CN114837654A - Multi-terminal monitoring system for oil well dynamic liquid level based on Internet of Things and cloud platform - Google Patents
Multi-terminal monitoring system for oil well dynamic liquid level based on Internet of Things and cloud platformDownload PDFInfo
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及油井液面监测技术领域，具体领域为一种基于物联网及云平台的油井动液面多端监测系统。The invention relates to the technical field of oil well liquid level monitoring, in particular to an oil well dynamic liquid level multi-terminal monitoring system based on the Internet of Things and a cloud platform.
背景技术Background technique
在油井的钻探和开采中，油井液面(此时液面是动态变化的，叫做“动液面”)的准确监测直接影响着油井的现场安全以及生产效率，油井动液面的监测对于现场的安全保障以及作业效率提升十分重要。传统的动液面监测往往需要操作人员机动作业，实时性不高且油井现场不同区域及职责人员间协调性差。In the drilling and production of oil wells, the accurate monitoring of oil well liquid level (at this time the liquid level changes dynamically, called "dynamic liquid level") directly affects the field safety and production efficiency of oil wells. It is very important to ensure safety and improve work efficiency. Traditional dynamic liquid level monitoring often requires operators to operate maneuvers, with low real-time performance and poor coordination between different areas and responsible personnel on the oil well site.
发明内容SUMMARY OF THE INVENTION
针对现有技术存在的不足，本发明的目的在于提供一种基于物联网及云平台的油井动液面多端监测系统，将油井动液面监测设备固定安装于油井的节流管汇等地方，利用物联网相关技术实现油井现场多终端协同操控以及动液面监测数据的多终端同步显示。In view of the deficiencies in the prior art, the purpose of the present invention is to provide a multi-terminal monitoring system for oil well dynamic liquid level based on the Internet of Things and cloud platform, and the oil well dynamic liquid level monitoring equipment is fixedly installed in the choke manifold and other places of the oil well, The use of Internet of Things related technologies realizes the coordinated control of multiple terminals on the oil well site and the simultaneous display of multiple terminals of dynamic liquid level monitoring data.
为实现上述目的，本发明提供如下技术方案：一种基于物联网及云平台的油井动液面多端监测系统，包括油井液位仪、监测分析主机、无线网桥、无线局域网主机和无线局域网从机，所述油井液位仪的信号端与监测分析主机连接，监测分析主机与无线网桥有线通信连接，所述无线局域网主机和无线局域网从机分别与无线网桥无线通信连接。In order to achieve the above purpose, the present invention provides the following technical solutions: a multi-terminal monitoring system for oil well dynamic liquid level based on the Internet of Things and cloud platform, including oil well liquid level meter, monitoring and analysis host, wireless bridge, wireless local area network host and wireless local area network slave. The signal terminal of the oil well level instrument is connected with the monitoring and analysis host, the monitoring and analysis host is connected with the wireless bridge in wired communication, and the wireless LAN host and the wireless LAN slave are respectively connected with the wireless bridge in wireless communication.
优选的，还包括有云平台，所述无线局域网主机通过互联网与云平台数据通信。Preferably, a cloud platform is also included, and the wireless local area network host communicates data with the cloud platform through the Internet.
优选的，所述云平台包括网关、服务器模块、数据库和智能算法模块，所述服务器模块通过网关与无线局域网主机无线通信，所述数据库和智能算法模块分别于服务器模块通信。Preferably, the cloud platform includes a gateway, a server module, a database and an intelligent algorithm module, the server module wirelessly communicates with the wireless local area network host through the gateway, and the database and the intelligent algorithm module communicate with the server module respectively.
优选的，所述无线局域网从机包括有电脑端和手机端。Preferably, the wireless local area network slave includes a computer terminal and a mobile phone terminal.
优选的，云平台的数据处理方式为接收无线局域网主机发出的油井液面声波数据，根据预设算法判断数据是否异常，若数据异常，即分析异常元原因，并输出数据异常提示及具体异常原因；若数据正常，则根据油井测试位置选定对应的数据处理算法，并对数据进行分析，分析完成后返回具体的液面位置数据。Preferably, the data processing method of the cloud platform is to receive the acoustic wave data of the oil well liquid level sent by the wireless local area network host, and judge whether the data is abnormal according to a preset algorithm. ; If the data is normal, select the corresponding data processing algorithm according to the test position of the oil well, and analyze the data, and return the specific liquid level position data after the analysis is completed.
优选的，判断数据是否异常的预设算法为BP神经网络算法。Preferably, the preset algorithm for judging whether the data is abnormal is a BP neural network algorithm.
优选的，用于分析的数据处理算法为使用幅值测定法、区间最小二乘平滑滤波以及自相关周期估计进行分析输出油井液面深度数据。Preferably, the data processing algorithm used for analysis is to use the amplitude measurement method, interval least squares smoothing filtering and autocorrelation period estimation to analyze and output oil well liquid level depth data.
与现有技术相比，本发明的有益效果是：利用物联网相关技术实现油井现场多终端协同操控以及动液面监测数据的多终端同步显示，当遭遇恶劣天气，便可在室内无影响地通过液位仪来实时监测液面位置数据，对于动液面全天候安全监控便会带来许多便利。Compared with the prior art, the present invention has the beneficial effects of: using the Internet of Things related technology to realize the coordinated control of multiple terminals on the oil well site and the synchronous display of multiple terminals of the dynamic liquid level monitoring data, when encountering bad weather, it can be used indoors without influence. Real-time monitoring of the liquid level position data through the liquid level meter will bring a lot of convenience to the all-weather safety monitoring of the dynamic liquid level.
附图说明Description of drawings
图1为本发明的油井液面测量示意图；Fig. 1 is the oil well liquid level measurement schematic diagram of the present invention;
图2为本发明的无线网桥局域组网示意图；2 is a schematic diagram of the wireless bridge local area networking of the present invention;
图3为本发明的无线路由局域组网示意图。FIG. 3 is a schematic diagram of wireless routing local area networking according to the present invention.
图4为本发明的在节流管汇处实测液面回波检测图；Fig. 4 is the liquid level echo detection diagram actually measured at the throttling manifold of the present invention;
图5为本发明的云平台架构示意图；5 is a schematic diagram of the cloud platform architecture of the present invention;
图6为本发明的云平台分析流程图；Fig. 6 is the cloud platform analysis flow chart of the present invention;
图7为本发明的BP神经网络结构图。FIG. 7 is a structural diagram of the BP neural network of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
本发明提供一种技术方案：一种基于物联网及云平台的油井动液面多端监测系统，其特征在于：包括油井液位仪、监测分析主机、无线网桥、无线局域网主机、无线局域网从机和云平台，所述油井液位仪的信号端与监测分析主机连接，监测分析主机与无线网桥有线通信连接，所述无线局域网主机和无线局域网从机分别与无线网桥无线通信连接，所述无线局域网从机包括有电脑端和手机端。The invention provides a technical solution: a multi-terminal monitoring system for oil well dynamic liquid level based on the Internet of Things and cloud platform, which is characterized in that it includes an oil well liquid level instrument, a monitoring and analysis host, a wireless bridge, a wireless local area network host, and a wireless local area network slave. machine and cloud platform, the signal end of the oil well level instrument is connected with the monitoring and analysis host, the monitoring and analysis host is connected with the wireless bridge for wired communication, and the wireless LAN host and the wireless LAN slave are respectively connected with the wireless bridge for wireless communication, The wireless local area network slave includes a computer terminal and a mobile phone terminal.
如图2所示，无线局域网主机担当无线局域网server的角色，在负责对液位仪操控的同时，可实时的为无线局域网从机提供同步的回声数据波形和液面位置数据。局域网内的从机(电脑端或手机端)作为client首先需连接到局域网主机所做的server，连接成功后，局域网主机会实时的将接收的回声数据波形和液面位置数据传递给连接到其上的client。这样，油井操作或监控人员便可方便的同步查看油井液位的实时数据。As shown in Figure 2, the wireless LAN host plays the role of the wireless LAN server. While in charge of controlling the liquid level meter, it can provide synchronous echo data waveform and liquid level position data for the wireless LAN slave in real time. The slave in the LAN (computer terminal or mobile terminal) as the client first needs to connect to the server made by the LAN host. After the connection is successful, the LAN host will transmit the received echo data waveform and liquid level position data to the server connected to it in real time. on the client. In this way, oil well operators or monitoring personnel can easily and simultaneously view real-time data of oil well fluid levels.
另外，如果环境恶劣或者现场不允许测试人员在井旁进行页面位置测量，可通过两个无线网桥天线点对点远程操控井旁的液位仪设备，在无遮挡的情况下，点对点直线距离在200米左右。此时，可借用无线路由实现局域组网来满足多端同步接收的需求，无线局域网由连接到无线路由的主机、连接到无线路由的电脑端或手机端从机组成，如图3所示，无线局域网主机担当无线局域网server的角色，通过网线连接到近端的无线网桥，实现与远端液位仪设备的无线连接，在负责对液位仪操控的同时，可实时的为无线局域网从机提供同步的回声数据波形和液面位置数据。局域网内的从机(电脑端或手机端)作为client首先需连接到局域网主机所做的server，连接成功后，局域网主机会实时的将接收的回声数据波形和液面位置数据传递给连接到其上的client。同样，油井操作或监控人员由此可方便的同步查看油井液位的实时数据。In addition, if the environment is harsh or the tester is not allowed to measure the page position beside the well, the liquid level instrument equipment beside the well can be remotely controlled point-to-point through two wireless bridge antennas. In the case of no obstruction, the point-to-point straight-line distance is 200 meters or so. At this time, the wireless router can be used to implement local area networking to meet the needs of multi-terminal synchronous reception. The wireless local area network consists of a host connected to the wireless router, and a computer or mobile phone slave connected to the wireless router, as shown in Figure 3. The wireless local area network host plays the role of the wireless local area network server, and is connected to the near-end wireless bridge through the network cable to realize the wireless connection with the remote liquid level instrument equipment. The machine provides synchronized echo data waveform and liquid level position data. The slave in the LAN (computer terminal or mobile terminal) as the client first needs to connect to the server made by the LAN host. After the connection is successful, the LAN host will transmit the received echo data waveform and liquid level position data to the server connected to it in real time. on the client. Likewise, well operators or monitoring personnel can conveniently view real-time data on well fluid levels simultaneously.
油井液位仪是通过回声计量的方式根据液面回波而计算得到油井液面深度。测量示意图如图1所示，在井口套管与油管环空处发射声波，测量接箍和液面的反射波，在已知接箍长度时，计算得到声波在环空的传播速度v与液面反射波的时间t，从而得到液面深度s，即The oil well liquid level meter calculates the oil well liquid level depth according to the liquid level echo by means of echo measurement. The measurement diagram is shown in Figure 1. The sound wave is emitted at the wellhead casing and the tubing annulus, and the reflected waves of the coupling and the liquid surface are measured. When the length of the coupling is known, the propagation velocity v of the sound wave in the annulus and the liquid surface are calculated. The time t of the surface reflected wave, so as to obtain the liquid surface depth s, namely
s＝vt/2 (1)s=vt/2 (1)
一般情况下，液位仪对于采集到的回声数据进行两路处理：一路进入高频滤波通道，后变成节箍波信号来计算声速数据，另一路进入低频滤波通道变成液面波信号，再经过波形整理来定位液面位置。Under normal circumstances, the liquid level meter performs two-way processing on the collected echo data: one channel enters the high-frequency filter channel, and then becomes the hoop wave signal to calculate the sound speed data, and the other channel enters the low-frequency filter channel and becomes the liquid surface wave signal. Then, the liquid level position can be positioned by waveform arrangement.
目前所使用的油井液位仪主要存在两个问题：第一，常常需要人工定期到油井现场进行测试，效率较低且受现场测试天气和环境影响；第二，对所采集到的数据不能得到精确的分析结果，往往需要人工多次确认和比对分析来得到准确液位数据。There are two main problems with the currently used oil well level instrument: first, it is often necessary to conduct regular tests at the oil well site manually, which is inefficient and is affected by the weather and environment of the field test; second, the collected data cannot be obtained. Accurate analysis results often require manual confirmation and comparison analysis to obtain accurate liquid level data.
针对问题一，根据长期的测试及验证，选定节流管汇处作为固定放置液位仪的位置，此处测得的液面回波数据较为稳定且容易分析，更易实现无人远程实时监测。In view of the first problem, according to long-term testing and verification, the throttling manifold is selected as the fixed position for placing the liquid level meter. The liquid level echo data measured here is relatively stable and easy to analyze, and it is easier to realize unmanned remote real-time monitoring. .
在此位置的实测液面回波波形如图4所示，从图中可以看出，虽然初始段有较大干扰，但后面的波形却明显的呈现出液面回波的周期性衰减特性，易于软件自动分析和计算油井液面的具体位置。The measured liquid surface echo waveform at this position is shown in Figure 4. It can be seen from the figure that although there is a large disturbance in the initial section, the subsequent waveform clearly shows the periodic attenuation characteristics of the liquid surface echo. Easy-to-use software automatically analyzes and calculates the exact location of the oil well level.
针对问题二，可借助云平台上的智能算法实现，具体如下所述：For the second problem, it can be realized with the help of intelligent algorithms on the cloud platform, as follows:
所述无线局域网主机通过互联网与云平台数据通信。The wireless local area network host communicates data with the cloud platform through the Internet.
所述云平台包括网关、服务器模块、数据库和智能算法模块，所述服务器模块通过网关与无线局域网主机无线通信，所述数据库和智能算法模块分别于服务器模块通信。The cloud platform includes a gateway, a server module, a database and an intelligent algorithm module, the server module wirelessly communicates with the wireless local area network host through the gateway, and the database and the intelligent algorithm module communicate with the server module respectively.
云平台的目的在于构建一个油井液位的复杂数据智能分析中心和数据存储中心。在云平台布设智能油井液位识别算法，更方便现场操作人员通过互联网借助云端复杂算法处理难以准确分析出液面具体位置的复杂数据，同时，将油田现场的动液面数据汇总至云数据中心，不仅有利于不同区域的工作人员共享测试经验，也更有利于云端智能算法的不断优化，并以此来为现场工作人员提供更为精准的智能识别算法，具体架构如图5所示，当油井现场有复杂数据需要云端支持时，主机程序界面有一键上传当前数据的按钮，在主机联网的情况下，点击按钮上传数据，云端服务器应用程序在收到数据后，调用智能算法程序，该算法程序能够借助云计算的强大算力准确的识别出液面具体位置，计算出来的结果再由服务器应用程序通过互联网定点传递给请求云端支持的油井现场主机应用程序，测试人员便可得到准确的油井液面深度。The purpose of the cloud platform is to build a complex data intelligent analysis center and data storage center of oil well liquid level. The intelligent oil well liquid level identification algorithm is deployed on the cloud platform, which is more convenient for the field operators to process the complex data that is difficult to accurately analyze the specific position of the liquid level through the Internet with the help of the cloud complex algorithm. , which is not only conducive to the sharing of test experience among staff in different areas, but also is more conducive to the continuous optimization of cloud intelligent algorithms, so as to provide on-site staff with more accurate intelligent identification algorithms. The specific architecture is shown in Figure 5. When When there is complex data on the oil well site that needs cloud support, the host program interface has a button to upload the current data. When the host is connected to the Internet, click the button to upload the data, and the cloud server application will call the intelligent algorithm program after receiving the data. The program can use the powerful computing power of cloud computing to accurately identify the specific position of the liquid level, and the calculated results are then transmitted by the server application to the oil well site host application requesting cloud support through the Internet at a fixed point, and the tester can get the accurate oil well. liquid depth.
完成液面位置测试数据分析后，服务器应用程序会同步的将该数据存储到数据库，数据库的数据一方面可供测试人员下载学习，另一方面，智能算法程序会定期的利用数据库的测试数据更新算法，以便后期更为精准地对复杂数据进行分析。After completing the analysis of the liquid level test data, the server application will store the data in the database synchronously. On the one hand, the data in the database can be downloaded and learned by the testers. On the other hand, the intelligent algorithm program will regularly update the test data using the database. Algorithms for more accurate analysis of complex data later.
云平台的核心是对所上传液面位置测试数据的智能分析，其具体分析流程如图6所示，云平台的数据处理方式为接收无线局域网主机发出的油井液面声波数据，根据预设算法判断数据是否异常(在实际的油井液面测试中，由于设备原因或者现场环境影响，所测的数据常常无法分析出进行油井液面，这些数据被称为异常液面测试数据，导致测试异常的具体原因有气瓶压力不足、枪体漏气、枪体有污垢、主板故障、现场测试气路不畅以及液面气泡过多等)，若数据异常，即分析异常元原因，并输出数据异常提示及具体异常原因；若数据正常，则根据油井测试位置选定对应的数据处理算法，并对数据进行分析，分析完成后返回具体的液面位置数据。The core of the cloud platform is the intelligent analysis of the uploaded liquid level position test data. The specific analysis process is shown in Figure 6. The data processing method of the cloud platform is to receive the sound wave data of the oil well liquid level sent by the wireless local area network host, according to the preset algorithm. Judging whether the data is abnormal (in the actual oil well liquid level test, due to equipment reasons or the influence of the on-site environment, the measured data often cannot be analyzed for the oil well liquid level, these data are called abnormal liquid level test data, resulting in abnormal test data. The specific reasons include insufficient pressure of the gas cylinder, air leakage of the gun body, dirt on the gun body, failure of the main board, poor gas circuit in the field test, and too many bubbles on the liquid surface, etc.) If the data is abnormal, analyze the cause of the abnormality and output the abnormal data. If the data is normal, the corresponding data processing algorithm will be selected according to the test position of the oil well, and the data will be analyzed, and the specific liquid level position data will be returned after the analysis is completed.
判断数据是否异常的预设算法为BP神经网络算法，其如图7所示，BP神经网络是一种前馈神经网络，其主要特点是信号前向传递，误差反向传播。在前向传递中，输入信号从输入层经隐含层逐层处理，直至输出层。每一层的神经元状态只影响下一层神经元状态。如果输出层得不到期望输出，则转入反向传播，根据预测误差调整网络权值和阔值，从而使BP神经网络预测输出不断逼近期望输出。The preset algorithm for judging whether the data is abnormal is the BP neural network algorithm, which is shown in Figure 7. The BP neural network is a feedforward neural network, and its main features are forward signal transmission and error back propagation. In the forward pass, the input signal is processed layer by layer from the input layer through the hidden layer until the output layer. The neuron state of each layer only affects the neuron state of the next layer. If the output layer can't get the expected output, turn to back propagation, adjust the network weights and thresholds according to the prediction error, so that the predicted output of the BP neural network is constantly approaching the expected output.
油井液位数据是一个声波采样序列，整个采样时长为30秒，针对其声学特征，首先将30秒在时域分别依次分成10段，提取每段的负向最大极值，同时再提取声波采样序列的时域正向最大幅值、时域负向最大幅值、频域主峰频率和频域次珠峰频率总共的14个值作为BP神经网络的输入，对应输入层节点变为14。选定正常数据、枪体压力不足、枪体有污垢、主板故障、现场测试气路不畅、液面气泡过多以及其他原因这7个结果作为BP神经网络的输出，对应输出层节点变为7。根据输入输出节点的个数以及经验值，隐含层节点选定18个。The oil well liquid level data is a sequence of acoustic wave sampling. The entire sampling time is 30 seconds. According to its acoustic characteristics, the 30 seconds are firstly divided into 10 segments in the time domain, and the negative maximum extremum of each segment is extracted. At the same time, the acoustic wave samples are extracted. A total of 14 values of the sequence's time-domain positive maximum amplitude, time-domain negative maximum amplitude, frequency-domain main peak frequency and frequency-domain sub-peak frequency are used as the input of the BP neural network, and the corresponding input layer node becomes 14. Select normal data, insufficient gun body pressure, gun body dirt, motherboard failure, poor gas circuit in field test, excessive liquid level bubbles, and other reasons as the output of the BP neural network, and the corresponding output layer node becomes 7. According to the number of input and output nodes and empirical values, 18 hidden layer nodes are selected.
BP神经网络的整体结构便为14-18-7，即输入层有14个节点，隐含层有18个节点，输出层有7个节点。经过上千次的训练后，该网络在实际的运行中能够对复杂数据中的异常数据进行准确的筛选。同时，随着数据库中实测数据的不断积累，该网络会进行定期的对新数据进行训练并更新网络本身，另外，借助云计算的强大算力，后期也会不断地丰富网络结构而使整个智能网络算法更加健壮和精准。The overall structure of the BP neural network is 14-18-7, that is, the input layer has 14 nodes, the hidden layer has 18 nodes, and the output layer has 7 nodes. After thousands of times of training, the network can accurately screen abnormal data in complex data in actual operation. At the same time, with the continuous accumulation of measured data in the database, the network will regularly train new data and update the network itself. In addition, with the powerful computing power of cloud computing, the network structure will be continuously enriched in the later stage to make the whole intelligent The network algorithm is more robust and accurate.
用于分析的数据处理算法为使用幅值测定法、区间最小二乘平滑滤波以及自相关周期估计进行分析输出油井液面深度数据。The data processing algorithm used for analysis is to use the amplitude measurement method, interval least squares smoothing filtering and autocorrelation period estimation to analyze the output oil level depth data.
通过本技术方案，本系统首先将液位仪固定安装于油井的节流管汇等地，并针对长期放置的情形进行相应的保护。同时，基于无线网桥及局域互联技术，在油井现场不同区域布设多个监测终端，测试人员可以在不同区域实现远距离全天候对井下液面位置数据的协同测试。当油井现场出现难以直接判断出准确液面位置的复杂监测数据时，一键将监测数据上传到云端，借助布设在云端的复杂智能算法实现更为精准的液面位置监测。Through the technical solution, the system firstly installs the liquid level gauge on the choke manifold of the oil well and other places, and performs corresponding protection against the long-term placement. At the same time, based on wireless bridges and local interconnection technology, multiple monitoring terminals are deployed in different areas of the oil well site, so that testers can realize long-distance and all-weather collaborative testing of downhole liquid level data in different areas. When complex monitoring data that is difficult to directly determine the exact liquid level position appears on the oil well site, the monitoring data can be uploaded to the cloud with one click, and more accurate liquid level position monitoring can be achieved with the help of complex intelligent algorithms deployed in the cloud.
首先在油井现场选定固定监测位置放置油井液位仪，借助无线网桥和无线路由组建油井局域互联系统，整个系统能够实现油井液位数据的同步测试同步显示，监测终端放置于井场录井仪等室内环境，既可以是电脑也可以是手机，在实时测试以及及时协调方面既方便又快捷。First, select a fixed monitoring position on the oil well site to place the oil well level gauge, and build an oil well local interconnection system with the help of wireless bridges and wireless routing. The indoor environment such as well instrument can be either a computer or a mobile phone, which is convenient and fast in real-time testing and timely coordination.
在解决了测试环节的问题后，油井现场通过互联网上传到云平台的疑难复杂数据就可以通过数据分析服务器来解决。数据分析服务器上布设有智能识别算法，同时，云平台本身又拥有强大的云计算力和云存储量，对于在其上实施复杂算法非常便捷。由于大部分的复杂数据都是无效的异常测试数据，这里首先使用神经网络算法对异常数据进行分类判定，对于正常数据，则根据测试位置的不同，分别使用不同的液位识别算法进行位置判定。After solving the problems in the testing process, the difficult and complex data 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 equipped with intelligent identification algorithms. At the same time, the cloud platform itself has powerful cloud computing power and cloud storage capacity, which is very convenient for implementing complex algorithms on it. Since most of the complex data are invalid abnormal test data, the neural network algorithm is used to classify and determine the abnormal data. For normal data, different liquid level recognition algorithms are used to determine the position according to the different test positions.
整个过程结束后，数据分析服务器再将结果通过互联网传回油井现场的液位仪终端。每次上传上来的复杂数据都会被数据分析服务器保存至数据库服务器，这些数据既方便了技术人员下载进行经验积累，又可以帮助智能分析算法进行迭代升级以使其能更加精准的进行油井动液面判断。After the whole process is over, the data analysis server sends the results back to the liquid level gauge terminal at the oil well site through the Internet. The complex data uploaded every time will be saved to the database server by the data analysis server. These data are not only convenient for technicians to download and accumulate experience, but also help the intelligent analysis algorithm to iteratively upgrade to make it more accurate for oil well dynamic fluid level. judge.
尽管已经示出和描述了本发明的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由所附权利要求及其等同物限定。Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by the appended claims and their equivalents.
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