CN114837654A - Oil well working fluid level multi-end monitoring system based on Internet of things and cloud platform - Google Patents
Oil well working fluid level multi-end monitoring system based on Internet of things and cloud platform Download PDFInfo
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- 239000003129 oil well Substances 0.000 title claims abstract description 70
- 238000012544 monitoring process Methods 0.000 title claims abstract description 46
- 239000012530 fluid Substances 0.000 title claims abstract description 24
- 239000007788 liquid Substances 0.000 claims abstract description 72
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- E—FIXED CONSTRUCTIONS
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- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/04—Measuring depth or liquid level
- E21B47/047—Liquid level
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Abstract
The invention relates to the technical field of oil well liquid level monitoring, in particular to an oil well working fluid level multi-end monitoring system based on the Internet of things and a cloud platform, which comprises an oil well liquid level meter, a monitoring and analyzing host, a wireless network bridge, a wireless local area network host and a wireless local area network slave, wherein the signal end of the oil well liquid level meter is connected with the monitoring and analyzing host, the monitoring and analyzing host is in wired communication connection with the wireless network bridge, and the wireless local area network host and the wireless local area network slave are respectively in wireless communication connection with the wireless network bridge.
Description
Technical Field
The invention relates to the technical field of oil well liquid level monitoring, in particular to an oil well working liquid level multi-end monitoring system based on the Internet of things and a cloud platform.
Background
In the drilling and exploitation of oil wells, the accurate monitoring of the liquid level of the oil well (at the moment, the liquid level is dynamically changed and is called as the "working fluid level") directly affects the site safety and the production efficiency of the oil well, and the monitoring of the working fluid level of the oil well is very important for the site safety guarantee and the improvement of the operation efficiency. The traditional dynamic liquid level monitoring usually needs the maneuvering operation of operators, the real-time performance is not high, and the coordination among different areas and responsibility personnel of an oil well site is poor.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an oil well working fluid level multi-end monitoring system based on the Internet of things and a cloud platform, wherein oil well working fluid level monitoring equipment is fixedly arranged at the throttle manifold and other places of an oil well, and multi-terminal cooperative control of oil well sites and multi-terminal synchronous display of working fluid level monitoring data are realized by using the related technology of the Internet of things.
In order to achieve the purpose, the invention provides the following technical scheme: the oil well working fluid level multi-end monitoring system based on the Internet of things and the cloud platform comprises an oil well liquid level meter, a monitoring and analyzing host, a wireless network bridge, a wireless local area network host and a wireless local area network slave, wherein a signal end of the oil well liquid level meter is connected with the monitoring and analyzing host, the monitoring and analyzing host is in wired communication connection with the wireless network bridge, and the wireless local area network host and the wireless local area network slave are in wireless communication connection with the wireless network bridge respectively.
Preferably, the wireless local area network system further comprises a cloud platform, and the wireless local area network host is in data communication with the cloud platform through the internet.
Preferably, the cloud platform comprises a gateway, a server module, a database and an intelligent algorithm module, the server module is in wireless communication with the wireless local area network host through the gateway, and the database and the intelligent algorithm module are respectively in communication with the server module.
Preferably, the wireless lan slave machine includes a computer terminal and a mobile phone terminal.
Preferably, the data processing mode of the cloud platform is to receive oil well liquid surface sound wave data sent by the wireless local area network host, judge whether the data is abnormal according to a preset algorithm, analyze abnormal element reasons if the data is abnormal, and output a data abnormality prompt and specific abnormal reasons; and if the data is normal, selecting a corresponding data processing algorithm according to the oil well test position, analyzing the data, and returning specific liquid level position data after the analysis is finished.
Preferably, the preset algorithm for judging whether the data is abnormal is a BP neural network algorithm.
Preferably, the data processing algorithm for analysis outputs the well fluid level depth data for analysis using amplitude determination, interval least squares smoothing filtering, and autocorrelation period estimation.
Compared with the prior art, the invention has the beneficial effects that: the multi-terminal cooperative control of oil well sites and the multi-terminal synchronous display of the monitoring data of the working fluid level are realized by utilizing the related technology of the Internet of things, when severe weather is encountered, the position data of the fluid level can be monitored in real time through the liquid level meter indoors without influence, and a lot of convenience is brought to all-weather safe monitoring of the working fluid level.
Drawings
FIG. 1 is a schematic representation of the well level measurement of the present invention;
FIG. 2 is a schematic diagram of a wireless bridge LAN in accordance with the present invention;
fig. 3 is a schematic diagram of a wireless router local area network according to the present invention.
FIG. 4 is a graph of measured level echo detection at a choke manifold according to the present invention;
FIG. 5 is a schematic diagram of a cloud platform architecture according to the present invention;
FIG. 6 is a flow diagram of cloud platform analysis of the present invention;
FIG. 7 is a diagram of the BP neural network structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that: the utility model provides an oil well working fluid level multi-end monitoring system based on thing networking and cloud platform which characterized in that: the oil well liquid level monitoring system comprises an oil well liquid level instrument, a monitoring and analyzing host, a wireless network bridge, a wireless local area network host, a wireless local area network slave and a cloud platform, wherein a signal end of the oil well liquid level instrument is connected with the monitoring and analyzing host, the monitoring and analyzing host is in wired communication connection with the wireless network bridge, the wireless local area network host and the wireless local area network slave are in wireless communication connection with the wireless network bridge respectively, and the wireless local area network slave comprises a computer end and a mobile phone end.
As shown in fig. 2, the wlan master plays a role of a wlan server, and is responsible for controlling the liquid level meter, and simultaneously, can provide synchronous echo data waveform and liquid level position data for the wlan slave in real time. The slave (computer end or mobile phone end) in the local area network is used as a client and firstly needs to be connected to a server made by a local area network host, and after the slave is successfully connected, the local area network host can transmit the received echo data waveform and liquid level position data to the client connected to the local area network host in real time. Therefore, oil well operating or monitoring personnel can conveniently and synchronously check the real-time data of the oil well liquid level.
In addition, if the environment is severe or the site does not allow a tester to measure the page position beside the well, the point-to-point remote control of the liquid level instrument equipment beside the well can be realized through the two wireless network bridge antennas, and the point-to-point linear distance is about 200 meters under the condition of no shielding. At this time, a wireless router can be used to realize local area networking to meet the requirement of multi-end synchronous receiving, the wireless local area network is composed of a host connected to the wireless router, and a computer end or a mobile phone end slave connected to the wireless router, as shown in fig. 3, the wireless local area network host plays the role of a wireless local area network server, is connected to a wireless network bridge at the near end through a network cable to realize wireless connection with a far-end liquid level instrument device, and can provide synchronous echo data waveform and liquid level position data for the wireless local area network slave in real time while being responsible for controlling the liquid level instrument. The slave (computer end or mobile phone end) in the local area network is used as a client and firstly needs to be connected to a server made by a local area network host, and after the slave is successfully connected, the local area network host can transmit the received echo data waveform and liquid level position data to the client connected to the local area network host in real time. Also, the oil well operating or monitoring personnel can conveniently and synchronously check the real-time data of the oil well liquid level.
The oil well liquid level meter calculates the depth of the oil well liquid level according to the liquid level echo by an echo measurement mode. The measurement schematic diagram is shown in figure 1, sound waves are emitted at the annular space between the wellhead casing and the oil pipe, the reflected waves of the coupling and the liquid level are measured, and when the length of the coupling is known, the propagation speed v of the sound waves in the annular space and the time t of the reflected waves of the liquid level are calculated, so that the depth s of the liquid level, namely the depth s of the liquid level, is obtained
s＝vt/2 (1)
Generally, a liquid level meter performs two-way processing on acquired echo data: one path of the signal enters the high-frequency filtering channel and then becomes a nodal hoop wave signal to calculate sound velocity data, the other path of the signal enters the low-frequency filtering channel and becomes a liquid surface wave signal, and the liquid surface position is positioned through waveform sorting.
The oil well liquid level meter used at present mainly has two problems: firstly, the oil well site is often required to be tested by manpower regularly, the efficiency is low, and the oil well site is influenced by site testing weather and environment; secondly, the collected data cannot be analyzed accurately, and the accurate liquid level data is obtained by manual confirmation and comparison analysis for many times.
Aiming at the first problem, according to long-term test and verification, the throttle manifold is selected as a position for fixedly placing the liquid level meter, the measured liquid level echo data is stable and easy to analyze, and unmanned remote real-time monitoring is easier to realize.
The actually measured liquid level echo waveform at this position is shown in fig. 4, and it can be seen from the figure that although the initial segment has large interference, the following waveform obviously shows the periodic attenuation characteristic of the liquid level echo, and the software is easy to automatically analyze and calculate the specific position of the oil well liquid level.
For the second problem, the second problem can be realized by means of an intelligent algorithm on the cloud platform, which is specifically described as follows:
and the wireless local area network host is in data communication with the cloud platform through the Internet.
The cloud platform comprises a gateway, a server module, a database and an intelligent algorithm module, wherein the server module is in wireless communication with a wireless local area network host through the gateway, and the database and the intelligent algorithm module are respectively in communication with the server module.
The cloud platform aims to construct an oil well liquid level complex data intelligent analysis center and a data storage center. The intelligent oil well liquid level recognition algorithm is distributed on a cloud platform, so that field operators can process complex data which are difficult to accurately analyze the specific position of the liquid level through the Internet by means of a cloud complex algorithm more conveniently, meanwhile, working liquid level data of an oil field are gathered to a cloud data center, the testing experience sharing of workers in different areas is facilitated, the continuous optimization of the cloud intelligent algorithm is facilitated, and a more accurate intelligent recognition algorithm is provided for the field workers, the specific structure is shown in figure 5, when the complex data of the oil field need to be supported by a cloud, a button for uploading the current data is arranged on a host program interface, under the condition that a host is networked, the button is clicked to upload the data, an intelligent algorithm program is called after the cloud server application program receives the data, and the algorithm program can accurately recognize the position of the liquid level by means of strong computing power of the cloud, and the calculated result is transmitted to an oil well field host application program requesting cloud support through the server application program at a fixed point through the Internet, and a tester can obtain the accurate oil well liquid level depth.
After the liquid level position test data analysis is completed, the server application program can synchronously store the data into the database, the data of the database can be downloaded and learned by testers on one hand, and on the other hand, the intelligent algorithm program can regularly update the algorithm by using the test data of the database, so that the complex data can be analyzed more accurately in the later period.
The core of the cloud platform is intelligent analysis of uploaded liquid level position test data, the specific analysis flow is shown in fig. 6, the data processing mode of the cloud platform is to receive oil well liquid level sound wave data sent by a wireless local area network host, and judge whether the data is abnormal or not according to a preset algorithm (in the actual oil well liquid level test, the measured data often cannot be analyzed to carry out oil well liquid level due to equipment reasons or field environment influence, the data are called abnormal liquid level test data, the specific reasons of the test abnormality include insufficient gas cylinder pressure, air leakage of a gun body, dirt of the gun body, failure of a main board, unsmooth field test gas circuit, excessive liquid level bubbles and the like), if the data is abnormal, the abnormal element reasons are analyzed, and data abnormal prompts and specific abnormal reasons are output; and if the data is normal, selecting a corresponding data processing algorithm according to the oil well test position, analyzing the data, and returning specific liquid level position data after the analysis is finished.
The preset algorithm for judging whether the data is abnormal is a BP neural network algorithm, which is shown in fig. 7, and the BP neural network is a feed-forward neural network and is mainly characterized by signal forward transmission and error backward propagation. In forward pass, the input signal is processed layer by layer from the input layer through the hidden layer to the output layer. The neuronal state of each layer only affects the neuronal state of the next layer. If the expected output cannot be obtained by the output layer, the backward propagation is carried out, and the network weight and the broad value are adjusted according to the prediction error, so that the predicted output of the BP neural network continuously approaches the expected output.
The oil well liquid level data is a sound wave sampling sequence, the whole sampling time is 30 seconds, according to the acoustic characteristics of the oil well liquid level data, the 30 seconds are respectively and sequentially divided into 10 sections in the time domain, the negative maximum extreme value of each section is extracted, meanwhile, 14 total values of the time domain positive maximum amplitude, the time domain negative maximum amplitude, the frequency domain main peak frequency and the frequency domain sub-bead peak frequency of the sound wave sampling sequence are extracted to be used as the input of a BP neural network, and the corresponding input layer node is changed into 14. And selecting 7 results of normal data, insufficient gun body pressure, dirt on the gun body, mainboard failure, unsmooth field test gas circuit, excessive liquid level bubbles and other reasons as the output of the BP neural network, wherein the corresponding output layer node is changed into 7. According to the number of input and output nodes and the empirical value, the number of hidden layer nodes is selected to be 18.
The whole structure of the BP neural network is 14-18-7, namely the input layer has 14 nodes, the hidden layer has 18 nodes, and the output layer has 7 nodes. After thousands of training, the network can accurately screen abnormal data in complex data in actual operation. Meanwhile, with the continuous accumulation of measured data in the database, the network can regularly train new data and update the network, and in addition, with the help of the strong computing power of cloud computing, the network structure can be continuously enriched in the later period, so that the whole intelligent network algorithm is more robust and accurate.
The data processing algorithm for analysis outputs well fluid level depth data for analysis using amplitude determination, interval least squares smoothing filtering, and autocorrelation period estimation.
Through the technical scheme, the system firstly fixedly installs the liquid level meter on the throttle manifold and other places of the oil well and correspondingly protects the liquid level meter against the long-term placement situation. Meanwhile, based on the wireless network bridge and the local area interconnection technology, a plurality of monitoring terminals are arranged in different areas of an oil well field, and testers can realize remote all-weather cooperative testing on the position data of the underground liquid level in different areas. When complex monitoring data which are difficult to directly judge the accurate liquid level position appear on an oil well site, the monitoring data are uploaded to the cloud end through one key, and more accurate liquid level position monitoring is realized through a complex intelligent algorithm arranged at the cloud end.
The method comprises the steps of firstly, selecting a fixed monitoring position on an oil well site, placing an oil well liquid level instrument, building an oil well local area interconnection system by means of a wireless network bridge and a wireless route, enabling the whole system to achieve synchronous testing and synchronous display of the oil well liquid level data, enabling a monitoring terminal to be placed in indoor environments such as a well site logging instrument and the like, enabling the monitoring terminal to be a computer or a mobile phone, and enabling the oil well liquid level instrument to be convenient and fast in real-time testing and timely coordination.
After the problem of the testing link is solved, the difficult and complicated data which are uploaded to the cloud platform on the oil well site through the Internet can be solved through the data analysis server. The data analysis server is provided with an intelligent recognition algorithm, and meanwhile, the cloud platform has strong cloud computing power and cloud storage capacity, so that the implementation of a complex algorithm on the cloud platform is very convenient. As most of complex data are invalid abnormal test data, the abnormal data are classified and judged by using a neural network algorithm, and for normal data, different liquid level identification algorithms are respectively used for position judgment according to different test positions.
And after the whole process is finished, the data analysis server transmits the result back to the liquid level instrument terminal of the oil well site through the Internet. The complex data uploaded every time can be stored to the database server by the data analysis server, the data are convenient for technicians to download for experience accumulation, and the intelligent analysis algorithm can be assisted to carry out iteration upgrading so that the working fluid level of the oil well can be judged more accurately.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The utility model provides an oil well working fluid level multi-end monitoring system based on thing networking and cloud platform which characterized in that: the oil well level gauge comprises an oil well level gauge, a monitoring and analyzing host, a wireless network bridge, a wireless local area network host and a wireless local area network slave, wherein a signal end of the oil well level gauge is connected with the monitoring and analyzing host, the monitoring and analyzing host is in wired communication connection with the wireless network bridge, and the wireless local area network host and the wireless local area network slave are in wireless communication connection with the wireless network bridge respectively.
2. The oil well working fluid level multi-end monitoring system based on the internet of things and the cloud platform as claimed in claim 1, wherein: the wireless local area network host is in data communication with the cloud platform through the Internet.
3. The oil well working fluid level multi-end monitoring system based on the internet of things and the cloud platform as claimed in claim 2, wherein: the cloud platform comprises a gateway, a server module, a database and an intelligent algorithm module, wherein the server module is in wireless communication with a wireless local area network host through the gateway, and the database and the intelligent algorithm module are respectively in communication with the server module.
4. The oil well working fluid level multi-end monitoring system based on the internet of things and the cloud platform as claimed in claim 1, wherein: the slave wireless local area network comprises a computer end and a mobile phone end.
5. The oil well working fluid level multi-end monitoring system based on the Internet of things and the cloud platform as claimed in claim 3, wherein: the data processing mode of the cloud platform is that oil well liquid surface sound wave data sent by a wireless local area network host are received, whether the data are abnormal or not is judged according to a preset algorithm, if the data are abnormal, abnormal element reasons are analyzed, and data abnormal prompts and specific abnormal reasons are output; if the data is normal, selecting a corresponding data processing algorithm according to the oil well testing position, analyzing the data, and returning specific liquid level position data after the analysis is finished.
6. The oil well working fluid level multi-end monitoring system based on the internet of things and the cloud platform as claimed in claim 5, wherein: and the preset algorithm for judging whether the data is abnormal is a BP neural network algorithm.
7. The oil well working fluid level multi-end monitoring system based on the internet of things and the cloud platform as claimed in claim 5, wherein: the data processing algorithm for analysis outputs well fluid level depth data for analysis using amplitude determination, interval least squares smoothing filtering, and autocorrelation period estimation.
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