentration from the gas composition as key parameters; presetting the corrosion resistance level, explosion-proof level, and material compatibility threshold of the production equipment; calculating the matching degree between each production equipment and the key parameters using a weighted scoring method; and selecting equipment with a matching degree not lower than the preset threshold as compatible equipment. In step 5, the dynamic optimization analysis of the multidimensional data fusion model is specifically carried out by adopting a distributed computing architecture. The computing task is decomposed into multiple sub-tasks, which are processed in parallel by different computing nodes. Finally, the production management strategy and optimization decision results are aggregated to generate the production management strategy and optimization decision results. The production management strategy includes dynamically allocating equipment to different gas wells based on real-time equipment status and production task priorities, and matching suitable operators to each piece of equipment based on the professional skill level and work experience of the operators. The optimization decision-making results include real-time analysis of gas well pressure, temperature, and flow rate through a distributed computing architecture, combined with equipment performance curves and historical production data, to generate equipment parameter optimization schemes, and to reallocate energy and materials based on dynamic changes in gas well production.

2. The gas well production management method based on the Internet of Things according to claim 1, characterized in that, After obtaining the pressure, temperature, flow rate, and gas composition information of the target gas well in step 1, the method further includes: Edge computing nodes are used to perform preliminary processing on the collected raw data, removing outliers and supplementing missing data; data collected by different types of sensors are synchronously calibrated, and a unified timestamp is generated through data fusion algorithms.

3. The gas well production management method based on the Internet of Things according to claim 1, characterized in that, After sending the alarm information to the designated terminal, it also includes: Receive feedback instructions from the designated terminal, execute the feedback instructions, and store the feedback instructions and corresponding production data in the database; When the same type of anomaly is detected again, the stored feedback instructions are automatically recalled as a reference; and a warning signal is issued to on-site personnel through an audible and visual alarm device, prompting them to take appropriate measures.

4. The gas well production management method based on the Internet of Things according to claim 1, characterized in that, If the production data received by the processor comes from a remote control request from a specified terminal, the remote control instructions are executed directly.

5. A gas well production management system based on the Internet of Things, characterized in that, For implementing the Internet of Things-based gas well production management method according to any one of claims 1-4, the system comprises: The data acquisition module (1) is used to connect to the Internet of Things sensor network and call the gas well monitoring module to obtain the data parameters of the target gas well; Model building module (2) constructs a multi-dimensional data fusion model based on the acquired data information; The equipment management module (3) is used to perform compatibility analysis based on gas composition information and generate a set of equipment resources; The personnel management module (4) is used to match the professional skill level, work experience and equipment operation qualifications of operators to generate a set of operators corresponding to each piece of equipment. The optimization analysis module (5) performs dynamic optimization analysis on the multi-dimensional data fusion model based on the set of equipment resources and the set of operators, and outputs production management strategies and optimization decision results.

6. In the gas well production management system based on the Internet of Things according to claim 5, the data acquisition module (1), model building module (2), equipment management module (3), personnel management module (4) and optimization analysis module (5) are connected in hardware through an industrial-grade communication bus; and all of the above modules are connected to the processor via signals.

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