Oil and gas production data acquisition method and device and electronic equipment

Abstract

The invention provides an oil and gas production data acquisition method and device and electronic equipment, and the method comprises the steps: obtaining sensing data collected by a sensor through controlling a data collection terminal disposed at an oil and gas production site; according to a preset window, expanding the sensing data to a sliding window data set containing the sensing data; generating a confidence interval according to a preset confidence level; determining an interval deviation corresponding to the confidence interval according to a mean absolute error and a standard deviation between the perception data and sliding window data in the sliding window data set; and according to the deviation maximum value and the deviation minimum value corresponding to the interval deviation, performing data cleaning on the sliding window data, and according to a preset data distribution rule, distributing the cleaned data to a downstream subsystem. Real-time monitoring of oil and gas production information in the oil and gas production and conveying process can be achieved, collected data are cleaned and audited, and the accuracy and timeliness of oil and gas production operation management and analysis are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of oil and gas data collection, and in particular to a method, device and electronic equipment for collecting oil and gas production data. Background Art

[0002] With the widespread popularity of natural gas in China, the number of oil and gas pipelines has increased. Scientifically and rationally assess, manage, and record the oil and gas flow of oil and gas pipelines at all levels, timely and comprehensively grasp the dynamic production capacity of the blocks, and provide real-time and accurate quantitative data for production decisions.

[0003] However, the current manual data entry method requires a lot of labor costs, a large workload and multiple employees, and the data entry cost is relatively high for remote areas. Manual work is uncertain and prone to errors, and it is difficult to trace data errors. Manual data entry has delays and a long entry cycle, which cannot guarantee the timeliness and synchronization of data. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a method, device and electronic equipment for collecting oil and gas production data, which can realize real-time monitoring of real-time flow, cumulative flow, gas pressure, temperature and other information during the oil and gas production and transportation process, and at the same time clean and audit the collected data, improve the accuracy and timeliness of oil and gas production operation management and analysis, assist production capacity decision-making, and optimize production plans.

[0005] The present disclosure provides a method for collecting oil and gas production data, including:

[0006] Controlling a data acquisition terminal disposed at an oil and gas production site to obtain sensing data collected by a sensor connected to the data acquisition terminal;

[0007] For the perception data corresponding to each moment, according to a preset window, the perception data is expanded to a sliding window data set containing the perception data;

[0008] On the basis that the sliding window data set conforms to a normal distribution, a confidence interval is generated according to a preset confidence level; based on the mean absolute error and standard deviation between the sensed data and the sliding window data in the sliding window data set, an interval deviation corresponding to the confidence interval is determined;

[0009] According to the maximum deviation and the minimum deviation corresponding to the interval deviation, data cleaning is performed on the sliding window data, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rule.

[0010] In an optional implementation manner, the controlling a data acquisition terminal disposed at an oil and gas production site to obtain the sensing data collected by a sensor connected to the data acquisition terminal specifically includes:

[0011] Control the data acquisition terminal to send a receiving data instruction to the sensor using a sensor data transceiver protocol;

[0012] Wherein, the data acquisition terminal is communicatively connected to a multi-source sensor network composed of a plurality of sensor nodes;

[0013] For each of the sensor nodes, the sensing data corresponding to the current moment is collected according to a preset data collection interval, and all the sensing data constitute a multi-source sensing data set with spatiotemporal correlation;

[0014] The multi-source perception data set is parsed into a target format through a preset data parsing program and then saved in a preset database.

[0015] In an optional implementation, the mean absolute error between the perception data and the sliding window data in the sliding window data set is determined based on the following formula:

[0016]

[0017] Wherein, η represents the mean absolute error between the perception data and the sliding window data; V(t i ) represents the sensor at time t i The sensory data collected at i ) represents the sensor at time t i The sliding window data corresponding to the perception data collected at; N represents the number of data.

[0018] In an optional implementation, the standard deviation between the perception data and the sliding window data in the sliding window data set is determined based on the following formula:

[0019]

[0020] Wherein, δ represents the standard deviation between the perception data and the sliding window data; V(t i ) represents the sensor at time t i The sensory data collected at i ) represents the sensor at time t i The sliding window data corresponding to the perception data collected at; N represents the number of data.

[0021] In an optional implementation, the interval deviation is determined based on the following formula:

[0022] DEV=η(V,W)+C*δ(V,W)

[0023] Among them, DEV represents the interval deviation; η(V, W) represents the mean absolute error between the perception data and the sliding window data; δ(V, W) represents the standard deviation between the perception data and the sliding window data; C represents the constant corresponding to the confidence interval.

[0024] In an optional implementation manner, the maximum deviation and the minimum deviation corresponding to the interval deviation are determined based on the following steps:

[0025] For each moment, the sum of the sliding window data value corresponding to the moment and the interval deviation is determined as the maximum deviation value;

[0026] The difference between the sliding window data value corresponding to the moment and the interval deviation is determined as the minimum deviation value.

[0027] In an optional implementation, after distributing the cleaned data to the downstream subsystem, the method further includes:

[0028] Controlling a preset data management subsystem to configure a corresponding identity for each of the sensors;

[0029] After receiving the cleaned data, determine whether the collection device corresponding to the data has been registered and authenticated; if the collection device has not been registered and authenticated, the data is prohibited from being uploaded;

[0030] The device operation status corresponding to each sensor is collected, and the sensors that are not in use are disabled.

[0031] The embodiment of the present disclosure also provides a device for collecting oil and gas production data, including:

[0032] A data acquisition module, used to control a data acquisition terminal provided at an oil and gas production site, and obtain sensing data collected by a sensor connected to the data acquisition terminal;

[0033] A sliding window data set determination module, configured to expand the perception data corresponding to each moment into a sliding window data set containing the perception data according to a preset window;

[0034] An interval deviation determination module is used to generate a confidence interval according to a preset confidence level on the basis that the sliding window data set conforms to a normal distribution; and to determine the interval deviation corresponding to the confidence interval according to the mean absolute error and standard deviation between the sensed data and the sliding window data in the sliding window data set;

[0035] The data cleaning and distribution module is used to clean the sliding window data according to the maximum deviation and the minimum deviation corresponding to the interval deviation, and distribute the cleaned data to the downstream subsystem according to the preset data distribution rules.

[0036] An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the above-mentioned method for collecting oil and gas production data, or steps in any possible implementation of the above-mentioned method for collecting oil and gas production data are executed.

[0037] The embodiments of the present disclosure also provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program executes the above-mentioned method for collecting oil and gas production data, or the steps in any possible implementation of the above-mentioned method for collecting oil and gas production data.

[0038] The embodiments of the present disclosure also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for collecting oil and gas production data, or the steps in any possible implementation of the above-mentioned method for collecting oil and gas production data.

[0039] The disclosed embodiment provides a method, device and electronic device for collecting oil and gas production data. The method controls a data collection terminal set at an oil and gas production site to obtain the sensed data collected by the sensor connected to the data collection terminal; for the sensed data corresponding to each moment, the sensed data is expanded to a sliding window data set containing the sensed data according to a preset window; on the basis that the sliding window data set conforms to a normal distribution, a confidence interval is generated according to a preset confidence level; based on the sensed data, the average absolute error and standard deviation between the sliding window data in the sliding window data set and the sensed data, the interval deviation corresponding to the confidence interval is determined; based on the maximum deviation and the minimum deviation corresponding to the interval deviation, the sliding window data is cleaned, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rules. The method can realize real-time monitoring of real-time flow, cumulative flow, gas pressure, temperature and other information in the oil and gas production and transportation process, and clean and audit the collected data at the same time, improve the accuracy and timeliness of oil and gas production operation management and analysis, assist production capacity decision-making, and optimize production plans.

[0040] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0042] Figure 1 A flow chart showing a method for collecting oil and gas production data provided by an embodiment of the present disclosure is shown;

[0043] Figure 2 A flow chart showing another method for collecting oil and gas production data provided by an embodiment of the present disclosure;

[0044] Figure 3 A schematic diagram of a device for collecting oil and gas production data provided by an embodiment of the present disclosure is shown;

[0045] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0047] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0048] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0049] Research has found that the current manual data entry method consumes a lot of labor costs, has a large workload and requires multiple employees, and the data entry cost is relatively high for remote areas. Manual work is uncertain and prone to errors, and it is difficult to trace data errors. Manual data entry has delays and a long entry cycle, which cannot guarantee the timeliness and synchronization of data.

[0050] Based on the above research, the present disclosure provides a method, device and electronic device for collecting oil and gas production data, which obtains the sensed data collected by the sensor connected to the data collection terminal by controlling the data collection terminal set at the oil and gas production site; for the sensed data corresponding to each moment, according to the preset window, the sensed data is expanded to a sliding window data set containing the sensed data; on the basis that the sliding window data set conforms to the normal distribution, a confidence interval is generated according to the preset confidence level; according to the sensed data, the average absolute error and standard deviation between the sliding window data in the sliding window data set and the sensed data, the interval deviation corresponding to the confidence interval is determined; according to the maximum deviation and the minimum deviation corresponding to the interval deviation, the sliding window data is cleaned, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rules. It can realize the real-time monitoring of the real-time flow, cumulative flow, gas pressure, temperature and other information in the oil and gas production and transportation process, and at the same time, clean and audit the collected data, improve the accuracy and timeliness of oil and gas production operation management and analysis, assist production capacity decision-making, and optimize production plans.

[0051] To facilitate understanding of this embodiment, a method for collecting oil and gas production data disclosed in the embodiment of the present disclosure is first introduced in detail. The execution subject of the method for collecting oil and gas production data provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the method for collecting oil and gas production data can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0052] See also Figure 1 FIG. 1 is a flowchart of a method for collecting oil and gas production data provided by an embodiment of the present disclosure, the method comprising steps S101 to S104, wherein:

[0053] S101. Control a data acquisition terminal disposed at an oil and gas production site to obtain perception data collected by a sensor connected to the data acquisition terminal.

[0054] In the specific implementation, a data acquisition terminal is set up at the oil and gas production site. The data acquisition terminal serves as the data source of the entire system. It communicates with sensors such as flow meters through a field bus or other forms of signals to obtain the oil and gas production information collected by the sensors.

[0055] Here, the data acquisition terminal may include a main control board, a battery, and a device housing. The data acquisition terminal is powered by a disposable battery. The main control circuit integrates the voltage stabilizing circuit required for the main controller, the voltage stabilizing circuit required for the peripheral device, the circuit required for the isolation circuit, and the boost circuit for external power supply. These power supply circuits all adopt a low-power design solution and are opened and closed in time by software to achieve strict power consumption management.

[0056] Among them, the main control board integrates AI input conversion circuit, which can convert 2-wire, 3-wire, and 4-wire current signal input. The main controller can directly collect and isolate each AI signal. The main controller integrates DI input and DO output circuits. The DO output circuit can output 24V control signal to the outside, which can be used as the action trigger signal of the on-site solenoid valve equipment; the DI input circuit can receive external 5-36V voltage signal input to realize the operation status detection of the equipment.

[0057] Optionally, the data acquisition terminal integrates a 4G module, which supports TCP and UDP communication modes, Modbus RTU protocol, Modbus TCP protocol, and DDP communication. The data acquisition terminal is equipped with a wireless communication module, and the wireless pressure instrument and wireless temperature instrument of the oil and gas well station can realize data access and support the control of command issuance.

[0058] Specifically, the data acquisition terminal is controlled, and a sensor data receiving and sending protocol is adopted to send and receive data instructions to the sensor; wherein the data acquisition terminal is communicatively connected to a multi-source sensor network composed of a plurality of sensor nodes; for each sensor node, the perception data corresponding to the current moment is collected according to a preset data collection interval, and all the perception data constitute a multi-source perception data set with spatiotemporal correlation; the multi-source perception data set is parsed into a target format through a preset data parsing program, and then saved into a preset database.

[0059] Here, the control data acquisition terminal sends and receives data commands to the sensor through the sensor data transmission and reception protocol, obtains the data value collected by the sensor at the current moment, and parses the data value at the current moment into a normal format through the program and saves it into the database.

[0060] Among them, the data acquisition terminal is a multi-source sensor network Ai=(A1, A2, A3,…, An) composed of n different sensor nodes. Each sensor can collect a set of perception data sets Si=(S1, S2, S3,…, Sn). Si (1≤i≤n) represents multiple sets of multi-source perception data sets with spatiotemporal correlation collected by a node Ai at fixed time intervals.

[0061] As a possible implementation mode, the line data acquisition terminal includes a main control board, a main controller, a serial port unit 1, a clock unit, a serial port unit 2, a GPIO unit, an ADC unit and a power management unit arranged on the main control board, the serial port unit 1 is provided with an RS485 interface circuit and a 4G module interface circuit, the clock unit is provided with a high-speed main clock and an RTC low-speed clock, the serial port unit 2 is provided with a Zigbee module interface circuit, the GPIO unit is provided with a DI interface circuit and a DO interface circuit, the ADC unit is provided with an AI signal processing circuit, and the power management unit is provided with a voltage stabilizing circuit and a voltage boosting circuit.

[0062] Here, the main controller is the main control unit of the system. It controls the specified working time and sleep time of each peripheral by running a specifically written embedded program. The main control circuit integrates the 3.3V voltage regulator circuit required for the main controller, the 5V voltage regulator circuit required for the peripheral device, the 3.3V circuit required for the isolation circuit, and the 24V boost circuit for external power supply. These power supply circuits all adopt a low-power design and are turned on and off in time-sharing manner through software to achieve strict power consumption management.

[0063] Among them, the AI ​​signal processing module is used to convert the current-to-voltage circuit integrated on the main control board, including input isolation and voltage conversion circuit. It can realize 0-2.5V voltage conversion for the input 0-25ma current and provide it to the main controller for direct sampling and processing;

[0064] Furthermore, the power management module is responsible for converting the 7.2V battery voltage into the 3.3V voltage required for the main controller to work, the 5V voltage required for the RS485, 4G module, Zigbee module, DI, and DO interface circuits to work, and is responsible for boosting the 7.2V voltage for use by the AI ​​circuit;

[0065] Furthermore, the serial port unit module includes RS485 interface circuit, 4G module interface circuit and ZigBee interface circuit. The RS485 interface circuit includes the conversion of the main controller TTL serial port level into RS485 signal, and includes an external isolation circuit; the 4G module is the external communication channel of the entire system, and the 4G module interface circuit includes a basic signal level matching circuit and a signal isolation circuit; the ZigBee interface circuit includes a ZigBee interface signal level processing circuit and a signal isolation circuit. ZigBee is responsible for communicating with external ZigBee instruments, and as a coordinator in the ZigBee network, it is responsible for component networks and completing device node identity authentication.

[0066] Furthermore, the GPIO unit module is used for the digital signal processing circuit integrated on the main control board, including a signal processing circuit and an isolation circuit, and is used for outputting a 24V control signal to the outside and detecting a level signal of an external input;

[0067] Furthermore, the clock unit module includes a high-speed main clock and an RTC low-speed clock, which are circuit units mainly used to provide a working clock for the main controller, and provide a pulse clock when the main controller is in sleep mode and when it is running.

[0068] S102: For the perception data corresponding to each moment, according to a preset window, expand the perception data to a sliding window data set that includes the perception data.

[0069] In the specific implementation, the sensor collects the original perception data at each time point according to the preset data collection time interval. After obtaining the original perception data, the original perception data is smoothed; the perception data value at each moment is expanded to a range that includes this point, and all values ​​in this range are used to generate new perception data values ​​using an algorithm.

[0070] Here, let the current time be t, the window be set to ω, the value of each perception data at time point ti can be expressed as Vi = [V1(t1), V2(t2), V3(t3), ..., Vn(tn)], set to take ω perception data values ​​centered on the perception data value V(t) to calculate the mean, and the value of the dynamic sliding window data set W(t) at the current time t is shown in the following formula:

[0071]

[0072] Among them, W(t) represents the sliding window data set; V(t) represents the perception data value corresponding to the perception data at time t; ω represents the sliding window.

[0073] S103. On the basis that the sliding window data set conforms to a normal distribution, generate a confidence interval according to a preset confidence level; determine the interval deviation corresponding to the confidence interval according to the mean absolute error and standard deviation between the perception data and the sliding window data in the sliding window data set.

[0074] In the specific implementation, on the basis that the sliding window data set conforms to the normal distribution, a confidence interval is generated according to a preset confidence level, and the size of the interval deviation is calculated; the size of the interval deviation value is calculated by the mean absolute error, constant and standard deviation.

[0075] Here, the mean absolute error is the average of the absolute values ​​of the deviations between all original perception data values ​​and the sliding window data values; the standard deviation is the square root of the arithmetic mean of the squared deviations of the perception original data sequence from the sliding window data set.

[0076] Specifically, the mean absolute error between the perception data and the sliding window data in the sliding window dataset is determined based on the following formula:

[0077]

[0078] Where η represents the mean absolute error between the perception data and the sliding window data; V(t i ) represents the sensor at time t i The sensory data collected at i ) represents the sensor at time t i The sliding window data corresponding to the perception data collected at; N represents the number of data.

[0079] Furthermore, the standard deviation between the perception data and the sliding window data in the sliding window dataset is determined based on the following formula:

[0080]

[0081] Among them, δ represents the standard deviation between the perception data and the sliding window data; V(t i ) represents the sensor at time t i The sensory data collected at i ) represents the sensor at time t i The sliding window data corresponding to the perception data collected at; N represents the number of data.

[0082] Furthermore, the interval deviation is determined based on the following formula:

[0083] DEV=η(V,W)+C*δ(V,W)

[0084] Among them, DEV represents the interval deviation; η(V, W) represents the mean absolute error between the perception data and the sliding window data; δ(V, W) represents the standard deviation between the perception data and the sliding window data; C represents the constant corresponding to the confidence interval.

[0085] S104: performing data cleaning on the sliding window data according to the maximum deviation value and the minimum deviation value corresponding to the interval deviation, and distributing the cleaned data to a downstream subsystem according to a preset data distribution rule.

[0086] In the specific implementation, after calculating the deviation of the data set using the mean difference and accidental error of the two data sets, the maximum deviation and the minimum deviation corresponding to the confidence interval of the data are generated based on the sliding window data set for data cleaning. After obtaining the clean data after cleaning, the data is distributed according to the set distribution rules.

[0087] Here, for each moment, the sum of the sliding window data value corresponding to the moment and the interval deviation is determined as the maximum deviation; the difference between the sliding window data value corresponding to the moment and the interval deviation is determined as the minimum deviation.

[0088] The disclosed embodiment provides a method for collecting oil and gas production data, which controls a data collection terminal set at an oil and gas production site to obtain the sensed data collected by a sensor connected to the data collection terminal; for the sensed data corresponding to each moment, according to a preset window, the sensed data is expanded to a sliding window data set containing the sensed data; on the basis that the sliding window data set conforms to a normal distribution, a confidence interval is generated according to a preset confidence level; according to the sensed data, the mean absolute error and standard deviation between the sliding window data in the sliding window data set and the sensed data, the interval deviation corresponding to the confidence interval is determined; according to the maximum deviation and the minimum deviation corresponding to the interval deviation, the sliding window data is cleaned, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rules. The method can realize real-time monitoring of real-time flow, cumulative flow, gas pressure, temperature and other information in the oil and gas production and transportation process, and at the same time, clean and audit the collected data, improve the accuracy and timeliness of oil and gas production operation management and analysis, assist production capacity decision-making, and optimize production plans.

[0089] See also Figure 2 FIG. 2 is a flowchart of another method for collecting oil and gas production data provided by an embodiment of the present disclosure, wherein the method includes steps S201 to S207, wherein:

[0090] S201. Control a data acquisition terminal disposed at an oil and gas production site to obtain perception data collected by a sensor connected to the data acquisition terminal.

[0091] S202: For the perception data corresponding to each moment, expand the perception data to a sliding window data set including the perception data according to a preset window.

[0092] S203. On the basis that the sliding window data set conforms to a normal distribution, generate a confidence interval according to a preset confidence level; and determine the interval deviation corresponding to the confidence interval according to the mean absolute error and standard deviation between the perception data and the sliding window data in the sliding window data set.

[0093] S204: performing data cleaning on the sliding window data according to the maximum deviation value and the minimum deviation value corresponding to the interval deviation, and distributing the cleaned data to a downstream subsystem according to a preset data distribution rule.

[0094] Here, steps S201 to S204 are substantially the same as steps S101 to S104, have the same implementation methods, and can achieve the same technical effects, and thus will not be described in detail.

[0095] S205: Control the preset data management subsystem to configure a corresponding identity for each sensor.

[0096] S206, after receiving the cleaned data, determine whether the collection device corresponding to the data has been registered and authenticated; if the collection device has not been registered and authenticated, prohibit the data from being uploaded;

[0097] S207: Collect the device operation status corresponding to each sensor, and deactivate the sensors that are not in use.

[0098] In the specific implementation, the data management subsystem is responsible for managing the operating status information of the oil and gas production data remote terminal, assigning identity tags to the oil and gas production data remote terminal, and canceling the oil and gas production data remote terminal that is no longer in use; at the same time, it manages the data flow information of the oil and gas production data remote terminal and collects the operating status of the oil and gas production data remote terminal.

[0099] As a preferred implementation, the data management subsystem will provide equipment registration and authentication functions to ensure that only authenticated devices can upload data. The data management subsystem needs to analyze the equipment operating status and implement monitoring of the collection equipment status as well as management, diagnosis and maintenance. The data management subsystem needs to implement equipment cost management and provide functions such as renewal and shutdown.

[0100] The disclosed embodiment provides a method for collecting oil and gas production data, which controls a data collection terminal set at an oil and gas production site to obtain the sensed data collected by a sensor connected to the data collection terminal; for the sensed data corresponding to each moment, according to a preset window, the sensed data is expanded to a sliding window data set containing the sensed data; on the basis that the sliding window data set conforms to a normal distribution, a confidence interval is generated according to a preset confidence level; according to the sensed data, the mean absolute error and standard deviation between the sliding window data in the sliding window data set and the sensed data, the interval deviation corresponding to the confidence interval is determined; according to the maximum deviation and the minimum deviation corresponding to the interval deviation, the sliding window data is cleaned, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rules. The method can realize real-time monitoring of real-time flow, cumulative flow, gas pressure, temperature and other information in the oil and gas production and transportation process, and at the same time, clean and audit the collected data, improve the accuracy and timeliness of oil and gas production operation management and analysis, assist production capacity decision-making, and optimize production plans.

[0101] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0102] Based on the same inventive concept, the embodiments of the present disclosure also provide an oil and gas production data collection device corresponding to the oil and gas production data collection method. Since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to the above-mentioned oil and gas production data collection method in the embodiments of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0103] See also Figure 3 , Figure 3 A schematic diagram of a device for collecting oil and gas production data provided by an embodiment of the present disclosure. Figure 3 As shown in , the oil and gas production data collection device 300 provided in the embodiment of the present disclosure includes:

[0104] The data acquisition module 310 is used to control a data acquisition terminal disposed at an oil and gas production site to obtain sensing data collected by a sensor connected to the data acquisition terminal.

[0105] The sliding window data set determination module 320 is used to expand the perception data corresponding to each moment into a sliding window data set containing the perception data according to a preset window.

[0106] The interval deviation determination module 330 is used to generate a confidence interval according to a preset confidence level based on the fact that the sliding window data set conforms to the normal distribution; and determine the interval deviation corresponding to the confidence interval based on the mean absolute error and standard deviation between the perception data and the sliding window data in the sliding window data set.

[0107] The data cleaning and distribution module 340 is used to clean the sliding window data according to the maximum deviation and the minimum deviation corresponding to the interval deviation, and distribute the cleaned data to the downstream subsystem according to the preset data distribution rules.

[0108] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0109] The disclosed embodiment provides a device for collecting oil and gas production data. The device controls a data collection terminal set at an oil and gas production site to obtain the sensed data collected by the sensor connected to the data collection terminal. For the sensed data corresponding to each moment, the sensed data is expanded to a sliding window data set containing the sensed data according to a preset window. On the basis that the sliding window data set conforms to a normal distribution, a confidence interval is generated according to a preset confidence level. The interval deviation corresponding to the confidence interval is determined according to the mean absolute error and standard deviation between the sensed data and the sliding window data in the sliding window data set. The sliding window data is cleaned according to the maximum deviation and the minimum deviation corresponding to the interval deviation, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rules. The device can realize real-time monitoring of real-time flow, cumulative flow, gas pressure, temperature and other information in the oil and gas production and transportation process, and clean and audit the collected data at the same time, improve the accuracy and timeliness of oil and gas production operation management and analysis, assist production capacity decision-making, and optimize production plans.

[0110] Corresponds to Figure 1 and Figure 2 The method for collecting oil and gas production data in the present disclosure also provides an electronic device 400, such as Figure 4 FIG. 4 is a schematic diagram of the structure of an electronic device 400 provided in an embodiment of the present disclosure, including:

[0111] Processor 41, memory 42, and bus 43; memory 42 is used to store execution instructions, including memory 421 and external memory 422; memory 421 here is also called internal memory, which is used to temporarily store operation data in processor 41 and data exchanged with external memory 422 such as hard disk. Processor 41 exchanges data with external memory 422 through memory 421. When the electronic device 400 is running, the processor 41 communicates with the memory 42 through bus 43, so that the processor 41 executes Figure 1 and Figure 2 The steps of the method for collecting oil and gas production data.

[0112] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for collecting oil and gas production data described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0113] The embodiments of the present disclosure also provide a computer program product, which includes computer instructions. When the computer instructions are executed by a processor, the steps of the method for collecting oil and gas production data described in the above method embodiment can be executed. For details, please refer to the above method embodiment, which will not be repeated here.

[0114] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0115] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0119] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for collecting oil and gas production data, characterized in that: include: Controlling a data acquisition terminal disposed at an oil and gas production site to obtain sensing data collected by a sensor connected to the data acquisition terminal; For the perception data corresponding to each moment, according to a preset window, the perception data is expanded to a sliding window data set containing the perception data; On the basis that the sliding window data set conforms to a normal distribution, a confidence interval is generated according to a preset confidence level; based on the mean absolute error and standard deviation between the sensed data and the sliding window data in the sliding window data set, an interval deviation corresponding to the confidence interval is determined; According to the maximum deviation and the minimum deviation corresponding to the interval deviation, data cleaning is performed on the sliding window data, and the cleaned data is distributed to the downstream subsystem according to the preset data distribution rule.

2. The method according to claim 1, characterized in that The controlling of the data acquisition terminal disposed at the oil and gas production site to obtain the sensing data collected by the sensor connected to the data acquisition terminal specifically includes: Control the data acquisition terminal to send a data receiving instruction to the sensor using a sensor data receiving and sending protocol; Wherein, the data acquisition terminal is communicatively connected to a multi-source sensor network composed of a plurality of sensor nodes; For each of the sensor nodes, the sensing data corresponding to the current moment is collected according to a preset data collection interval, and all the sensing data constitute a multi-source sensing data set with spatiotemporal correlation; The multi-source perception data set is parsed into a target format through a preset data parsing program and then saved in a preset database.

3. The method according to claim 1, characterized in that: The mean absolute error between the perception data and the sliding window data in the sliding window data set is determined based on the following formula: Wherein, η represents the mean absolute error between the perception data and the sliding window data; V(t i ) represents the sensor at time t i The sensory data collected at i ) represents the sensor at time t i The sliding window data corresponding to the perception data collected at; N represents the number of data.

4. The method according to claim 1, characterized in that The standard deviation between the perception data and the sliding window data in the sliding window data set is determined based on the following formula: Wherein, δ represents the standard deviation between the perception data and the sliding window data; V(t i ) represents the sensor at time t i The sensory data collected at i ) represents the sensor at time t i The sliding window data corresponding to the perception data collected at; N represents the number of data.

5. The method according to claim 1, characterized in that The interval deviation is determined based on the following formula: DEV=η(V,W)+C*δ(V,W) Among them, DEV represents the interval deviation; η(V, W) represents the mean absolute error between the perception data and the sliding window data; δ(V, W) represents the standard deviation between the perception data and the sliding window data; C represents the constant corresponding to the confidence interval.

6. The method according to claim 1, characterized in that Determine the maximum deviation and the minimum deviation corresponding to the interval deviation based on the following steps: For each moment, the sum of the sliding window data value corresponding to the moment and the interval deviation is determined as the maximum deviation value; The difference between the sliding window data value corresponding to the moment and the interval deviation is determined as the minimum deviation value.

7. The method according to claim 1, characterized in that After distributing the cleaned data to the downstream subsystems, the method further includes: Controlling a preset data management subsystem to configure a corresponding identity for each of the sensors; After receiving the cleaned data, determine whether the collection device corresponding to the data has been registered and authenticated; if the collection device has not been registered and authenticated, the data is prohibited from being uploaded; The device operation status corresponding to each sensor is collected, and the sensors that are not in use are disabled.

8. A device for collecting oil and gas production data, characterized in that: include: A data acquisition module is used to control a data acquisition terminal provided at an oil and gas production site to obtain sensing data collected by a sensor connected to the data acquisition terminal; A sliding window data set determination module, configured to expand the perception data corresponding to each moment into a sliding window data set containing the perception data according to a preset window; An interval deviation determination module is used to generate a confidence interval according to a preset confidence level on the basis that the sliding window data set conforms to a normal distribution; and to determine the interval deviation corresponding to the confidence interval according to the mean absolute error and standard deviation between the sensed data and the sliding window data in the sliding window data set; The data cleaning and distribution module is used to clean the sliding window data according to the maximum deviation and the minimum deviation corresponding to the interval deviation, and distribute the cleaned data to the downstream subsystem according to the preset data distribution rules.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method for collecting oil and gas production data as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the method for collecting oil and gas production data as described in any one of claims 1 to 7.

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