Oil well automatic production metering abnormality monitoring and early warning method and device

Abstract

The present application relates to a kind of oil well production technology field, and a kind of oil well automatic production abnormal monitoring and early warning method and device, the former includes: the various data of the metering equipment in the acquisition metering process;The various data of metering equipment is input into production abnormal monitoring and diagnosis model library, and the output metering abnormal diagnosis result;In combination with the preset alarm mechanism, screening metering abnormal diagnosis result, push corresponding alarm information.The present application establishes production abnormal monitoring and diagnosis model library, automatically collects the various data of metering equipment, uses production abnormal monitoring and diagnosis model library to carry out monitoring and diagnosis, obtains corresponding metering abnormal diagnosis result, and in combination with the preset alarm mechanism, push alarm information, realize oil well automatic production abnormal monitoring and early warning, improve production efficiency, reduce yield loss, while effectively eliminate invalid alarm, redundant alarm, improve the use rate of human resources, avoid safety accidents.

Description

Technical Field

[0001] This invention relates to the field of oil well production calculation technology, specifically a method and device for automatic monitoring and early warning of abnormal oil well production calculation. Background Technology

[0002] Currently, single-well metering methods in oilfields include two types: inter-metering well-rounding metering and single-well dynamometer card production measurement. Inter-metering well-rounding metering is further divided into lower-level machine (RTU) metering and upper-level machine (remote metering program) metering. The application of these methods has met the needs of automatic oil and gas metering to a certain extent, but there are still problems that need to be solved urgently: 1. Low monitoring and measurement efficiency, and untimely problem detection. As the metering method with the highest measurement accuracy, inter-well metering relies on the linkage control of software programs and hardware. Once the software program and the on-site hardware status are inconsistent, it is very easy to cause measurement failure. Currently, whether the measurement conditions are met and whether there are any abnormalities in the measurement process all depend on manual judgment based on on-site or remote monitoring data from the monitoring system. This results in low problem-solving efficiency and a lack of intelligent automatic production measurement anomaly monitoring and early warning methods for oil wells.

[0003] The metering operation carries high risks. The purpose of production metering is to obtain the daily output of each oil well. The Shixi Oilfield uses a valve-reversing sampling metering method, a relatively common method in oilfields, which has a smaller error and more stable measurement error compared to using dynamometer cards to estimate output. However, in actual operation, accidents such as valve reversal, abnormal pressure, and oil and gas leaks are prone to occur. Current technology cannot accurately monitor remotely, resulting in numerous false alarms and missed alarms. Summary of the Invention

[0004] This invention provides a method and device for automatic production monitoring and early warning of oil wells, which overcomes the shortcomings of the prior art. It can effectively solve the problems of low efficiency, false alarms and missed alarms that existing automatic production monitoring and early warning of oil wells relies on on-site or remote manual judgment.

[0005] One of the technical solutions of this invention is achieved through the following measures: a method for automatic monitoring and early warning of abnormal production in oil wells, characterized in that it includes: Collect various data from the metering equipment during the metering process; Input various data from metering equipment into the production anomaly monitoring and diagnosis model library, and output the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various monitoring and diagnosis models to verify whether communication is normal, whether instruments are normal, whether upstream oil wells are normal, whether upstream pipelines are normal, and whether the metering process is normal. Based on the preset alarm mechanism, the system filters the diagnostic results of measurement anomalies and pushes the corresponding alarm information.

[0006] The following are further optimizations and / or improvements to the above-mentioned technical solution: The above alarm generation conditions are the first alarm generation condition and the second alarm generation condition; The first alarm generation conditions include alarm impact classification and impact degree classification conditions for each type of alarm impact; The second alarm generation conditions include the allowable response time corresponding to the metrological anomaly diagnosis result, the degree of impact corresponding to the metrological anomaly diagnosis result, the severity of the consequences corresponding to the metrological anomaly diagnosis result, and the correlation among the three.

[0007] The above-mentioned system, combined with a preset alarm mechanism, filters the metrological monitoring and diagnostic results and outputs corresponding alarm information, including: Obtain the diagnostic results of measurement anomalies after monitoring and diagnosis by the production anomaly monitoring and diagnosis model library; The results of the measurement anomaly diagnosis are used to determine the corresponding alarm impact type and impact degree by taking the first alarm generation conditions into the instrument; The results of the metrological anomaly diagnosis and the corresponding degree of impact are incorporated into the second alarm generation conditions to determine the severity of the consequences corresponding to the metrological anomaly diagnosis results. Alarm information will be pushed out based on the severity of the consequences corresponding to the diagnostic results of measurement anomalies.

[0008] The various data from the aforementioned metering equipment are input into the production anomaly monitoring and diagnosis model library via a thread; and / or the pushed alarm information includes the time, metering anomaly diagnosis result, metering equipment, and severity of consequences.

[0009] The second technical solution of the present invention is achieved through the following measures: an automatic oil well production anomaly monitoring and early warning device, comprising: The data acquisition unit collects various types of data from the metering equipment during the metering process. The monitoring and diagnosis unit inputs various data from the metering equipment into the production anomaly monitoring and diagnosis model library and outputs the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various models to verify whether the communication is normal, whether the instruments are normal, whether the upstream oil wells are normal, whether the upstream pipeline network is normal, and whether the metering process is normal. The alarm unit, in conjunction with a preset alarm mechanism, filters the diagnostic results of metering anomalies and pushes out corresponding alarm information.

[0010] The following are further optimizations and / or improvements to the above-mentioned technical solution: The above also includes: The model creation unit builds models and creates a library of production anomaly monitoring and diagnostic models. Alarm mechanism establishment unit, setting up alarm mechanisms; The management and configuration unit configures the monitoring metering equipment and model parameters, and manages user information.

[0011] This invention establishes a targeted production anomaly monitoring and diagnosis model library, automatically collects various data from metering equipment, uses the production anomaly monitoring and diagnosis model library for monitoring and diagnosis, obtains corresponding metering anomaly diagnosis results, and combines a preset alarm mechanism to push alarm information, realizing automatic production anomaly monitoring and early warning for oil wells, improving production efficiency, reducing production losses, and effectively eliminating invalid alarms and redundant alarms, improving the utilization rate of human resources, and avoiding safety accidents. Attached Figure Description

[0012] Appendix Figure 1 This is a flowchart of the method of the present invention.

[0013] Appendix Figure 2 This is a flowchart of the method for outputting alarm information in this invention.

[0014] Appendix Figure 3 This is a schematic diagram of a device structure according to the present invention.

[0015] Appendix Figure 4 This is a schematic diagram of another device structure according to the present invention. Detailed Implementation

[0016] The present invention is not limited to the following embodiments, and specific implementation methods can be determined according to the technical solutions and actual conditions of the present invention.

[0017] The present invention will be further described below with reference to embodiments and accompanying drawings: Example 1: As shown in the attached document Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for automatic monitoring and early warning of abnormal production in oil wells, including: Step S101: Collect various data from the metering equipment during the metering process; Step S102: Input various data from the metering equipment into the production anomaly monitoring and diagnosis model library, and output the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various models to verify whether the communication is normal, whether the instrument is normal, whether the upstream oil well is normal, whether the upstream pipeline is normal, and whether the metering process is normal. Step S103: Combine the preset alarm mechanism to filter the metering anomaly diagnosis results and push the corresponding alarm information.

[0018] This invention discloses an automatic production anomaly monitoring and early warning method for oil wells. It establishes a targeted production anomaly monitoring and diagnostic model library, automatically collects various data from metering equipment, uses the library for monitoring and diagnosis, obtains corresponding metering anomaly diagnostic results, and combines this with a preset alarm mechanism to push alarm information. This achieves automatic production anomaly monitoring and early warning for oil wells, improving production efficiency, reducing production losses, effectively eliminating invalid and redundant alarms, improving human resource utilization, and preventing safety accidents.

[0019] Example 2: As shown in the attached document Figure 2 As shown in the figure, an embodiment of the present invention discloses a method for automatic monitoring and early warning of abnormal production in oil wells, including: Step S201: Collect various data from the metering equipment during the metering process; Step S202: Input various data from the metering equipment into the production anomaly monitoring and diagnosis model library, and output the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various monitoring and diagnosis models to verify whether the communication is normal, whether the instrument is normal, whether the upstream oil well is normal, whether the upstream pipeline is normal, and whether the metering process is normal. Here, various data from the metering equipment are input into the production anomaly monitoring and diagnostic model library via threads.

[0020] The monitoring and diagnostic models in the production anomaly monitoring and diagnostic model library here are established based on the automatic production calculation process. During establishment, several model judgment rules can be identified and established first, and then corresponding monitoring and diagnostic models can be established based on these rules. In this embodiment, the monitoring and diagnostic models may include, but are not limited to: Monitoring and diagnostic model one verifies whether the instruments are functioning properly. The input parameters are sensor data, including pressure, oil temperature, load, and equipment operating status. The model rules include: sensor data falling to zero or negative values ​​when the equipment is running, and fluctuation of a certain parameter being less than 1% over 60 minutes. The output data is: if the model rules are met, an alarm is output for pressure change / temperature change / load falling to zero, indicating an instrument malfunction. Monitoring and diagnostic model two verifies whether the instruments are functioning properly. The input parameters are sensor data, including pressure, oil temperature, load, and equipment operating status. Model rules include: when the equipment is running, the number of times the real-time data shows a change of more than 20% between adjacent points within M consecutive minutes is greater than N. Output data: if the model rules are met, an alarm for abnormal fluctuations in pressure / temperature / load will be output. Here, M and N are set according to actual conditions. Monitoring and diagnostic model three verifies whether the upstream oil well is normal. Input parameters include back pressure, manifold pressure, separator pressure displayed when connected to the separator, oil temperature, load, and load span. Model rules include: back pressure < low value & back pressure < system pressure at this station & oil temperature < high-high value & low-low value < load < high-high value & low-low value < load span < high-high value. Output data: if conforming to model rules, outputs "pumping well pipeline rupture." Monitoring and diagnostic model four verifies whether the upstream oil well is normal. The input parameters include oil well back pressure and manifold pressure. The model rules include: the manifold pressure change is within the limit, and the back pressure exceeds the upper limit for M consecutive minutes. The output data is: if the model rules are met, the wellhead pipeline is blocked. Here, M minutes is set according to the actual situation. At the same time, the time threshold M must be calculated based on 3 data points per minute. Monitoring and diagnostic model five verifies whether the upstream pipeline network is normal; the input parameters include separator pressure and well back pressure; the model rules include: the system pressure at the metering station suddenly drops below 50%, and within 30 minutes, the back pressure of more than 3 wells is lower than P; the output data is: if the model rules are met, the oil gathering branch at the metering station is broken; here, P is set according to the actual situation. Monitoring and diagnostic model six verifies whether the metering system instruments and data transmission are normal. The input parameters include manifold pressure, separator pressure, manifold pressure, and equipment operating status. The model rules include: for K consecutive hours, the three data points A, B, and C on module A have not changed. The output data is: if the model rules are met, the output metering station module A will freeze. Here, K can be set according to the actual situation and can be set to 30 to 40 minutes. Monitoring and diagnostic model seven verifies whether the metering process is normal; the input parameters include separator pressure and separator liquid level; the model rules include: separator pressure < low-low value, separator liquid level > high-high value, manifold pressure < low-low value; the output data: if the model rules are met, the metering station safety valve will trip. Monitoring and diagnostic model eight verifies whether the metering process is normal; the input parameters include metering liquid level and oil pressure valve status; the model rules include: separator liquid level > high-high value & oil pressure valve status is closed; output data: if the model rules are met, the metering process is abnormal and the oil pressure valve is not open to pressurize oil. Monitoring and diagnostic model nine verifies whether the metering process is normal; the input parameters include separator status and lower liquid level switch status; the model rules include: 30 minutes, separator status = metering idle & lower liquid level switch status = off; output data: conforms to the model rules, outputting that the separator pressure in the metering room is too slow.

[0021] Step S203: Combine the preset alarm mechanism to filter the metering anomaly diagnosis results and push the corresponding alarm information.

[0022] The preset alarm mechanism here includes the first alarm generation condition and the second alarm generation condition; The first alarm generation conditions include alarm impact classification and impact degree classification conditions for each type of alarm impact; For example, the conditions for generating the first alarm can be set as follows: The types of impacts that can be triggered by an alarm include personal injury, environmental impact, and economic or production losses. The criteria for classifying the degree of impact include: In the category of personal injury impact, the degree of impact is determined based on whether the operator's response to a certain alarm avoids personal injury. If the operator's response to a certain alarm can avoid personal injury, it is set to the highest level. Among the environmental impact types, an impact that affects only a local area within the factory is considered minor; an impact that affects a local area surrounding the factory is considered significant; and an impact that severely affects a large area surrounding the factory is considered severe. Among the types of economic or production losses, the amount of economic or production loss determines the degree of impact, namely no impact, minor impact, significant impact, and severe impact.

[0023] The second alarm generation conditions include the allowable response time corresponding to the metrological anomaly diagnosis result, the degree of impact corresponding to the metrological anomaly diagnosis result, the severity of the consequences corresponding to the metrological anomaly diagnosis result, and the correlation among the three.

[0024] For example, the conditions for generating the second alarm can be set as follows: This section combines a pre-set alarm mechanism to filter measurement monitoring and diagnostic results and outputs corresponding alarm information, including: Step S2031: Obtain the measurement anomaly diagnosis results after monitoring and diagnosis by the production anomaly monitoring and diagnosis model library; Step S2032: Input the measurement anomaly diagnosis result into the first alarm generation condition to determine the corresponding alarm impact type and impact degree; Step S2033: Input the measurement anomaly diagnosis result and the corresponding impact degree into the second alarm generation conditions to determine the severity of the consequences corresponding to the measurement anomaly diagnosis result; Step S2034: An alarm message is pushed out based on the severity of the consequences corresponding to the metrological anomaly diagnosis result. This means that the alarm message is pushed out according to the severity level of the consequences. The pushed alarm message includes the time, metrological anomaly diagnosis result, metrological equipment, and severity of consequences.

[0025] Example 3: As shown in the attached document Figure 3 As shown in the figure, an embodiment of the present invention discloses an automatic production calculation anomaly monitoring and early warning device for oil wells, comprising: The data acquisition unit collects various types of data from the metering equipment during the metering process. The monitoring and diagnosis unit inputs various data from the metering equipment into the production anomaly monitoring and diagnosis model library and outputs the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various models to verify whether the communication is normal, whether the instruments are normal, whether the upstream oil wells are normal, whether the upstream pipeline network is normal, and whether the metering process is normal. The alarm unit, in conjunction with a preset alarm mechanism, filters the diagnostic results of metering anomalies and pushes out corresponding alarm information.

[0026] Example 4: As shown in the appendix Figure 4 As shown in the figure, an embodiment of the present invention discloses an automatic production calculation anomaly monitoring and early warning device for oil wells, comprising: The data acquisition unit collects various types of data from the metering equipment during the metering process. The monitoring and diagnosis unit inputs various data from the metering equipment into the production anomaly monitoring and diagnosis model library and outputs the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various models to verify whether the communication is normal, whether the instruments are normal, whether the upstream oil wells are normal, whether the upstream pipeline network is normal, and whether the metering process is normal. The alarm unit, in conjunction with a preset alarm mechanism, filters the diagnostic results of metering anomalies and pushes out corresponding alarm information; The model creation unit builds models and creates a library of production anomaly monitoring and diagnostic models. Alarm mechanism establishment unit, setting up alarm mechanisms; The management and configuration unit configures the monitoring metering equipment and model parameters, and manages user information.

[0027] Example 5: This embodiment of the invention discloses a storage medium storing a computer program that can be read by a computer. The computer program is configured to execute an automatic production monitoring and early warning method for oil wells during runtime.

[0028] The aforementioned storage media may include, but are not limited to, USB flash drives, read-only memory, portable hard drives, magnetic disks, optical disks, and other media capable of storing computer programs.

[0029] Example 6: This embodiment of the invention discloses an electronic device, including a processor and a memory. The memory stores a computer program, which is loaded and executed by the processor to realize an automatic production monitoring and early warning method for oil wells.

[0030] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. It can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0031] Storage devices can include, but are not limited to, USB flash drives, read-only memory, portable hard drives, magnetic disks, optical disks, and other media that can store computer programs.

[0032] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0033] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0034] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0035] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0036] The above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and optimal implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A method for automatic monitoring and early warning of abnormal production in oil wells, characterized in that, include: Collect various data from the metering equipment during the metering process; Input various data from metering equipment into the production anomaly monitoring and diagnosis model library, and output the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various monitoring and diagnosis models to verify whether communication is normal, whether instruments are normal, whether upstream oil wells are normal, whether upstream pipelines are normal, and whether the metering process is normal. Based on the preset alarm mechanism, the metering anomaly diagnosis results are filtered, and corresponding alarm information is pushed, including: Obtain the diagnostic results of measurement anomalies after monitoring and diagnosis by the production anomaly monitoring and diagnosis model library; The results of the measurement anomaly diagnosis are used to determine the corresponding alarm impact type and impact degree by taking the first alarm generation conditions into the instrument; The results of the metrological anomaly diagnosis and the corresponding degree of impact are incorporated into the second alarm generation conditions to determine the severity of the consequences corresponding to the metrological anomaly diagnosis results. Alarm information will be pushed based on the severity of the consequences corresponding to the abnormal measurement diagnosis results. The alarm mechanism includes a first alarm generation condition and a second alarm generation condition. The first alarm generation conditions include alarm impact classification and impact degree classification conditions for each type of alarm impact; The second alarm generation conditions include the allowable response time corresponding to the measurement anomaly diagnosis result, the degree of impact corresponding to the measurement anomaly diagnosis result, the severity of the consequences corresponding to the measurement anomaly diagnosis result, and the correlation between the three. The alarm information pushed includes the time, the diagnostic results of the metering anomaly, the metering equipment, and the severity of the consequences.

2. The method for automatic production monitoring and early warning of oil wells according to claim 1, characterized in that, Various data from the metering equipment are input into the production anomaly monitoring and diagnostic model library via a thread.

3. An automatic production calculation anomaly monitoring and early warning device for oil wells, wherein the automatic production calculation anomaly monitoring and early warning device for oil wells uses the automatic production calculation anomaly monitoring and early warning method as described in claim 1 or 2, characterized in that, include: The data acquisition unit collects various types of data from the metering equipment during the metering process. The monitoring and diagnosis unit inputs various data from the metering equipment into the production anomaly monitoring and diagnosis model library and outputs the metering anomaly diagnosis results. The production anomaly monitoring and diagnosis model library includes various models to verify whether the communication is normal, whether the instruments are normal, whether the upstream oil wells are normal, whether the upstream pipeline network is normal, and whether the metering process is normal. The alarm unit, in conjunction with a preset alarm mechanism, filters the diagnostic results of metering anomalies and pushes out corresponding alarm information.

4. The automatic production monitoring and early warning device for oil wells according to claim 3, characterized in that, Also includes: The model creation unit builds models and creates a library of production anomaly monitoring and diagnostic models. Alarm mechanism establishment unit, setting up alarm mechanisms; The management and configuration unit configures the monitoring metering equipment and model parameters, and manages user information.

5. A storage medium, characterized in that, The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the oil well automatic production calculation anomaly monitoring and early warning method as described in claim 1 or 2 when it is run.

6. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, which is loaded and executed by the processor to implement the oil well automatic production anomaly monitoring and early warning method as described in claim 1 or 2.

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