CN-122018462-A - Intelligent oil extraction safety control system and method

CN122018462ACN 122018462 ACN122018462 ACN 122018462ACN-122018462-A

Abstract

The invention discloses an intelligent oil extraction safety control system and method, and relates to the field of oil extraction. The system comprises a monitoring module, a control module, an early warning module, an execution module and a remote monitoring center. The monitoring module collects parameters such as wellhead pressure, temperature, work diagram, working fluid level and the like in real time, the control module cleans and eliminates invalid data, an improved algorithm is adopted to perform working condition diagnosis, liquid production amount calculation and working fluid level inversion, a parameter threshold database is combined to judge abnormality, the early warning module alarms in time, the execution module carries out linkage treatment, and the remote monitoring center realizes remote regulation. The invention improves the safety and efficiency of oil extraction and has strong practicability.

Inventors

XIE JIANJUN
Qi Mingxiang
SUN SHAOHUA
CHEN JUNJIE
LIU YAN
FU XIURONG

Assignees

胜利方兰德石油装备股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260202

Claims (7)

1. The intelligent oil extraction safety control system is characterized by comprising a monitoring module, a control module and a control module, wherein the monitoring module is used for acquiring various parameters in the oil extraction process in real time through a sensor, and the parameters comprise, but are not limited to, wellhead pressure, temperature, flow, oil gas concentration, equipment operation state parameters, work diagram data and working fluid level related data; the system comprises a monitoring module, a control module, an early warning module, an execution module, a remote monitoring center and a control module, wherein the monitoring module is used for monitoring parameters acquired by the monitoring module and analyzing and processing the parameters, the processing comprises data cleaning, working condition diagnosis, liquid production pump efficiency calculation and working fluid level inversion conversion; The control module is internally provided with a parameter threshold database and an abnormal data model, the data cleaning is to filter and screen data according to the abnormal data model to remove unqualified data, the working condition diagnosis comprises two algorithms of improved single work diagram graphic diagnosis and improved historical work diagram characteristic value comparison diagnosis, the liquid yield and pump efficiency calculation is used for calculating and collecting the liquid yield of the oil well pump, the liquid level inversion conversion is used for calculating liquid level data in real time through an improved sound wave method and an improved pressure recovery method to accurately obtain the liquid level depth of the oil well, the parameter threshold database stores normal range thresholds of various parameters in the oil extraction process, and the abnormal data model is constructed based on abnormal data characteristics in the oil extraction process.
2. The intelligent oil recovery safety control system according to claim 1, wherein the execution module comprises a valve control unit, a pump control unit and an emergency stopping unit, wherein the valve control unit is used for controlling the opening and closing of a wellhead valve, the pump control unit is used for controlling the start and stop of an oil recovery pump and the rotating speed, and the emergency stopping unit is used for stopping the operation of oil recovery equipment in an emergency.
3. The intelligent oil recovery safety control system of claim 1, wherein the communication module is configured to communicate wirelessly, the wireless communication comprising one or more of 4G, 5G, loRa, or NB-IoT.
4. The intelligent oil extraction safety control system according to claim 1, wherein the control module comprises the following specific implementation logic: The data cleaning comprises the steps of filtering and screening data according to an abnormal data model, and removing unqualified data, and concretely comprises the steps of receiving the data collected by a monitoring module, extracting the characteristics of the data, comparing the extracted characteristics with the characteristics in the abnormal data model, judging the unqualified data and removing if the characteristics of the data accord with the abnormal data model; the working condition diagnosis comprises the steps of preprocessing a single-work diagram collected by a monitoring module, removing noise interference, adopting a Gaussian filtering algorithm, and adopting the formula: wherein For the filtered pixel values, For the pixel coordinates, Is standard deviation and The method comprises the steps of setting a value to 1-3 according to the noise condition of the work diagram, extracting characteristic parameters of a single work diagram, including but not limited to a maximum load value, a minimum load value and a stroke length, comparing the characteristic parameters with characteristic parameters of a standard work diagram, and calculating the difference degree, wherein the formula is as follows: wherein In order for the degree of difference to be the same, To the first of the work diagrams to be diagnosed The number of characteristic parameters of the device is, Is the standard diagram of the work The number of characteristic parameters of the device is, As the number of characteristic parameters, the degree of difference When the set threshold value is exceeded, judging the abnormal working condition of the corresponding type; The improved historical work pattern characteristic value comparison diagnosis algorithm includes collecting work pattern data of oil well set for some period, establishing historical work pattern characteristic database, calculating the similarity between the characteristic value of current work pattern and the characteristic value of historical work pattern, and cosine similarity algorithm Wherein In order for the degree of similarity to be the same, Is the first of the current diagram The value of the characteristic is a value of, Is the first of the historical work diagrams Characteristic value, when similarity When the value is lower than the set threshold, judging whether the pipe column is in an abnormal working condition or not by combining the characteristics of the historical abnormal work diagram; The liquid yield and pump efficiency calculation adopts an improved liquid yield calculation algorithm, and specifically comprises the steps of collecting parameters of stroke, stroke frequency and pump diameter of the oil pump, and calculating the liquid yield according to a liquid yield calculation formula, wherein the formula is as follows: wherein In order to produce a liquid volume of the liquid, In order to correct the coefficient of the coefficient, Depending on the nature of the fluid in the well, In order to be a stroke of the stroke, In order to make the time of the impact, Is the diameter of the pump, the diameter of the pump is the same as the diameter of the pump, For the pump efficiency, correcting the calculation result, and fitting a correction coefficient according to actual production data; the inversion conversion of the working fluid level is that firstly, an improved acoustic method is used for calculating, namely, the acoustic signal is transmitted and the reflected signal is received, and the acoustic propagation time is recorded According to the formula Calculating the depth of the working fluid Wherein For the propagation velocity of sound waves in the well medium, Correcting according to the temperature and pressure parameters in the well, wherein the correction formula is as follows Wherein Is the speed of sound waves in the standard state, For the temperature correction coefficient(s), As a result of the pressure correction factor, In the case of the temperature in the well, Is the pressure in the well, and then the dynamic liquid level depth is calculated by using a modified pressure recovery method, wherein the equation is Wherein To shut in the well The pressure at the moment in time is, As a function of the original formation pressure, In order to achieve a yield of the product, As a function of the viscosity of the fluid, In order for the permeability to be a function of, Is the thickness of the oil layer, In order to achieve a degree of porosity, the porous material, In order to integrate the compression coefficient(s), And (5) inverting the equation to obtain the depth of the working fluid level for the radius of the shaft.
5. The intelligent oil production safety control system according to claim 4, wherein the liquid production pump efficiency calculation logic is as follows: step1, determining a correction coefficient Influence factors and basic values of (a): the viscosity of fluid in the oil production well is collected in real time through a monitoring module by combining with the actual working condition of the oil production well Air content Water content Parameters, establishing fluid properties and A correlation database of values, in an initial state, set according to a conventional classification of fluid properties Basic value of (i), i.e When the fluid is of low viscosity Low air content Low water content Is used for the crude oil of the oil-water separator, The value was set to 0.92; step 2, fitting based on actual production data And (3) correcting a model: Collecting actual liquid production amount data of oil production well for the past 6 months Wherein Actually measured through a metering separator, and simultaneously recording the stroke acquired by a monitoring module in a corresponding time period The times of punching Diameter of pump Pump effect Substituting the above data into formula Back-pushing the history Values, i.e. Then using least square method And performing multiple linear regression with the fluid property parameters to obtain a fitting formula: wherein Is a regression coefficient; Step 3, real-time calculation and dynamic correction Value: The control module receives real-time acquisition of the monitoring module And fluid property parameters, first according to step 1 to determine Then substituting the fitting formula in the step 2 to calculate the real time Value of calculated liquid production amount of the day every 24 hours And actually measure the liquid yield Comparing, if the error exceeds 3%, re-optimizing the regression coefficient Ensure that The value is always matched with the actual working condition; and 4, accurately calculating the liquid yield and controlling errors: Will be in real time Value substitution formula Obtaining the final liquid yield.
6. The intelligent oil recovery safety control system according to claim 4, wherein the improved sonic method comprises the following logic: Step I, determining a basic value of the sound wave propagation speed Determining a basic value of sound wave propagation speed under a standard state by combining geological conditions of a production well and medium types in the well through laboratory simulation and historical data statistics ; II, constructing a temperature-pressure correction model, namely collecting different temperatures of the oil production well within the past 3 months Pressure and force Measured value of acoustic wave propagation velocity under condition Wherein Synchronous acquisition is carried out through the acoustic logging instrument, and corresponding temperature sensor and pressure sensor monitoring data are correspondingly recorded at the same time And Then adopting multiple linear regression algorithm pair And (3) with Fitting to obtain a correction model: wherein For the temperature correction coefficient(s), Is a pressure correction coefficient; III, acquiring parameters in real time and calculating the corrected sound wave speed, wherein a monitoring module acquires the temperature in the well in real time through a temperature sensor and a pressure sensor And pressure The temperature to be collected And pressure The data is transmitted to the control module, and the control module calls the step I And (II) automatically calculating the real-time sound wave propagation speed by the correction model : ; Step IV, dynamically optimizing correction coefficients, namely, every 72 hours, the control module calculates corrected sound wave velocity values Measured value of synchronous acoustic logging instrument Comparing and calculating error rate If (1) Over 2%, then re-fit based on the most recently acquired 100 sets of data Coefficient, optimizing correction model; Step V, calculating the depth of the working fluid level The control module is used for controlling the time difference between sound wave emission and reflection Combining the corrected sonic velocity By the formula And calculating the depth of the working fluid level.
7. An intelligent oil extraction safety control method applied to the intelligent oil extraction safety control system as claimed in any one of claims 1 to 6, which is characterized by comprising the following steps: s1, a monitoring module collects parameters and sends the parameters to a control module; S2, the control module cleans and analyzes the data, and judges whether the data are abnormal or not by combining working condition diagnosis, liquid yield pump efficiency calculation and working fluid level inversion conversion results; Step S3, if the abnormality is judged, the control module sends an early warning instruction to the early warning module, the early warning module sends an early warning signal, and meanwhile, the control module sends a corresponding control instruction to the execution module according to the abnormality; S4, the execution module receives the control instruction and executes corresponding operation to process abnormal conditions; and S5, the control module sends the parameter information and the early warning signal to a remote monitoring center through the communication module, and the remote monitoring center sends a control instruction to the control module according to the situation so as to remotely regulate and control the oil extraction process.

Description

Intelligent oil extraction safety control system and method Technical Field The invention relates to the field of oil extraction, in particular to an intelligent oil extraction safety control system and method. Background The oil extraction operation is an important link in the petroleum industry, the operation environment is complex and changeable, and various potential safety hazards such as overhigh wellhead pressure, abnormal temperature, oil and gas leakage and the like exist, and once the potential hazards are improperly treated, the potential hazards are extremely easy to cause safety accidents, and casualties and property loss are caused. At present, the traditional oil extraction safety control mainly relies on manual inspection and simple monitoring equipment, and has the problems of untimely monitoring, early warning lag, low treatment efficiency and the like. The manual inspection is limited by time and space, so that real-time comprehensive monitoring of the oil extraction process is difficult to realize, problems can be found after accidents occur, and early warning and timely treatment cannot be realized. The simple monitoring equipment has single function, can only monitor partial parameters, has limited data processing capacity, and can not accurately judge and quickly respond to abnormal conditions. Therefore, the intelligent oil extraction safety control system and the intelligent oil extraction safety control method which can monitor in real time, early warn in time and rapidly process abnormal conditions are developed, and the intelligent oil extraction safety control system and the intelligent oil extraction safety control method have important significance for improving the safety and the efficiency of oil extraction operation. Disclosure of Invention The invention aims to provide an intelligent oil extraction safety control system and method for solving the problems in the background technology. The intelligent oil extraction safety control system comprises a monitoring module, a monitoring module and a control module, wherein the monitoring module is used for acquiring various parameters in the oil extraction process in real time through a sensor, and the parameters comprise, but are not limited to, wellhead pressure, temperature, flow, oil gas concentration, equipment operation state parameters, work diagram data and working fluid level related data; the system comprises a monitoring module, a control module, an early warning module, an execution module, a remote monitoring center and a control module, wherein the monitoring module is used for monitoring parameters acquired by the monitoring module and analyzing and processing the parameters, the processing comprises data cleaning, working condition diagnosis, liquid production pump efficiency calculation and working fluid level inversion conversion; The control module is internally provided with a parameter threshold database and an abnormal data model, the data cleaning is to filter and screen data according to the abnormal data model to remove unqualified data, the working condition diagnosis comprises two algorithms of improved single work diagram graphic diagnosis and improved historical work diagram characteristic value comparison diagnosis, the liquid yield and pump efficiency calculation is used for calculating and collecting the liquid yield of the oil well pump, the liquid level inversion conversion is used for calculating liquid level data in real time through an improved sound wave method and an improved pressure recovery method to accurately obtain the liquid level depth of the oil well, the parameter threshold database stores normal range thresholds of various parameters in the oil extraction process, and the abnormal data model is constructed based on abnormal data characteristics in the oil extraction process. Preferably, the execution module comprises a valve control unit, a pump control unit and an emergency stopping unit, wherein the valve control unit is used for controlling the opening and closing of a wellhead valve, the pump control unit is used for controlling the start and stop of the oil extraction pump and the rotating speed, and the emergency stopping unit is used for stopping the operation of the oil extraction equipment in an emergency. Preferably, the communication module employs a wireless communication means including one or more of 4G, 5G, loRa, or NB-IoT. Preferably, the specific implementation logic of the control module is as follows: The data cleaning comprises the steps of filtering and screening data according to an abnormal data model, and removing unqualified data, and concretely comprises the steps of receiving the data collected by a monitoring module, extracting the characteristics of the data, comparing the extracted characteristics with the characteristics in the abnormal data model, judging the unqualified data and removing if the characteristics of the data accord with the abnormal d