CN114688009A - Intelligent intermittent pumping control system of beam-pumping unit and control method thereof - Google Patents

Abstract

The invention relates to an intelligent intermediate pumping control system for a beam pumping unit, comprising at least a cloud computing unit (2) capable of processing according to the collected equipment parameters and oil field parameters in the pumping process, the cloud computing unit (2) 2) being able to receive the data information collected by the intelligent sensing unit (4) and subjected to data preprocessing by the edge computing unit (3), wherein the cloud computing unit (2) can receive the data in the manner of establishing a grey model; The real-time data in the pumping process of the oil well is processed, and the data is processed in an orderly manner according to the established gray model, so that the cloud computing unit (2) outputs the interval according to the data sequence generated by the cloud computing unit (2) that can predict the interval pumping period. Pump cycle control strategy. In addition, the present invention also relates to a control method of an intelligent intermediate pumping control system for a beam pumping unit.

Description

A kind of intelligent intermediate pumping control system and control method of beam pumping unit

technical field

The invention relates to the technical field of beam pumping equipment, in particular to an intelligent intermediate pumping control system for a beam pumping unit and a control method thereof.

Background technique

At present, improving the development level of low-yield wells has become one of the main directions of domestic oilfields. The conventional manual start-stop intermittent pumping control technology has problems such as long downtime, difficult management, easy freezing and blocking of wells in winter, and inability to implement the intermittent pumping system. For this reason, the research focus of domestic Daqing Oilfield, Changqing Oilfield, and Shengli Oilfield mainly focuses on technologies such as intelligent extraction pumping unit, intelligent pumping unit control device, and intelligent long-stroke pumping unit. The problem of easy damage, intelligent intermediate pumping needs to add fluid and test equipment, and the one-time investment of long-stroke pumping units is large. There is a lack of low-cost and safe management of pumping technology between pumping units in China.

The high-efficiency intermittent pumping control technology of pumping units developed by Daqing Oilfield realizes downhole pumping by converting conventional long-cycle centralized intermittent oil production into multiple short-cycle intermittent oil production, and combining crank full-cycle operation and swing operation. Stop pumping, ground equipment small shutdown. However, this technology does not have an intelligent design for the change of the actual oil layer liquid supply capacity in the oil field and the movement mechanism of the crank's full-circle movement and swinging alternately. High efficiency and energy saving, but the pumping unit cannot effectively automatically adjust the motion mechanism of the crank alternately performing full-cycle motion and swinging with the change of the oil supply capacity of the oil field after a certain working time.

In order to improve oil production efficiency and reduce energy consumption, most oilfields currently use cloud computing-based digital control systems to optimize the operation of pumping units. Through the digital collection of the key data of the pumping unit, it is transmitted to the cloud computing center for data analysis and processing, and then the instruction is sent to the pumping unit controller at the production site according to the calculation result, and the pumping unit is intelligently controlled. The current digital pumping unit control system has improved the problem of excessive energy consumption of the pumping unit to a certain extent, but it still cannot meet the requirements of the construction of smart oil fields in terms of system reliability, adjustment of pumping unit strokes, and indirect pumping control. demand. At present, low-permeability oil well production has the following characteristics:

(1) Poor reliability: Oil wells generate a large amount of data in a short period of time, which requires a cloud computing center to analyze and process these data and provide real-time responses. However, oilfield production sites are generally located in remote areas where the network is unstable, and a large amount of data transmission will increase the network load, making it difficult to meet the requirements of oil well control systems for reliability and low latency.

(2) Relying on manual experience to determine the interval pumping cycle: low-permeability oil wells have poor liquid supply capacity, and the staff determines the switching time of the pumping wells according to subjective experience, or adopts a fixed-time pumping system of 8 hours or 16 hours. Compared with continuous operation, although intermittent production can reduce energy consumption, if the intermittent pumping system of pumping wells relies on the subjective judgment of oil well staff, it may reduce oil production efficiency and liquid production.

(3) The adjustment of the stroke times does not match the actual supply and production balance: the downhole situation of low-permeability oil wells is complex, and the liquid level of the pumping well fluctuates greatly. Generally, fixed stroke times are adopted for the operation of the pumping unit. In order to ensure the liquid production, the stroke value is generally larger than the reasonable value, resulting in low pump efficiency and large equipment wear.

The patent document with the patent number of CN106285572A discloses an intelligent pumping control device for pumping units, which includes a pumping controller, an electric control box, a displacement sensor, a load sensor, and a wireless antenna; the displacement sensor measures the displacement at the polished rod of the pumping unit and connected to the indirect pumping controller; the load sensor measures the load at the polished rod of the pumping unit and is connected to the indirect pumping controller; the indirect pumping controller is connected to the electric control box, and the indirect pumping controller includes: power supply module, communication module, control module, Power module; the power module collects the current, voltage and power during the working process of the pumping unit and sends it to the control module; the communication module is connected with the wireless antenna, and the control module obtains the start of the pumping unit according to the load and displacement at the polished rod of the pumping unit Stop information and send start and stop commands to the electric control box; the electric control box includes the power supply unit and the frequency converter, and realizes the start and stop of the pumping unit and the adjustment of the stroke times according to the commands of the intermittent pumping controller. However, the device does not accurately adjust the intermediate pumping strategy according to the actual permeability of the low-permeability oilfield, and the acquired control and adjustment parameters have certain delays and errors. Make an optimal and intelligently adjustable interval pumping cycle control strategy.

Therefore, in view of the defects of the prior art, there is a need for an intelligent thinning control system capable of intelligently adjusting the thinning mechanism and the number of strokes to achieve efficient thinning.

In addition, on the one hand, there are differences in the understanding of those skilled in the art; on the other hand, because the inventor has studied a large number of documents and patents when making the present invention, but the space limit does not list all the details and contents in detail, but this is by no means The present invention does not possess the features of the prior art, on the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art.

SUMMARY OF THE INVENTION

In view of the deficiencies of the prior art, the technical solution of the present invention provides an intelligent intermediate pumping control system for beam pumping units, which at least includes a system capable of processing according to the collected equipment parameters and oil field parameters in the pumping process. Cloud computing unit, the cloud computing unit can receive the data information collected by the intelligent perception unit and preprocessed by the edge computing unit, wherein the cloud computing unit can build a gray model for the oil wells it receives. The real-time data in the pumping process is processed, and it predicts the data sequence of the next pumping cycle according to the established gray model, so that the cloud computing unit outputs the control of the pumping cycle according to the data sequence that can predict the pumping cycle. Strategy. The advantage is that the real-time data in the oilfield production process is collected by the sensors deployed by the intelligent perception unit, and then the data is preliminarily processed in the controller at the edge. The edge computing unit deploys some computing functions of the cloud in the intelligent pumping unit controller with limited computing, network and storage functions through the computing offloading strategy. Oil machines and other related equipment can not only reduce network load and transmission delay, but also meet the needs of production equipment for real-time computing functions. At the same time, it can also perform abnormal detection and filtering of data at the edge, reducing the transmission and storage of a large amount of invalid data in the traditional cloud mode, thereby improving the system's response speed to equipment, and ultimately achieving the goal of efficient and safe production. In addition, the cloud computing unit realizes the prediction of the system with incomplete information acquisition through the established gray model. By studying the similarities and differences of the development trends of different factors in the system under the interaction, the degree of correlation between the factors is obtained. The acquired data is processed accordingly, and the disordered data is transformed into a regular data sequence, so as to predict the future development trend of things according to the laws presented by the generated data. Usually, the data obtained by the system needs to meet the requirements of equidistant and reflect the characteristics of the system, and the eigenvalues of the future system are predicted by establishing a gray model for a certain amount of data.

According to a preferred embodiment, the cloud computing unit cooperates with the edge computing unit, wherein the edge computing unit receives the operating parameters of the pumping unit acquired by the intelligent sensing unit and Oilfield parameters in the process, and preprocessing the received data information, so that the cloud computing unit can obtain the data information from which invalid data has been screened out.

According to a preferred embodiment, the cloud computing unit completes the calculation and processing of the oilfield dynamometer data according to the data information uploaded by the edge computing unit, and establishes a gray model related to the submersion degree, so as to obtain the dynamometer according to the obtained gray model. The data and the established grey model output the pumping unit interval control strategy suitable for the actual seepage situation of the oil field. Its advantage is that the oilfield full pumping area, stop pumping area, stop pumping time and pumping time and other parameters are calculated by using the dynamometer diagram outputted by the data information collected after the pumping unit completes a stage of pumping work. And according to the prediction results of the submersion degree output by the established grey model, the control strategy of the pumping unit's interval pumping cycle in the next stage of pumping work is output, which eliminates the defect that the control strategy obtained by a single analysis method cannot match the actual oil field swabbing parameters. , through the integration and mutual verification of the relevant data of the two control strategies, the system can output a control strategy with higher accuracy.

According to a preferred embodiment, the cloud computing unit can also create a fuzzy rule that can adjust the balance degree of the pumping unit according to the received data information, so that the pumping unit can The fuzzy rules established by the cloud computing unit are used to carry out self-balancing oil pumping work. The advantage lies in that, according to the characteristic that the intelligent control system of the pumping unit can adjust the balance degree when the pumping unit is working, the input quantity of the system is the balance degree deviation and the balance degree deviation change rate. The control output of the corresponding self-balancing pumping unit is obtained by analyzing and calculating the two, which constitutes the total control look-up table of the balance degree. The balance of the beam pumping unit is adjusted according to the two input quantities, so that the intelligent control system has better and stable dynamic performance.

According to a preferred embodiment, the cloud computing unit updates the dynamometer diagram and the grey model established by the cloud computing unit in real time according to the received data information, so as to obtain the swept area data of the dynamometer diagram and the subsidence degree data predicted by the grey model. The control strategy of the interval pumping period of the pumping unit in the next working stage is adjusted. Fuzzy control does not need to establish an accurate mathematical model, and can simplify the complexity of the control system, and can also have strong robustness. Therefore, the use of fuzzy control algorithm can better solve the complexity of nonlinear control system.

According to a preferred embodiment, the pumping motor works normally when the crank of the oil pumping unit operates in a full circle, and the control strategy of the interval pumping period of the oil pumping unit at least includes adjusting the crank to alternately perform full circle motion and swing. Movement mechanism and control how much the crank swings during the swing.

According to a preferred embodiment, the pumping unit includes at least a pumping motor, an intelligent pumping unit controller, and a balancing unit, wherein the intelligent pumping unit controller can be based on the latest output from the cloud computing unit The interval pumping cycle control strategy adjusts the working state of the oil-pumping motor, so as to complete the automatic adjustment of the stroke times, the intelligent interval-pump control and the automatic adjustment of the balance of the oil-pumping mechanism.

According to a preferred embodiment, the intelligent pumping unit controller controls the pumping motor to work at the rated speed during the whole cycle of the crank; the stroke loss of the oil well is calculated according to the polished rod dynamometer diagram, and when the polished rod movement length is less than the stroke loss When the crank rotates once and the polished rod completes a cycle of reciprocating operation, the crank swing angle is calculated according to the motion length of the polished rod; the minimum value point of the power during the swing process is monitored according to the electric power curve collected in real time, and the maximum value is determined. best drive position.

The present application also provides a control method for an intelligent intermediate pumping control system for a beam pumping unit, which at least includes the following steps: using an intelligent sensing unit to collect three-phase electrical parameters and various sensor signals of the pumping unit, and using a communication module to The collected data is transmitted to the edge computing unit; the collected data information is preprocessed by the edge computing unit and further uploaded to the cloud computing unit; the cloud computing unit is used to analyze the received data, and the obtained data and control signals are downloaded. Send it to the intelligent pumping unit controller to realize the stroke adjustment, self-balancing adjustment of the pumping unit and display through the control panel.

According to a preferred embodiment, the cloud computing unit and the edge computing unit perform data transmission and control signal delivery through a communication module.

Description of drawings

FIG. 1 is a schematic diagram of the work flow of an intelligent intermediate pumping control system for a beam pumping unit of the present invention.

List of reference signs

1: pumping unit; 2: cloud computing unit; 3: edge computing unit; 4: intelligent sensing unit; 5: communication module; 6: control panel; 11: pumping motor; 12: intelligent pumping unit controller; 13 : Balance unit.

Detailed ways

The following detailed description is given in conjunction with the accompanying drawings.

Low-permeability oilfields generally refer to oilfields with insufficient formation liquid supply capacity. Due to the poor continuity, low permeability and low porosity of the oil layer, during the oil production process of the oil well, the downhole liquid level cannot be quickly restored to the reasonable range of production, which makes the oil pump appear liquid. The phenomenon of face impact and emptying.

(1) Liquid level impact

Liquid level impact refers to the phenomenon that the oil well pump damages the equipment due to the shock wave caused by the pump being not full during the oil extraction process. Since the pump cannot reach full pumping during the upstroke, the pressure inside the pump is lower than the pressure at full pumping, resulting in a late opening of the traveling valve during the downstroke. At this time, when the swimming valve cannot be opened normally, the downward acceleration of the sucker rod will generate shock waves and cause damage to the equipment.

(2) Evacuation

In low permeability oilfields, the phenomenon of insufficient liquid supply will generally occur in the middle and late stages of production, that is, the liquid supply capacity of the formation does not match the output capacity of the oil well pump, resulting in the inefficient operation of the oil pumping unit, and the phenomenon of unsatisfactory pumping and empty pumping of the oil well pump. , which will not only increase the wear and tear of the equipment and increase the accident rate, but also increase the waste of electric energy.

(3) The liquid level fluctuates greatly

The formation environment of low-permeability oilfields is complex, and there are many influencing factors. During the oil production process of oil wells, the recovery of downhole liquid level is often irregular and fluctuates greatly. It has a great impact on the formulation of the pumping unit operation system, and the set stroke value cannot make the pump efficiency within a reasonable production range.

Therefore, determining the reasonable switching time of the pumping unit and the reasonable stroke value during the operation are the key issues to realize the cost reduction and efficiency increase of the oil field. The adjustment of the pumping unit needs to meet the following conditions:

(1) The liquid supply capacity is insufficient. When the downhole liquid supply capacity is insufficient, the pumping unit stops.

(2) The liquid supply capacity is normal and the pump fullness is low. When the downhole liquid supply capacity is normal, but the pump efficiency is low, the stroke value of the pumping unit is reduced, so that the pump fullness is within a reasonable range and the power consumption is reduced.

(3) The liquid supply capacity is normal and the pump fullness is high. When the downhole liquid supply capacity is normal, but the pump fullness is high, increase the stroke value of the pumping unit, so that the fullness of the pumping unit is in a reasonable range and increase the liquid production.

Example 1

The present application provides an intelligent intermediate pumping control system for a beam pumping unit, which may include a pumping unit 1 , a cloud computing unit 2 , an edge computing unit 3 , and an intelligent sensing unit 4 .

According to a specific embodiment shown in FIG. 1 , an intelligent sensing unit 4 is installed in the pumping unit 1 and its connected oil well. The oil pumping unit 1 is also provided with an edge computing unit 3 capable of preprocessing the data information collected by the intelligent sensing unit 4 . The edge computing unit 3 can transmit the preprocessed data information to the cloud computing unit 2 . The cloud computing unit 2 can more specifically calculate and analyze the received preprocessed data information. The introduction of the edge computing unit 3 can reduce the delay of data transmission and the computing pressure of the cloud computing unit 2, improve the processing speed of tasks, and meet the real-time requirements of the Internet of Things system.

Preferably, the cloud computing unit 2 can analyze, process and display the complex data of oil wells, and provide functions such as alarm monitoring, abnormal data filtering and equipment management. Reliable control.

Preferably, the edge computing unit 3 has the function of a gateway, which can analyze, process and store data downwards, and upload data upwards to the cloud computing unit 2 in the cloud. The edge computing unit 3 also provides limited function computing functions and facilitates the control of business scenarios. With the in-depth exploration and wide application of big data and IoT technologies in the oilfield production industry, the number of devices connected to the oilfield IoT has surged, and the data generated by oilfield production has grown exponentially. The traditional oilfield Internet of Things technology centralizes computing, storage and network resources, and the cloud computing center provides services to the oilfield production site, making efficient use of existing resources. However, with the continuous development of oilfield data services, the oilfield IoT system needs to ensure the real-time and reliability of massive data. However, it is difficult to achieve low-latency response with cloud computing, a central data processing mode, and will aggravate network problems. load, which affects the effective processing of oil well data services. By applying edge computing technology to the oilfield Internet of Things, part of the computing and storage functions are delegated to the nodes at the edge of the network, so that part of the business can be processed at the edge of the network, which can not only improve the response speed of the control system to equipment, but also reduce a large number of The need to upload data to the cloud reduces the system's dependence on network stability. The real-time data of oilfield production is collected by the sensors deployed by the intelligent sensing unit 4, and then the edge computing unit 3 in the edge controller performs preliminary processing on the data to provide real-time response for the production equipment at the edge. Preferably, the edge computing unit 3 deploys part of the computing function of the cloud in the intelligent pumping unit controller with limited computing, network and storage functions through the calculation offloading strategy. Sensing equipment, pumping units and other related equipment can not only reduce network load and transmission delay, but also meet the needs of production equipment for real-time computing functions. At the same time, the edge computing unit 3 can also perform anomaly detection, filtering and other processing on the data at the edge, reducing the transmission and storage of a large amount of invalid data in the traditional cloud mode, thereby improving the response speed of the system to the device, and finally achieving the goal of efficient and safe production.

Preferably, the intelligent sensing unit 4 generally includes a speed sensor, a vibration sensor, a temperature sensor, a pressure sensor, a dynamometer sensor, an intelligent controller, an electrical parameter module, a motor speed sensor, and the like. The electric parameter module collects parameters such as motor running current, voltage and frequency, and monitors the driving force in real time; the motor speed sensor monitors the motor operation according to the collected motor rotation angle; the temperature, pressure and vibration sensors mainly reflect the equipment and downhole when the pumping unit 1 is running. The dynamometer sensor mainly collects the parameters needed to generate the dynamometer. The intelligent sensing unit 4 communicates with the edge computing unit 3 mainly through a combination of wireless and wired.

Preferably, the cloud computing unit 2 can formulate and adjust the interval control strategy based on the gray system for the received data information preprocessed by the edge computing unit 3 . The gray system improves the understanding of the system by analyzing and processing the data information obtained by the cloud computing unit 2 . The gray system is based on the gray model, and its main contents include gray prediction, gray decision-making, gray statistics, and gray clustering. Grey prediction is mainly to predict the system with incomplete information acquisition. By studying the similarities and differences of the development trend of different factors in the system under the interaction, the degree of correlation between the factors is obtained. The acquired data is processed accordingly, and the disordered and disordered data is transformed into a regular data sequence, so that the future development trend of things can be predicted according to the law presented by the generated data, and the generated data can represent the time period of the next pumping cycle. The data series of the working state and oil storage state of the pumping unit and the oil field at different time points. The data sequence may be the operating parameters of the pumping unit 1 and the oil storage parameters of the oil well within a certain period of time. Usually, the data acquired by the system needs to meet the requirements of equidistant and can reflect the characteristics of the system, and a gray model capable of gray prediction is established through a certain amount of data acquired to predict the characteristic value of the system. The main basis of the gray system is information coverage. The essence of information coverage is the aggregation of partial information, which is characterized by the use of a small amount of incomplete data to complete gray dynamic modeling. The data requirements are at least 3, and the data must be distributed arbitrarily and with regularity. .

The downhole environment of oil wells is complex, and the intermittent pumping period is affected by factors such as fluid, oil layer, and operation mode. During the interval pumping period, the staff needs to regularly detect the downhole liquid level as the basis for judging the interval pumping period. Affected by the complex underground environment, the dynamic liquid level data cannot accurately reflect the downhole liquid supply capacity, which is poor information and has a small amount of data. The gray system theory has a good effect on this type of data, so it is a good choice for the formulation of the control strategy of the interval pumping cycle of oil wells. The subsidence degree is affected by factors such as permeability, formation energy, temperature, etc. Due to the uncertainty of each factor, the subsidence degree influencing factor has the characteristics of gray coverage. In the process of oil production, the subsidence degree is specific and deterministic at each time point, and has the characteristics of white information coverage. Therefore, the grey prediction model can be established according to the subsidence degree during the shut-in period as the original data.

In the actual production process, the subsidence degree data has small changes and a small amount of data, which meets the characteristics requirements of the gray model for data. Modeling based on submersion has high accuracy and small error. Therefore, it is feasible to establish a grey model based on the submersion degree of the pumping unit 1 during its operation. The cloud computing unit 2 can predict the working state change between the pumping unit 1 and the oil well in the next pumping cycle according to the established grey prediction model, and then output the control strategy of the pumping unit 1 between the pumping cycle. Compared with the problems of low work efficiency, waste of electric energy and real-time control lag in the operation of traditional oil pumping equipment in low permeability oil fields, the intelligent oil pumping unit control system operating in low permeability oil fields provided by this application has the following advantages:

(1) The system is real-time. The system can process and analyze the relevant data of the pumping unit 1 in real time, and control the pumping unit 1 in real time according to the analysis results obtained on the spot.

(2) Intelligent pumping control. The system can carry out scientific and reasonable intermittent pumping control on the pumping unit 1 according to the change of the downhole liquid supply capacity.

(3) Stroke adjustment control. The system can reasonably adjust the stroke times of the pumping unit 1 according to the pumping efficiency of the pumping unit 1 so as to improve the operating efficiency of the pumping unit 1 .

(4) Balance control. The system can judge the balance of the pumping unit 1 and automatically adjust it according to the operating state of the pumping unit 1 .

In the initial stage of production of the low-permeability oil well, the cloud computing unit 2 calculates the working strategy of the pumping unit 1 according to the pre-acquired parameters of the underground oil field. During the indirect pumping production stage of the low-permeability oil well, the intelligent pumping unit controller 12 of the pumping unit 1 receives the optimal operating frequency value of the pumping motor 11 of the pumping unit 1 from the cloud computing unit 2, and then drives the pumping unit 1 with this frequency value. The pumping motor 11 operates. The oil pumping motor 11 drives the four-bar linkage structure of the pumping unit 1 to perform periodic mechanical motion, and the four-bar linkage mechanism drives the oil well pump to complete the swabbing cycle and draw crude oil to the ground. During the operation of the pumping unit 1 , the intelligent sensing unit 4 converts various physical signals sensed into electrical signals and transmits them to the edge computing unit 3 for preprocessing such as data filtering. The edge computing unit 3 uploads the preprocessed data information to the cloud computing unit 2 for further processing and analysis. The cloud computing unit 2 uses the received dynamometer data and gray power model data to calculate the optimal stroke and interval pumping cycle control strategy of the pumping unit 1, and then drives the pumping motor 11 through the intelligent pumping unit controller 12 according to Work with the latest operating parameters. The cloud computing unit 2 can also complete the calculation of the balance of the pumping unit 1 by analyzing the electrical parameters of the pumping motor 11 , and then adjust the balance of the pumping unit 1 by controlling the balancing unit 13 .

Preferably, the cloud computing unit 2 can control the intelligent sensing unit 4 to complete the three-phase electrical parameters of the pumping unit 1, the current data of the pumping motor 11 and the balancing unit 13, the dynamometer data, and the data on the pumping unit 1 and the oil well. The acquisition of parameters within the sensor. Preferably, the sensor parameters may include real-time data information monitored by sensors such as angular displacement sensors, load sensors, and pressure sensors, as well as data such as wellhead temperature and flow rate in the oil field, so that the stroke, interval, and time of the pumping unit 1 can be calculated according to the above data information. Important production data such as pumping time, balance, power factor, power consumption per ton of oil, and active power saving rate of a single well. In addition, by using sensors to monitor the oil field in real time, it is also possible to obtain data information that can be used to calculate the dynamometer diagram and subsidence degree and other data that can characterize the oil field permeability. Preferably, the real-time updated interval pumping cycle control strategy calculated by the cloud computing unit 2 refers to the real-time dynamometer data collected and the operating parameters of the pumping unit 1 (for example, the number of strokes, the interval pumping time, the degree of balance, Whether there is a mismatch between the power factor, electricity consumption per ton of oil, and the active power saving rate of a single well) to the pumping unit parameters and oilfield parameters, or whether there is a mismatch between the set pumping unit parameters and oilfield parameters in the next pumping cycle According to the judgment result, the operation parameters of the subsequent working time of the pumping unit 1 are outputted in a targeted manner. Preferably, the oil field parameters include real-time changes in the permeability rate, oil storage volume, pressure, temperature and the like in the oil field. Preferably, the control strategy output by the cloud computing unit 2 refers to a pumping process in which the operating parameters of the pumping unit 1 are continuously changed within an interval pumping period, and the operating parameters of the pumping unit 1 include The number of strokes, duration of pumping and balance in a pumping cycle can be adjusted and set. Preferably, the existing oil field is generally in a low permeability state, therefore, the pumping unit 1 needs to stop the swabbing operation after a certain period of time, so that the crude oil in the oil field can slowly seep out of the formation to restore the oil amount in the oil field. In addition, during the pumping process, the pumping unit 1 also needs to adjust the stroke of the polished rod according to the demand, so that the pumping unit 1 can adjust the time rod for each full-cycle movement of the crank as the oil storage capacity of the oil field decreases within a period of time. strokes to avoid inefficient operation such as empty pumping. Specifically, the stroke of the polished rod is gradually shortened as the oil storage in the oil field decreases. The cloud computing unit 2 can intelligently analyze the collected dynamometer data and control the operating parameters of the pumping unit 1, so that the pumping unit 1 and the oil well parameters can be dynamically matched, so that the pumping unit 1 is always in the best operating state. And it can cooperate with the host computer monitoring configuration software and the dynamometer intelligent working condition diagnosis and dynamometer oil measurement system of the Oil and Gas Technology Research Institute to realize oil measurement, fault diagnosis and intelligent control.

Preferably, the cloud computing unit 2 can control the intelligent sensing unit 4 to complete the collection of the three-phase electrical parameters of the pumping unit 1, the current data of the pumping motor 11 and the balancing unit 13, the dynamometer data and other sensor parameters. The cloud computing unit 2 can also process the working condition data collected in real time through the on-chip DSP engine of its main control chip, and control the parameters of external devices such as the intelligent pumping unit controller 12 through the on-chip MCU unit, and finally realize Automatic adjustment of stroke times, intelligent intermediate pumping control and automatic adjustment of balance of pumping unit 1. Preferably, the cloud computing unit 2 calculates and obtains dynamometer diagrams of multiple continuous intermittent pumping cycles according to the data information collected by the load sensor and the angular displacement sensor for many times in a row, and obtains multiple sets of gradually changing full pumping cycles from the dynamometer diagram. Area, stop pumping area, stop pumping time and pumping time and other parameters to predict the above parameters of the next pumping cycle, so as to predict the pumping unit according to the predicted full pumping area, stop pumping area, stop pumping time and pumping time The operating parameters of 1 are adjusted and the latest control strategy is generated, and then the pumping unit 1 adjusts the working state of the pumping motor 11 according to the control strategy including its operating parameters sent by the cloud computing unit 2, so that the pumping unit 1 can always Maintain the matching between its pumping parameters and the oil parameters of the oil field to avoid empty pumping and other phenomena. Preferably, the automatic adjustment of the stroke times, the intelligent thinning control and the automatic adjustment of the balance degree of the pumping unit 1 are to adjust the stroke and crank of the polished rod according to the real-time change of the operating parameter data of the pumping unit 1 in different time periods. the swing angle and the operation of the balancing motor. The cloud computing unit 2 generates a control strategy after predicting the matching situation between the operating parameters of the pumping unit 1 and the oil well parameters in the next pumping cycle according to the historical data of a plurality of continuous dynamometer diagrams on the time axis, and based on the intelligent perception The sensor monitoring data obtained in real time by the unit 4 in this interval pumping period verifies the predicted control strategy, updates the control strategy according to the verification result, and adjusts the subsequent pumping of the pumping unit 1 in the same interval pumping cycle according to the updated control strategy. Operating parameters for the time period. The measurement and control terminal will predict the dynamometer diagram of the next pumping cycle according to the historical data, and according to the output of the predicted dynamometer map, it can pre-adjust the control strategy of the operating parameters of the pumping unit 1 at different time points in this pumping cycle, so as to A swabbing process that is continuously updated based on historical data is established. During the swabbing process, the measurement and control terminal can also generate a real-time power indicator according to the actual collected oilfield parameters and the actual operating parameters of the pumping unit 1. The graph is compared and verified with the predicted dynamometer, so as to output the updated control strategy according to the difference between the real-time dynamometer and the predicted dynamometer, so as to adjust the remaining time of the pumping unit 1 during the pumping cycle according to the control strategy The operating parameters of the segment.

Preferably, the balance of the pumping unit 1 is a key parameter for the stable operation of the pumping unit 1, and a good control strategy can stably control the balance of the self-balancing pumping unit. Due to the different geological characteristics and formation liquid supply capacity, the pumping unit 1 is driven by the high speed of the drive motor and undergoes energy conversion by the reduction box, so that the energy is transferred to the donkey head, so that the oil rod reciprocates up and down. During the normal operation of the beam-type self-balancing pumping unit, when the balance motor is working, the changes of the parameters of the pumping unit 1 are all nonlinear changes, and the rotation of the balance arm of the pumping unit 1 is also complicated. An accurate mathematical model is more difficult and requires higher robustness of the system. Fuzzy control can meet the requirements of the above-mentioned pumping unit 1. As a branch of intelligent control, fuzzy control mainly has the following characteristics: (1) It does not need to establish an accurate mathematical model; (2) It can simplify the complexity of the control system; ( 3) It has strong robustness. Therefore, the use of fuzzy control algorithm can better solve the complexity of nonlinear control system.

Preferably, the balance of the pumping unit 1 is affected by various factors, and the use of fuzzy control can well control the balance of the beam pumping unit. Fuzzy control requires a total of three important steps, namely fuzzification, fuzzy rule design and reasoning and defuzzification. In the specific design, two input quantities are usually used, namely deviation and deviation change rate. The balance of the beam pumping unit can be adjusted, and the intelligent control system can have better and stable dynamic performance. According to the characteristic that the digital beam-type self-balancing pumping unit intelligent control system can control the balance degree of the pumping unit 1, the input quantities of the system are the balance degree deviation e and the balance degree deviation change rate ec. After analyzing and calculating the two, the corresponding control output of the self-balancing pumping unit is obtained, which constitutes the total control look-up table of the balance degree.

Specifically, the cloud computing unit 2 obtains the corresponding balance control signal u after analyzing and calculating through the fuzzy control algorithm, and sends it to the well site pumping unit 1 . The cloud computing unit 2 converts the balance control signal u into the control signal of the motor, controls the balance adjustment circuit of the self-balancing oil pumping unit by controlling the pull-in of the relay, and then controls the balance adjustment unit 13 to adjust the position of the swing arm type balance arm to perform The balance adjustment of the digital beam-type self-balancing pumping unit makes the digital beam-type self-balancing pumping unit reach an ideal balance state.

Preferably, when the system is initialized, data such as the balance degree of the upper monitoring platform and the control signal of the balance degree of the self-balancing pumping unit are received, and the balance degree is judged. If the balance degree is not within the set range, the function is called, and the The control signal value is converted into pulse control signal output, and the Forward and Reverse functions are called to control the solid state relay to act, and then control the balance motor to act. .

During the whole cycle operation of the crank, the oil well pump works normally, and the motor runs at the rated speed; when the oil pumping time arrives, it enters the crank swing operation phase, the oil well pump stops working, the sucker rod moves up and down with the preset stroke, and the crank rotates at the specified stroke. There is no impact and flexible reciprocating swing within the range. During the swing, the energy consumption of the motor is 10% of the normal operation. When the swing time is reached, the recirculation enters the full cycle operation stage.

During the full cycle of the crank, the control system controls the motor to work at the rated speed.

In the crank swing operation stage, the control goal is to minimize energy consumption, and at the same time, it is necessary to meet the signs of crank swing, that is, during the crank swing process, the crank swing amplitude is a gradual change, and the crank swing amplitude can be close to 0° or 360°.

For the swing angle, first calculate the stroke loss of the oil well according to the indicator diagram of the polished rod. The motion length of the polished rod is less than the stroke loss, which satisfies the situation that the sucker rod moves up and down and the oil pump stops working; the crank rotates 360 degrees once, and the polished rod completes a cycle of reciprocating operation. Calculate the crank swing angle based on the polished rod motion length.

For the optimal swing position, due to the different balance states of each well, the minimum power value point during the swing process is not necessarily at the top dead center of the polished rod. best drive position.

The calculation principle of the actual speed n of the motor is:

n=(1-s)60f/p.

Among them, s is the slip ratio of the three-phase asynchronous motor, p is the number of pole pairs of the motor, and f is the frequency of the motor stator power supply. The change of n and f is positively correlated, among them, the function of the frequency converter is mainly to use the on-off semiconductor device to act on the alternating current of fixed frequency, and change the alternating current of fixed frequency into alternating current of adjustable frequency.

The period from 0 to t1 is the whole cycle operation stage of the crank of the pumping unit 1. After the motor accelerates to the rated speed, it starts to decelerate to 0. The period from t1 to t2 is the crank swing operation stage of the pumping unit 1. , the motor starts to accelerate until it reaches a certain set value and then decelerates, and the motor accelerates in the reverse direction until it reaches a certain set value and then decelerates, and so on.

The crank of the beam pumping unit does a regular or irregular reciprocating motion in a non-full circle. Regular movement means that the crank reciprocates forward and reverse in both directions according to a fixed swing amplitude and swing period. Irregular motion means that the swing amplitude of the crank changes within the allowable range or the swing period of the crank changes.

The above-mentioned movement mode of the pumping unit 1 has the following advantages:

First, since the dead point is at or close to the minimum load point of the crank, the crank does a irregular or irregular movement around the dead point, which can reduce the motor load driving the crank movement;

Second, in this case, within the operating range of the suspension point, the swing of the crank is relatively large, which can provide an eye-catching reminder that the pumping unit 1 is running;

Third, since the crank does a non-circumferential bidirectional reciprocating motion around the dead point, the crank can achieve the shortest suspension point running length per unit rotation angle, so it is more conducive to realize the precise control of the suspension point position.

In the present application, the final position where the pumping unit 1 can stably stop under completely free sliding conditions without driving or braking is regarded as a stable equilibrium position. When the pumping unit 1 deviates from this position, as long as there is external force disturbance, the pumping unit 1 will be prompted to return to the stable equilibrium position. When the pumping unit 1 is in a stable equilibrium position, the sum of the gravitational potential energy of the surface mechanical part, the gravitational potential energy of the downhole liquid column and the elastic potential energy of the downhole rod and pipe string is the smallest. The above-mentioned limitation on the stable equilibrium position of the crank can realize the minimum driving power consumption required for the reciprocating motion of the crank, and the need to provide a reverse braking torque to ensure that the movement of the crank and the suspension point does not exceed the limit. wear and fatigue and impact of mechanical transmission components.

Further defining regular or irregular crank motion: Regular or irregular crank motion includes pauses with an intermittent character. This limitation can further reduce drive energy consumption, motor energy consumption and mechanical losses.

The value of the elastic static deformation length changes with the change of the downhole subsidence pressure. Under the condition of the existing production equipment, since the downhole subsidence pressure cannot be collected in real time, in the production practice process, the elastic static deformation length of the downhole rod and pipe string needs to be determined according to the subsidence pressure. The empirical value of , that is, the static elastic deformation length of the downhole rod and pipe string is the value of the elastic static deformation length of the downhole rod and pipe string corresponding to the empirical value of the submerged pressure.

There is a regular movement for the crank: the most typical movement method is to do forward and reverse bidirectional reciprocating motion according to a relatively fixed swing amplitude and swing frequency. For crank random motion: it includes not only the situation where the swing amplitude changes within the allowable range and the swing frequency changes, but also the situation where a short stop is added during the swing process, because the stop state belongs to a special movement with a speed of 0.

Example 2

This embodiment is a further improvement to Embodiment 1, and repeated content will not be repeated.

The basis of the grey forecasting model is the first-order univariate grey forecasting model, which is mainly constructed for the evolution characteristics of the main variables of the system. The first-order univariate gray prediction model has high modeling accuracy and requires a small amount of data, which is suitable for sequence modeling. The law of dynamic fluid level change in pumping unit well satisfies the characteristics of the first-order univariate gray prediction model.

There is a certain limit to the reduction of 1 stroke of the pumping unit. For oil wells with serious "empty pumping", it is necessary to consider the use of indirect pumping control, that is, "pump when there is oil, and stop when there is no oil". For such an indirect pumping well, when the pumping is restarted after stopping the pumping for a sufficient time, the dynamic liquid level is higher and the pumping efficiency is higher, which is reflected on the indicator diagram, which shows that the area of the indicator diagram is full, which is close to the theoretical indicator diagram. , so the area of the indicator diagram is larger. With the continuation of the pumping time, the downhole liquid supply speed cannot catch up with the pumping speed of the pumping unit 1, the dynamic liquid level gradually decreases, the pumping efficiency gradually decreases, the indicator diagram becomes shriveled, and the area decreases. When the area of the dynamometer diagram is reduced to a certain extent, and the change is not large, the oil supply to the oil well has been seriously insufficient, and the phenomenon of "empty pumping" begins. The key to realizing the above process is when to stop pumping and how long to stop pumping, that is, the determination of stop pumping point and stop pumping time.

Due to the influence of geological conditions and other factors, the uncertainty of downhole working conditions is relatively large. half a year), these parameters may no longer be suitable for oil well conditions, so these four parameters must be dynamically changed according to oil well conditions. The average value of the area of the dynamometer chart within the first 10 minutes after the system is turned on after the amount of follow-up fluid is sufficient. The stop pumping area (St) means that when the area of the dynamometer diagram is lower than the pump stop area (St) for a long period of time, the pumping stop is started. The duration of stopping drawing (Ts) refers to the length of time for stopping drawing. The pumping time (Cs) refers to the time from restarting pumping to stopping pumping again after stopping pumping.

When the control system is put into production operation for the first time, according to the actual production data of the oilfield, four parameters of the full pumping area Sm, the stop pumping area St, the stop pumping time Ts and the pumping time Cs are manually set, for example, the full pumping area is set to 50KN *m, the stop pumping area is 18KN*m, the stop pumping time is 2 hours, and the pumping time is 1 hour. After starting the pumping, compare the average value S _AV of the dynamometer area in the first 10 minutes with the full pumping area. If the average value S _AV of the dynamometer area in the first 10 minutes is greater than 70% of the full pumping area, Then in the next interval pumping cycle, increase the pumping time by △t, and decrease the pumping stop time by △t; if the average value of the area of the indicator diagram in the first 10 minutes S _AV is less than 70% of the full pumping area, the next Reduce the pumping time by △t and increase the stop pumping time by △t in each interval pumping cycle; if the average value of the area of the dynamometer chart within the first 10 minutes _SAV is equal to 70% of the full pumping area, the original parameters will be maintained constant. After that, the average area of the dynamometer is calculated every 10 minutes as the measured dynamometer area S. When S≤St*(1+10%), the pumping will be stopped. smoke.

In the process of calculating the area of the dynamometer diagram every 10 minutes, if the difference between the areas is within 10% for more than 2 consecutive hours, it is considered that a new area for stopping pumping has been found, and the data St is updated. If the average value of the area of the dynamometer within 10 minutes at the beginning of each startup is greater than or equal to 110% of the full pumping area for more than two consecutive hours, it is considered that a new full pumping area has been found, and the data Sm is updated accordingly. So far, the data Sm, St, Ts and Cs have been dynamically updated.

Example 3

There are three main control aspects in the process of oil production, namely remote monitoring of beam pumping units, self-balancing adjustment and automatic adjustment of stroke times.

The intelligent control system of digital beam self-balancing pumping unit mainly includes digital self-balancing pumping unit, digital intelligent pumping unit control cabinet and upper background monitoring equipment.

First, the load signal, displacement signal and three-phase electrical parameter data of the beam pumping unit are collected through the load, angular displacement sensor and three-phase electrical parameter acquisition module, and the collected data is stored in the external memory of the intelligent controller. Upload it to the monitoring platform and use the algorithm to analyze and calculate the balance, current stroke and other data of the beam self-balancing pumping unit, store the data in the database, and then send the data and control signals to the digital intelligent controller Balance adjustment and control of beam type self-balancing pumping units, etc.

1. Remote monitoring of pumping unit 1, capable of remotely operating the starting and stopping of pumping unit 1, and real-time display of background data.

2. Stroke time control, calculate the pumping efficiency of the beam pumping unit according to the collected load data, and then judge the running state of the pumping unit 1, and adjust the pulsation time in real time.

3. For self-balancing control, it is necessary to adopt intelligent control algorithm combined with intelligent control system, so that the pumping unit 1 always operates within the optimal balance range.

Example 4

The present application also provides a control method for an intelligent intermediate pumping control system for a beam pumping unit. When the pumping unit 1 starts to run, the oil production control method may include the following steps:

The intelligent sensing unit 4 collects the three-phase electrical parameters of the pumping unit 1 and various sensor signals, and uploads the collected data to the edge computing unit 3 through the communication module 5;

The edge computing unit 3 further transmits the collected data signal to the cloud computing unit 2 after preprocessing;

The cloud computing unit 2 analyzes the received data, and sends the obtained data and control signals to corresponding modules to realize stroke adjustment, self-balancing adjustment and display on the control panel 6 of the pumping unit 1 .

Preferably, the cloud computing unit 2 transmits data and issues control signals through the communication module 5 .

The above-mentioned control method of the intelligent intermediate pumping control system for the beam pumping unit can have the following functions and advantages:

(1) Real-time acquisition and transmission of oil well parameters and electrical parameters;

(2) It has the functions of receiving the command of the upper computer, realizing the remote start and stop of the pumping unit 1, voice prompt and alarm;

(3) It has the function of automatic determination and adjustment of the balance of the pumping unit 1. Automatically monitor and display the balance status of the pumping unit 1 in real time, manually or automatically adjust the pumping unit 1 to the best balance status, reduce the peak current, and achieve the purpose of protecting the reducer and saving energy;

(4) It has the function of automatic determination and adjustment of the best working stroke of the pumping unit 1. Real-time display of the stroke times of the pumping unit 1. It is possible to manually adjust the number of strokes steplessly, or automatically determine the optimal number of strokes according to the liquid production volume of the oil well. Under the condition of ensuring the liquid production volume, the number of strokes can be adjusted to the minimum to achieve the best energy-saving effect;

(5) It has the intelligent intermediate pumping function of the pumping unit 1 . For intermediate pumping wells, the intermediate pumping system can be manually set, and the optimal intermediate pumping system can be automatically formulated according to the changing trend of liquid production in the oil well, so as to realize the function of “pumping when there is oil, and stopping when there is no oil”. On the premise of the amount of water, the effective energy saving of the indirect pumping well is realized;

(6) With power frequency/frequency conversion automatic switching function. When the inverter fails, it can automatically switch to the power frequency mode to run;

(7) With soft start and overload protection functions. It can effectively reduce the starting inertial load, and combined with the optimal balance judgment and adjustment technology, the installed power of the pumping unit 1 is generally lowered by one gear, and the capacity of the well site power grid is greatly reduced.

It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the scope of the present invention. within the scope of protection of the invention. It should be understood by those skilled in the art that the description of the present invention and the accompanying drawings are illustrative rather than limiting to the claims. The protection scope of the present invention is defined by the claims and their equivalents. In the whole text, the features introduced by "preferably" are only an optional way, and should not be construed as a mandatory setting, so the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims (10)

1. a beam pumping unit intelligent pumping control system, comprising at least the cloud computing unit (2) that can be processed according to the equipment parameters and oil field parameters in the pumping process collected, it is characterized in that, the cloud computing The unit (2) is capable of receiving data information collected by the intelligent sensing unit (4) and subjected to data preprocessing by the edge computing unit (3), wherein, The cloud computing unit (2) can process the received real-time data during the pumping process of the oil well according to the method of establishing a gray model, and predict the data sequence of the next pumping cycle according to the established gray model, so that the The cloud computing unit (2) outputs a thinning cycle control strategy according to the data sequence generated by the cloud computing unit (2) capable of predicting the thinning cycle. 2. The intelligent intermediate pumping control system for beam pumping units according to claim 1, wherein the cloud computing unit (2) cooperates with the edge computing unit (3), wherein, The edge computing unit (3) receives the operating parameters of the pumping unit (1) and the oil field parameters during the pumping process obtained by the intelligent sensing unit (4), and preprocesses the received data information, so that The cloud computing unit (2) can obtain data information from which invalid data has been screened out. 3. The intelligent intermediate pumping control system for beam pumping units as claimed in claim 2, wherein the cloud computing unit (2) is to complete the oil field dynamometer diagram according to the data information uploaded by the edge computing unit (3). Calculate and process the data, and establish a gray model related to the subsidence degree, so as to output the interval pumping cycle control strategy of the pumping unit (1) suitable for the actual permeability of the oil field according to the obtained dynamometer data and the established gray model. 4. The intelligent intermediate pumping control system for beam pumping units according to claim 3, characterized in that, the cloud computing unit (2) can also create a system capable of controlling the pumping units according to the received data information. (1) Fuzzy rules for regulating the balance degree during operation, so that the oil pumping unit (1) can perform self-balancing oil pumping work according to the fuzzy rules established by the cloud computing unit (2). 5. The intelligent intermediate pumping control system for beam pumping unit as claimed in claim 4, is characterized in that, described cloud computing unit (2) renews the dynamometer diagram and grey model that it establishes in real time according to the data information received, Therefore, the control strategy of the pumping unit (1) in the next working stage is adjusted according to the calculated swept area of the dynamometer diagram and the subsidence degree data predicted by the grey model. 6. The intelligent intermediate pumping control system for beam pumping units according to claim 5, characterized in that, the interval pumping cycle control strategy of the pumping unit (1) at least comprises adjusting the crank to alternately perform full-circle motion and swing. and control the swing amplitude of the crank during the swing process. 7. The intelligent intermediate pumping control system for a beam pumping unit according to one of the preceding claims, characterized in that, the pumping unit (1) at least comprises a pumping motor (11), an intelligent pumping unit controller (12) and a balancing unit (13), wherein, The intelligent pumping unit controller (12) can adjust the working state of the pumping motor (11) according to the latest pumping cycle control strategy output by the cloud computing unit (2), thereby completing the pumping unit (1) Automatic adjustment of stroke times, intelligent pumping control and automatic adjustment of balance. 8. The intelligent intermediate pumping control system for beam pumping units according to one of the preceding claims, characterized in that, in the crank full circle operation stage, the intelligent pumping unit controller (12) controls the pumping motor (11) work at rated speed; Calculate the stroke loss of the oil well according to the dynamometer diagram of the polished rod. When the motion length of the polished rod is less than the stroke loss, the oil pump stops working; When the crank rotates once and the polished rod completes one cycle of reciprocating operation, the crank swing angle is calculated according to the motion length of the polished rod; According to the electric power curve collected in real time, the power minimum point in the swing process is monitored, and the optimal driving position is determined. 9. A control method for an intelligent intermediate pumping control system for a beam pumping unit, characterized in that at least the following steps are included: The three-phase electrical parameters and various sensor signals of the pumping unit (1) are collected by the intelligent sensing unit (4), and the collected data is transmitted to the edge computing unit (3) through the communication module (5); The collected data information is preprocessed by the edge computing unit (3) and further uploaded to the cloud computing unit (2); The cloud computing unit (2) is used to analyze the received data, and the obtained data and control signals are sent to the intelligent pumping unit controller (12) to realize stroke adjustment and self-balancing of the pumping unit (1). adjustment and display via the control panel (6). 10. The control method of the intelligent pumping control system for beam pumping units according to one of the preceding claims, characterized in that, the cloud computing unit (2) and the edge computing unit (3) are implemented through a communication module (5) Carry out data transmission and control signal delivery.

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