Intelligent monitoring equipment for natural gas wellhead

Abstract

The invention discloses a natural gas well mouth intelligent monitoring device which comprises an electric gas flow valve, two connecting discs are symmetrically fixed to the two ends of the electric gas flow valve, connecting holes are annularly distributed in the connecting discs, an indicator is formed on one side wall of the electric gas flow valve, and the indicator is arranged on the other side wall of the electric gas flow valve. A connecting rod is installed in the middle of the upper end of the electric gas flow valve, the top end of the connecting rod is connected with a shell, a display screen is installed on one side wall of the shell, an antenna is arranged on the other side wall of the shell, a solar panel assembly is installed at the upper end of the shell, and a processing terminal is fixed to one side in the shell. One side of the processing terminal is connected with a storage unit, one side of the storage unit is provided with a communication module, a lithium battery is fixed below the communication module, and the back of the shell is provided with a power connection port; according to different abnormal types, the system has the capability of intelligently and automatically generating feedback control suggestions.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas high-stability monitoring devices, and particularly relates to an intelligent monitoring device for natural gas wellheads. Background Art

[0002] In the prior art, there are significant limitations in the monitoring of natural gas wellheads. Traditional monitoring means are difficult to comprehensively and accurately obtain key parameters such as pressure and temperature at different positions of the production string, and cannot timely capture subtle changes in the parameters. Moreover, existing detection devices have poor stability and reliability in the face of complex production environments, such as high pressure, low temperature, and high humidity, and often have problems such as data errors or signal interruptions. In addition, the existing monitoring systems are inefficient in data transmission and processing, and it is difficult to meet the real-time requirements and cannot quickly provide strong support for production decisions. In summary, there is an urgent need for an efficient and reliable natural gas wellhead monitoring technology to solve the key problems in the process of natural gas hydrate production and ensure the smooth progress of production work and the improvement of production efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent monitoring device for natural gas wellheads in view of the current situation of the prior art.

[0004] The present invention is realized through the following technical solutions: The present invention provides an intelligent monitoring device for natural gas wellheads, including an electric gas flow valve. Two connection plates are symmetrically fixed at both ends of the electric gas flow valve. Connection holes are annularly distributed on the connection plates. An indicating mark is formed on one side wall of the electric gas flow valve. A connecting rod is installed in the middle of the upper end of the electric gas flow valve. The top of the connecting rod is connected to a housing. A display screen is installed on one side wall of the housing. An antenna is arranged on the other side wall of the housing. A solar panel assembly is installed on the upper end of the housing. A processing terminal is fixed on one side inside the housing. A storage unit is connected to one side of the processing terminal. A communication module is installed on one side of the storage unit. A lithium battery is fixed below the communication module. A power connection port is installed on the back of the housing;

[0005] It further includes a control system for the monitoring device. The signal output ends of all sensors and collection devices are uniformly connected to the data transmission layer through a dedicated cable or a wireless transmission module. The data transmission layer preliminarily integrates and encodes these signals, and uses a reliable data transmission protocol, such as the industrial Ethernet protocol or the low-power wide-area network protocol (selected according to the actual production environment), to stably and quickly transmit the data to the upper computer.

[0006] Preferably, a normal data range and a data change trend model are preset in the host computer. For temperature and pressure data, according to the technological requirements and historical data of natural gas hydrate exploitation, the normal temperature and pressure ranges at the positions of each sensor are set. For example, the normal temperature range at the submersible pump motor is set at [X1, X2] degrees Celsius, and the normal pressure range is set at [Y1, Y2] megapascals. For the data of the production and wellhead data collection device, and the data of the water sample and gas sample analysis sensor, corresponding normal index ranges are also set. When the collected data exceeds the normal range, or the change rate of parameters such as temperature and pressure is abnormal, the abnormal detection program is triggered.

[0007] Preferably, data analysis algorithms are used to deeply process the collected data. Data mining algorithms, such as association rule mining, are adopted to analyze the potential associations between the data of different sensors to discover hidden factors that may cause wellhead abnormalities. For example, if it is found that while the suction pressure of the submersible pump decreases, the temperature at a certain position of the exhaust pipe rises abnormally, and the production fluctuates, the degree of association between these abnormalities is judged through the association rule algorithm, and then the cause of the abnormality is determined.

[0008] Preferably, when the data processing and analysis layer determines an abnormality, the warning and feedback layer immediately activates the warning mechanism. Through the sound and light alarm device, an alarm is sent to the host computer operator, and at the same time, the abnormal position and abnormal parameters are prominently displayed on the monitoring interface. For example, the position where the temperature of the submersible pump motor rises abnormally is marked with a red flash, and the current temperature value and the normal range are displayed beside it.

[0009] Preferably, according to the type of abnormality, the system automatically generates feedback control suggestions. If the risk of pipeline rupture may be caused by excessive pressure at a certain position, the system suggests reducing the output power of the submersible pump to reduce the fluid pressure, and this suggestion is displayed on the monitoring interface for the operator to refer to and execute.

[0010] Preferably, in the host computer system, a comprehensive and normal data range and an actual data change trend model need to be set. For temperature and pressure data, the basis for setting is quite crucial. On the one hand, it is necessary to closely refer to the established technological requirements for natural gas hydrate exploitation, which are formed based on long-term practice and scientific research and are crucial for the stability and safety of the exploitation process. On the other hand, a large amount of detailed historical data is also an indispensable reference. By deeply mining and analyzing past data, the regular fluctuation patterns of the data can be understood. On this basis, for each position where the sensor is located, extremely precise normal temperature and pressure ranges are set one by one. For example, due to its special working environment and operation mechanism at the electric submersible pump motor, the normal temperature range is strictly set at [X1, X2] degrees Celsius, and the normal pressure range is set at [Y1, Y2] megapascals. Similarly, for the data collected by the production and wellhead data collection device, and the water sample and gas sample analysis sensors, corresponding normal index ranges are set according to similar scientific methods. When the data collected by the system in real time exceeds the pre-set normal range, or the change rate of key parameters such as temperature and pressure shows an abnormal trend, the abnormal detection program will be immediately triggered to quickly investigate potential problems.

[0011] The present invention has the following beneficial effects compared with the prior art:

[0012] The present invention can, according to different abnormal types, enable the system to have the intelligent ability to automatically generate feedback control suggestions. If the pressure at a certain position is too high, and the system evaluates that it may lead to the risk of pipeline rupture, the system will, based on the accurate understanding and analysis of the entire exploitation process flow, suggest reducing the output power of the electric submersible pump. By reducing the output power, the pressure of the fluid in the pipeline can be effectively reduced, thereby reducing potential risks. At the same time, this suggestion will be clearly displayed on the monitoring interface, providing clear operation reference for the operator, facilitating its execution according to the actual situation, and ensuring the safety and stability of the entire natural gas exploitation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a schematic structural diagram of a natural gas wellhead intelligent monitoring device according to the present invention;

[0014] Figure 2 is a right cross-sectional view of the housing in a natural gas wellhead intelligent monitoring device according to the present invention;

[0015] 1. Electric gas flow valve; 2. Antenna; 3. Solar panel assembly; 4. Connection plate; 5. Indicator mark; 6. Connection hole; 7. Connecting rod; 8. Housing; 9. Display screen; 10. Processing terminal; 11. Storage unit; 12. Communication module; 13. Lithium battery; 14. Power connection port. DETAILED DESCRIPTION OF THE INVENTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] As Figure 1 - Figure 2 shown, an intelligent monitoring device for a natural gas wellhead in this embodiment includes an electric gas flow valve 1. The electric gas flow valve 1 can automatically monitor the flow rate of natural gas. Two connection plates 4 are symmetrically fixed at both ends of the electric gas flow valve 1. Connection holes 6 are annularly distributed on the connection plates 4. An indicating mark 5 is formed on one side wall of the electric gas flow valve 1. The gas flow direction can be clearly known by using the indicating mark 5. The connecting rod 7 plays a role in connection and fixation. The connecting rod 7 is installed in the middle of the upper end of the electric gas flow valve 1. The top end of the connecting rod 7 is connected to a housing 8. A display screen 9 is installed on one side wall of the housing 8. An antenna 2 is arranged on the other side wall of the housing 8. The signal transmission effect can be enhanced through the antenna 2. The solar panel assembly 3 can convert external light energy into electrical energy for use. The solar panel assembly 3 is installed on the upper end of the housing 8. A processing terminal 10 is fixed on one side inside the housing 8. The operation of each device can be controlled through the processing terminal 10. The storage unit 11 can store information data. The storage unit 11 is connected to one side of the processing terminal 10. A communication module 12 is installed on one side of the storage unit 11. A lithium battery 13 is fixed below the communication module 12. A power connection port 14 is installed on the back of the housing 8. Networking communication can be carried out by virtue of the communication module 12 to realize the wireless transmission of information data. The lithium battery 13 can be used for power supply. The power connection port 14 can connect to an external power supply; As Figure 1 - Figure 2 shown, in this embodiment, the connection plate 4 is welded to the electric gas flow valve 1. The connection holes 6 are formed on the connection plate 4. Through the connection plate 4 and the connection holes 6, the two ends can be connected and fixed to the external natural gas pipeline. The indicating mark 5 is formed on the electric gas flow valve 1. The connecting rod 7 is welded to the electric gas flow valve 1. The housing 8 is bolted to the connecting rod 7. The display screen 9 is connected to the housing 8 by a card slot. The display screen 9 is an LCD screen. The housing 8 is made of polycarbonate and has good waterproof and corrosion-resistant effects. The display screen 9 can display information parameters. The antenna 2 is bolted to the housing 8. The solar panel assembly 3 is bolted to the housing 8. The processing terminal 10 is screwed to the housing 8. The storage unit 11 is electrically connected to the processing terminal 10. The communication module 12 is screwed to the housing 8. The lithium battery 13 is screwed to the housing 8. The power connection port 14 is screwed to the housing 8.

[0018] Preferably, the signal output ends of the electric gas flow valve 1 and the collection device are connected to the data transmission layer through a dedicated cable or a wireless transmission module. The data transmission layer preliminarily integrates and encodes these signals, and uses a reliable data transmission protocol, such as the industrial Ethernet protocol or the low-power wide area network protocol (selected according to the actual mining environment), to stably and quickly transmit the data to the upper computer.

[0019] Preferably, the normal data range and the data change trend model are preset in the upper computer. For the temperature and pressure data, according to the process requirements and historical data of natural gas hydrate mining, the normal temperature and pressure ranges of the position of the electric gas flow valve are set. For example, the normal temperature range at the electric submersible pump motor is set at [X1, X2] degrees Celsius, and the normal pressure range is set at [Y1, Y2] megapascals. For the data of the production and wellhead data collection device, and the data of the water sample and gas sample analysis sensors, the corresponding normal index ranges are also set. When the collected data exceeds the normal range, or the change rate of parameters such as temperature and pressure is abnormal, the abnormal detection program is triggered.

[0020] Preferably, data analysis algorithms are used to deeply process the collected data. Data mining algorithms, such as association rule mining, are used to analyze the potential associations between the data of different sensors to discover hidden factors that may cause wellhead anomalies. For example, if it is found that while the suction pressure at the inlet of the electric submersible pump decreases, the temperature at a certain position of the exhaust pipe rises abnormally, and the production fluctuates, the association degree between these anomalies is judged through the association rule algorithm, and then the cause of the anomaly is determined.

[0021] Preferably, when the data processing and analysis layer determines an anomaly, the warning and feedback layer immediately activates the warning mechanism. Through the sound and light alarm device, an alarm is sent to the operator of the upper computer, and at the same time, the abnormal position and abnormal parameters are prominently displayed on the monitoring interface. For example, the position where the temperature of the electric submersible pump motor rises abnormally is marked with a red flash, and the current temperature value and the normal range are displayed beside it.

[0022] Preferably, according to the type of anomaly, the system automatically generates feedback control suggestions. If the risk of pipeline rupture may be caused by excessive pressure at a certain position, the system suggests reducing the output power of the electric submersible pump to reduce the fluid pressure, and this suggestion is displayed on the monitoring interface for the operator to refer to and execute.

[0023] Preferably, in the host computer system, it is necessary to set a comprehensive and normal data range and a data change trend model that conforms to the actual situation. For temperature and pressure data, the basis for setting is quite crucial. On the one hand, it is necessary to closely refer to the established technological requirements for natural gas hydrate exploitation, which are formed on the basis of long-term practice and scientific research and are crucial for the stability and safety of the exploitation process. On the other hand, a large amount of detailed historical data is also an indispensable reference. By deeply mining and analyzing the past data, the regular fluctuation laws of the data can be insight. On this basis, for each position where the sensor is located, extremely accurate normal temperature and pressure intervals are set one by one. For example, due to its special working environment and operation mechanism, the normal temperature range at the electric submersible pump motor is strictly set between [X1, X2] degrees Celsius, and the normal pressure range is set between [Y1, Y2] megapascals. Similarly, for the data collected by the production and wellhead data collection device, and the water sample and gas sample analysis sensors, the corresponding normal index ranges are also set according to similar scientific methods. When the data collected by the system in real time exceeds the pre-set normal range, or the change rate of key parameters such as temperature and pressure shows an abnormal trend, the abnormal detection program will be immediately triggered to quickly investigate potential problems.

[0024] Preferably, data analysis algorithms are used to deeply process the massive data collected. Data mining algorithms play a key role. Taking the Apriori algorithm, a typical association rule mining algorithm, as an example, its core lies in exploring frequent item sets in the transaction database by setting support and confidence thresholds, and then generating strong association rules. Association rules are an important concept in data mining, aiming to discover the hidden association relationships between items in the data set. Simply put, if item B also frequently appears when item A appears in the data set, it can be considered that there may be some association between A and B. Strong association rules are rules that meet specific threshold conditions and have high reliability and practicality among many association rules.

[0025] Taking the supermarket shopping basket analysis as an example, if a large number of customers buy bread and milk at the same time, this implies an association between bread and milk. However, not all such associations are of practical value. Strong association rules need to set some measurement indicators to screen out the truly meaningful relationships. Commonly used measurement indicators include support, confidence, and lift.

[0026] Support is used to measure the frequency of occurrence of an item set (such as {bread, milk}) in all transactions (i.e., customer shopping records). For example, among 1000 shopping records, 200 records contain both bread and milk. Then the support of the item set {bread, milk} is 200÷1000 = 20%. The higher the support, the more common the association rule appears in the dataset.

[0027] Confidence, on the other hand, is for a specific association rule, such as "buy bread → buy milk". It represents the proportion of customers who buy milk among those who have bought bread. Suppose there are 300 customers who buy bread, and 200 of them also buy milk. Then the confidence of this rule is 200÷300≈66.7%. Confidence reflects the likelihood of the conclusion (buying milk) occurring when the premise condition (buying bread) holds.

[0028] Lift is a comprehensive evaluation index for association rules. It takes into account the confidence of the rule and the probability of the item set itself appearing in the dataset. A lift greater than 1 indicates that the association rule is more likely to occur than randomly; a lift equal to 1 means that the two item sets are independent and have no association; a lift less than 1 indicates that the occurrence of the association rule is negatively correlated. For example, the probability of buying bread is 30%, the probability of buying milk is 25%, and the probability of buying both bread and milk is 20%. Then the lift of the rule "buy bread → buy milk" is (20%÷30%)÷25%≈2.67, which is much greater than 1, indicating that this rule is a meaningful strong association rule.

[0029] When constructing a natural gas wellhead anomaly detection and monitoring system, strong association rules can deeply analyze the internal relationships between various wellhead parameters. For example, in cold winters, the wellhead pressure will rise sharply due to the change in natural gas density caused by low temperature. At the same time, the flow rate in the pipeline may decrease significantly due to the condensation of some gases. By mining association rules from a large amount of historical data of similar working conditions in multiple past winters, the strong association rule between the changes in these two parameters can be revealed. Once such a parameter combination reappears in real-time monitoring, the system can quickly determine that the wellhead is in an abnormal state based on the established strong association rule, issue an alarm in a timely manner, and fully ensure the safety of natural gas production.

[0030] In actual mining scenarios, the data collected by various sensors may seem independent, but in fact, there are hidden correlations. Taking complex mining conditions as an example, the system monitors data such as a decrease in the suction pressure at the inlet of an electric submersible pump, an increase in the temperature at a specific position in the exhaust pipe, and fluctuations in production. At this time, the Apriori algorithm will first scan the data set, count the occurrence frequencies of each item set in detail, filter out the frequent item sets that meet the support threshold, then generate association rules based on these frequent item sets, and filter out strong association rules according to the confidence threshold. In this way, the correlation degree of these abnormal data can be accurately determined, the root cause of the wellhead abnormality can be sorted out, and a solid foundation can be laid for subsequent problem-solving.

[0031] Preferably, when the data processing and analysis layer determines an abnormal situation according to the established algorithms and rules, the warning and feedback layer will immediately activate the warning mechanism with a very fast response speed. First, through a professional audible and visual alarm device, a clear and prominent alarm will be sent to the operator of the upper computer. The alarm sound can ensure that the operator can detect it in a noisy working environment in time, and at the same time, cooperate with the strongly flashing lights to attract the operator's attention. In addition, on the monitoring interface, the abnormal position and abnormal parameters will be displayed prominently. For example, when the temperature of the electric submersible pump motor rises abnormally, this position will be marked with a prominent red flash, allowing the operator to lock the problem at a glance. And the current temperature value and the pre-set normal range will be displayed in detail beside it, enabling the operator to quickly and intuitively understand the severity of the abnormal situation.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent monitoring device for a natural gas wellhead, characterized in that: It includes an electric gas flow valve (1). Two connecting plates (4) are symmetrically fixed at both ends of the electric gas flow valve (1). Connecting holes (6) are annularly distributed on the connecting plates (4). An indicating mark (5) is formed on one side wall of the electric gas flow valve (1). A connecting rod (7) is installed in the middle of the upper end of the electric gas flow valve (1). The top end of the connecting rod (7) is connected to a housing (8). A display screen (9) is installed on one side wall of the housing (8). An antenna (2) is arranged on the other side wall of the housing (8). A solar panel assembly (3) is installed on the upper end of the housing (8). A processing terminal (10) is fixed on one side inside the housing (8). A storage unit (11) is connected to one side of the processing terminal (10). A communication module (12) is installed on one side of the storage unit (11). A lithium battery (13) is fixed below the communication module (12). A power connection port (14) is installed on the back of the housing (8); It also includes a control system for the monitoring device. The signal output ends of all sensors and collection devices are uniformly connected to the data transmission layer through a dedicated cable or a wireless transmission module. The data transmission layer preliminarily integrates and encodes these signals, and a normal data range and a data change trend model are preset in the upper computer. For temperature and pressure data, according to the process requirements and historical data of natural gas hydrate exploitation, the normal temperature and pressure ranges of each sensor position are set.

2. The intelligent monitoring device for natural gas wellhead according to claim 1, characterized in that: The control system deeply processes the collected data by using data analysis algorithms, adopts data mining algorithms and association rule mining to analyze the potential associations between sensor data to discover hidden factors that may cause wellhead anomalies.

3. The intelligent monitoring device for natural gas wellhead according to claim 2, characterized in that: When the data processing and analysis layer determines an anomaly, the early warning and feedback layer immediately activates the early warning mechanism. Through an audible and visual alarm device, an alarm is sent to the operator of the upper computer, and at the same time, the abnormal position and abnormal parameters are prominently displayed on the monitoring interface.

4. The intelligent monitoring device for natural gas wellhead according to claim 1, characterized in that, A normal data range and a data change trend model are preset in the upper computer. For temperature and pressure data, according to the process requirements and historical data of natural gas exploitation, the normal temperature and pressure ranges of each sensor position are set. When the collected data exceeds the normal range, or the change rate of parameters such as temperature and pressure is abnormal, an abnormal detection program is triggered.

5. The intelligent monitoring device for a natural gas wellhead according to claim 4, wherein The collected data is deeply processed by using data analysis algorithms, adopts data mining algorithms and association rule mining to analyze the potential associations between different sensor data to discover hidden factors that may cause wellhead anomalies.

6. The intelligent monitoring device for natural gas wellhead according to claim 5, characterized in that, When the data processing and analysis layer determines an anomaly, the early warning and feedback layer immediately activates the early warning mechanism, sends an alarm to the operator of the upper computer, and at the same time, the abnormal position and abnormal parameters are prominently displayed on the monitoring interface.

7. The intelligent monitoring device for natural gas wellhead according to claim 6, wherein According to the type of anomaly, the system automatically generates feedback control suggestions. If the risk of pipeline rupture may be caused by excessive pressure at a certain position, the system suggests reducing the output power of the electrical submersible pump to reduce the fluid pressure, and this suggestion is displayed on the monitoring interface for the operator to refer to and execute.

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