Coiled tubing shaft operation tool monitoring method and device, medium and equipment

Abstract

The invention discloses a coiled tubing shaft operation tool monitoring method and device, a medium and equipment. The method comprises the steps that data of a plurality of sensors are obtained; preprocessing the data of the plurality of sensors; acquiring necessary data of the plurality of sensors; performing anomaly detection based on the necessary data and an anomaly detection algorithm; if an abnormal condition is detected, a decision-making measure is triggered; on the basis of the necessary data, comprehensive characteristics are obtained, and the comprehensive characteristics are used for judging the state of the tool; and sending the comprehensive characteristics to a monitoring center. According to the method, the characteristics of a plurality of sensors are integrated, the necessary data of the plurality of sensors are acquired, and the state and working condition of the coiled tubing wellbore operation tool can be analyzed and judged more accurately based on the acquired necessary data and the anomaly detection algorithm, so that the abnormal condition of the tool can be found in time, and corresponding measures are taken; therefore, the horizontal well continuous oil pipe shaft operation tool can be normally used.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development, and particularly relates to a coiled tubing wellbore operation tool monitoring method, device, medium and equipment. BACKGROUND

[0002] Coiled tubing is a flexible and continuous pipe wound on a large reel. It is an integral part of well servicing operations in the oil and gas industry. Unlike traditional jointed pipe, coiled tubing does not need to be connected multiple times and can be inserted into a well seamlessly. This technology has revolutionized every aspect of well maintenance, including stimulation treatments, cleanout, and even drilling operations in some cases. By using coiled tubing, operators can more effectively access deep or deviated wells while minimizing downtime.

[0003] In the prior art, a coiled tubing operation well bottom wireless data transmission method and device with publication number CN101737035A uses downhole construction data obtained downhole, which is converted into an acoustic vibration signal after being encoded and converted, and then transmitted to the ground using the coiled tubing body as a signal transmission channel, and received by the ground acoustic sensor, decoded and restored to the downhole construction data, and finally obtained by the monitoring computer. This method uses the coiled tubing body as a signal transmission channel to achieve wireless transmission of downhole construction data, solves the technical problem of not being able to use wired data transmission in coiled tubing operations, and can obtain real-time downhole tool position data, and can be widely used in the fields of coiled tubing stimulation operations, coiled tubing perforation, coiled tubing drilling, horizontal well downhole testing, and downhole production data monitoring.

[0004] However, this method can solve the technical problem of not being able to use wired data transmission in coiled tubing operations and can obtain real-time downhole tool position data, but it cannot monitor the running state, working parameters and environmental conditions of the coiled tubing wellbore operation tool in real time, so it cannot discover abnormal conditions of the tool in time, which may cause damage to the tool. SUMMARY

[0005] The main purpose of the present application is to provide a coiled tubing wellbore operation tool monitoring method, which aims to solve the technical problem that abnormal conditions cannot be discovered in time during the operation of the coiled tubing wellbore operation tool, thereby causing damage to the tool.

[0006] To achieve the above object, the application provides a coiled tubing wellbore operation tool monitoring method, which comprises the following steps: acquiring data of multiple sensors; preprocessing data of the multiple sensors; acquiring necessary data of the multiple sensors; performing abnormality detection based on the necessary data and an abnormality detection algorithm; triggering a decision measure if an abnormal condition is detected; acquiring comprehensive features based on the necessary data, the comprehensive features being used to determine a state of a tool; and sending the comprehensive features to a monitoring center.

[0007] Optionally, the multiple sensors comprise a pressure sensor, a temperature sensor, a vibration sensor, a position sensor and a liquid level sensor, and each of the sensors is arranged on a coiled tubing wellbore operation tool.

[0008] Optionally, the preprocessing of the data of the multiple sensors comprises the following steps: cleaning the data of the multiple sensors; filtering the cleaned data of the multiple sensors; and calibrating the filtered data of the multiple sensors.

[0009] Optionally, the acquisition of the necessary data of the multiple sensors comprises the following steps: acquiring a maximum pressure value and a pressure fluctuation degree of the pressure sensor; acquiring an average temperature and a temperature change rate of the temperature sensor; acquiring a mechanical vibration amount of the vibration sensor; acquiring geographical position information of the position sensor; and acquiring liquid level information of the liquid level sensor.

[0010] Optionally, the abnormality detection algorithm comprises statistical analysis and machine learning.

[0011] Optionally, the decision measure comprises alarming, stopping or remote control.

[0012] Optionally, the acquisition of the comprehensive features based on the necessary data comprises the following steps: fusing the necessary data based on a logic fusion method; and acquiring the comprehensive features.

[0013] Optionally, the logic fusion method comprises the following steps: setting a logic judgment rule based on a logic relationship between the necessary data of the multiple sensors; and fusing the necessary data based on the logic judgment rule to acquire comprehensive logic features, which are used to determine the state of the tool.

[0014] Optionally, the logic judgment rule comprises the following conditions: if P is abnormally high and T is abnormally increased, it is determined that a fault exists; if P is abnormally low and H is abnormally increased, it is determined that a fault exists; if V is abnormally large and F is abnormally decreased, it is determined that a fault exists; if P is abnormally high and V is abnormally large, it is determined that a fault exists; and if T is abnormally increased and F is abnormally decreased, it is determined that a fault exists.

[0015] Optionally, after the necessary data is fused based on the logic fusion method, the coiled tubing wellbore operation tool monitoring method comprises: smoothing pressure data based on an exponential weighted moving average algorithm; obtaining the smoothed pressure data, wherein the pressure data comprises the maximum pressure value and the pressure fluctuation degree; and analyzing and processing the smoothed pressure data.

[0016] In addition, to achieve the above-mentioned purpose, the embodiment of the present application further provides a coiled tubing wellbore operation tool monitoring device, which comprises: a first obtaining module for obtaining monitoring data related to fracture propagation; a preprocessing module for preprocessing data of a plurality of sensors; a second obtaining module for obtaining necessary data of a plurality of sensors; an anomaly detection module for performing anomaly detection based on the necessary data and an anomaly detection algorithm; a decision module for triggering decision measures when an abnormal situation is detected; a third obtaining module for evaluating and optimizing the obtained training model; a generating module for obtaining comprehensive features based on the necessary data, wherein the comprehensive features are used to judge the state of the tool; and a sending module for sending the comprehensive features to a monitoring center.

[0017] In addition, to achieve the above-mentioned purpose, the embodiment of the present application further provides a computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the coiled tubing wellbore operation tool monitoring method of any of the embodiments of the present application.

[0018] In addition, to achieve the above-mentioned purpose, the embodiment of the present application further provides a computing device, which comprises: at least one processor, a memory and an input output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the coiled tubing wellbore operation tool monitoring method of any of the embodiments of the present application.

[0019] The coiled tubing wellbore operation tool monitoring method provided by the embodiment of the present application can more accurately analyze and judge the state and working condition of the coiled tubing wellbore operation tool by comprehensively considering the characteristics of a plurality of sensors, obtaining necessary data of the plurality of sensors, and based on the obtained necessary data and an anomaly detection algorithm, so as to facilitate timely discovery of abnormal situations of the tool and taking of corresponding measures, so as to ensure that the coiled tubing wellbore operation tool of the horizontal well can be normally used. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of the coiled tubing wellbore operation tool monitoring method provided by the embodiment of the present application;

[0021] Figure 2A structural block diagram of the coiled tubing wellbore operation tool monitoring device provided in the embodiments of this application;

[0022] Figure 3 A schematic diagram of the structure of a medium provided in an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures:

[0025] 300-Continuous tubing wellbore operation tool monitoring device, 310-First acquisition module, 320-Preprocessing module, 330-Second acquisition module, 340-Anomaly detection module, 350-Decision module, 360-Third acquisition module, 370-Generation module, 380-Sending module.

[0026] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0027] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the application. Rather, these embodiments are provided to make the disclosure more thorough and complete, and to fully convey the scope of the disclosure to those skilled in the art.

[0028] In existing technologies, low-permeability tight oil reservoirs are often developed using fractured horizontal wells due to their poor reservoir properties. With the continuous development of coiled tubing technology, the tools for horizontal well coiled tubing operations are becoming increasingly sophisticated. To further monitor the tool's operating status, parameters, and environmental conditions, and promptly detect any abnormalities, and to take appropriate measures, there is an urgent need for monitoring methods specifically designed for the development of horizontal well coiled tubing tools. These methods would allow for real-time monitoring of the tool's operating status, parameters, and environmental conditions, and the transmission of data to a monitoring center via telemetry for analysis and processing. This would facilitate timely detection of tool anomalies and the implementation of appropriate countermeasures.

[0029] To address the aforementioned technical problems, this application provides a method for monitoring coiled tubing wellbore operating tools. This method can be executed by a computer, such as... Figure 1 As shown, the method may include the following steps:

[0030] S10 acquires data from multiple sensors.

[0031] The sensors include pressure sensors, temperature sensors, vibration sensors, position sensors, and level sensors. Each sensor is mounted on the tubing wellbore working tool and is connected to a telemetry system. The following is a detailed description of each sensor:

[0032] (1) A pressure sensor is a sensor or instrument that converts input mechanical pressure in a gas or liquid into an electrical output signal. A pressure sensor consists of a pressure-sensitive element that can measure, detect, or monitor applied pressure and electronic components that convert the information into an electrical output signal. Pressure sensors have two important characteristics: robustness to withstand high pressure; and elasticity to minimize deformation and return to their original shape when compressed. In short, a pressure sensor converts pressure into a small electrical signal, which is then transmitted and displayed.

[0033] (2) A temperature sensor is a device that measures the degree of hotness or coldness of an object, providing temperature measurement in a readable form through an electrical signal. The most common types are thermocouples and resistance temperature detectors.

[0034] (3) Vibration sensors are one of the key components in testing technology. Their main function is to receive mechanical quantities and convert them into proportional electrical quantities. Since it is also an electromechanical conversion device, it is sometimes called a transducer, vibration pickup, etc. The vibration sensor does not directly convert the original mechanical quantity to be measured into an electrical quantity. Instead, it uses the original mechanical quantity to be measured as the input quantity of the vibration sensor, which is then received by the mechanical receiving part to form another mechanical quantity suitable for conversion. Finally, the electromechanical conversion part converts it into an electrical quantity.

[0035] (4) The position sensor is used to detect the position of an object, which means that the position sensor is referenced to a fixed point or from a fixed point or position, and then the position sensor provides position feedback.

[0036] (5) A liquid level sensor (also known as a hydrostatic level gauge / liquid level transmitter / liquid level sensor / water level sensor) is a pressure sensor that measures the liquid level.

[0037] S20, preprocess the data from the multiple sensors.

[0038] In an exemplary embodiment, step S20 may include:

[0039] S210, Clean the data from the multiple sensors;

[0040] S220, Filter the data after cleaning from the multiple sensors;

[0041] S230, calibrate the filtered data from the multiple sensors.

[0042] Specifically, data cleaning can include removing duplicate data, filling in missing data, handling outlier data, and correcting erroneous data. The purpose of data cleaning is to improve the quality and accuracy of data and ensure the correctness and reliability of subsequent analysis and modeling. There are two types of data cleaning methods: manual cleaning and automatic cleaning. Automatic cleaning usually uses computer programs and algorithms to process and clean data.

[0043] Furthermore, during the process of scanning and digitizing physical objects, errors, redundancies, and measurement noise from the scanning environment are inevitably introduced. These points can significantly impact the subsequent reconstruction of the physical model. In order to better extract the feature data of the physical object, data filtering must be performed to remove these errors. Typically, data filtering is carried out using filtering techniques in digital signal processing and image processing.

[0044] Data calibration refers to a set of operations performed under specified conditions to determine the relationship between the readings of measuring instruments and the corresponding reproducible values ​​of metrological standards.

[0045] S30, acquire the necessary data from the multiple sensors.

[0046] In an exemplary embodiment, step S30 may specifically include the following steps:

[0047] S310, obtains the maximum pressure value and pressure fluctuation level of the pressure sensor;

[0048] S320, acquires the average temperature and temperature change rate of the temperature sensor;

[0049] S330, acquire the mechanical vibration amount of the vibration sensor;

[0050] S340, Obtain the geographical location information of the location sensor;

[0051] S350, acquire the liquid level information of the liquid level sensor.

[0052] S40, based on the necessary data and the anomaly detection algorithm, perform anomaly detection.

[0053] The anomaly detection algorithm includes statistical analysis and machine learning.

[0054] S50: If an anomaly is detected, a decision-making action is triggered.

[0055] In this embodiment, the decision-making measures include alarm, shutdown, or remote control.

[0056] S60, based on the necessary data, obtain comprehensive features, which are used to determine the state of the tool.

[0057] In an exemplary embodiment, step S60 may include the following steps:

[0058] S610, the necessary data is fused based on the logical fusion method;

[0059] S620, acquire comprehensive features.

[0060] Specifically, the logical fusion method may include the following steps:

[0061] S611, based on the logical relationships between the necessary data from the multiple sensors, logical judgment rules are set. For example, when the pressure sensor's pressure is abnormally high and the temperature sensor's temperature is abnormally high, it can be determined that the tool may be faulty. By defining and combining logical rules, a comprehensive logical characteristic can be obtained to determine the tool's state.

[0062] S612, the necessary data are fused based on the logical judgment rules to obtain comprehensive logical features for judging the state of the tool.

[0063] In an exemplary embodiment, the logical judgment rules include: if P is abnormally high and T is abnormally high, then a fault is determined to exist; if P is abnormally low and H is abnormally high, then a fault is determined to exist; if V is abnormally large and F is abnormally low, then a fault is determined to exist; if P is abnormally high and V is abnormally large, then a fault is determined to exist; if T is abnormally high and F is abnormally low, then a fault is determined to exist.

[0064] Furthermore, the logical fusion method can also be replaced by the weighted average method or principal component analysis method, and different weights can be assigned to different features according to the performance and importance of each sensor.

[0065] In this exemplary embodiment, by integrating the characteristics of multiple sensors, the fusion algorithm can more accurately analyze and determine the tool's status and operational condition. This comprehensive monitoring method can improve the sensitivity and reliability of monitoring, reduce false alarms and missed alarms, and provide more effective support for the operation and maintenance of the tool.

[0066] In an exemplary embodiment, after step S60, the following steps may also be included:

[0067] S61, smooths stress data based on exponentially weighted moving average algorithm;

[0068] S62, Obtain the smoothed pressure data, the pressure data including the maximum pressure value and the degree of pressure fluctuation.

[0069] S63, analyze and process the pressure data after smoothing.

[0070] Specifically, the Exponentially Weighted Moving Average (EWMA) algorithm can smooth the pressure data of horizontal well coiled tubing wellbore tools and calculate the smoothed pressure values ​​in real time. By further analyzing and processing the smoothed data, we can more sensitively capture pressure change trends and promptly detect anomalies. This monitoring method can help operators provide early warnings and take appropriate measures to ensure the safe operation of horizontal well coiled tubing wellbore tools. The algorithm will be illustrated below.

[0071] Suppose we have feature data from the following five sensors that we need to fuse: pressure sensor (P), temperature sensor (T), humidity sensor (H), vibration sensor (V), and flow sensor (F).

[0072] We can define the following logical judgment rules to fuse the features of these sensors:

[0073] If P is abnormally high and T is abnormally high, then a fault is identified.

[0074] If P is abnormally low and H is abnormally high, then a fault is identified.

[0075] If V is abnormally large and F is abnormally low, then a fault is identified.

[0076] If P is abnormally high and V is abnormally large, then a fault is identified.

[0077] If T increases abnormally and F decreases abnormally, then a fault is identified.

[0078] By defining and combining these logical rules, a comprehensive logical feature can be obtained to determine the state of the tool. For example, we can use "fault exists" as the comprehensive logical feature. When any of the above logical rules are met, the comprehensive feature is set to 1 (indicating a fault exists); otherwise, it is set to 0 (indicating normal operation).

[0079] In this way, we can use logical fusion to synthesize the features of different sensors, obtaining a comprehensive logical feature to determine the tool's status. The logical rules can be adjusted and optimized according to actual needs to adapt to different monitoring requirements and sensor characteristics.

[0080] Furthermore, when adapting to different monitoring needs and sensor characteristics, the technology can be further deepened by employing more complex logical fusion methods. Here is an example:

[0081] Suppose that the characteristic data of the following sensors need to be fused: pressure sensor (P), temperature sensor (T), humidity sensor (H), vibration sensor (V), and flow sensor (F).

[0082] First, the features of each sensor are normalized, mapping their value range to between 0 and 1. This ensures that features from different sensors have the same weight.

[0083] Then, thresholds are set for the characteristics of each sensor to define abnormal states. For example, for a pressure sensor, an abnormal state can be defined as a pressure value higher than a certain threshold; for a temperature sensor, an abnormal state can be defined as a temperature value exceeding a certain threshold.

[0084] Next, logical judgment rules are designed based on the logical relationships between the characteristics of different sensors. These logical judgment rules can be obtained based on expert experience, data analysis, or machine learning methods. For example, if P is abnormally high and T is abnormally high, a fault is judged to exist; if P is abnormally low and H is abnormally high, a fault is judged to exist. The specific logical judgment rules are as described above and will not be elaborated here.

[0085] For each logical rule, a weight is assigned based on whether the sensor's characteristics satisfy the rule. For example, if the pressure sensor's characteristics satisfy the rule of abnormally high P, it is given a higher weight; if the temperature sensor's characteristics satisfy the rule of abnormally high T, it is given a lower weight.

[0086] Finally, features are merged using a weighted summation method. For each feature, it is multiplied by its corresponding weight, and the weighted sum of all features is then used as the composite feature. Based on the value of the composite feature, the state of the tool can be determined; for example, exceeding a certain threshold indicates a malfunction.

[0087] This example demonstrates that in logical fusion methods, logical rules and weights can be flexibly defined based on different monitoring needs and sensor characteristics to achieve more accurate tool status judgments. This method requires adjustment and optimization according to specific circumstances to adapt to different application scenarios.

[0088] S70, the integrated features are sent to the monitoring center.

[0089] Specifically, data from multiple sensors can be transmitted to the monitoring center via a telemetry system. The monitoring center analyzes and processes the received information to facilitate timely detection of tool malfunctions and to take appropriate measures.

[0090] This application provides a method for monitoring coiled tubing wellbore tools. By integrating the characteristics of multiple sensors, the necessary data from these sensors is acquired. Based on the acquired data and anomaly detection algorithms, the status and working condition of the coiled tubing wellbore tools can be analyzed and judged more accurately. This facilitates timely detection of tool anomalies and allows for the implementation of corresponding measures to ensure the normal operation of horizontal well coiled tubing wellbore tools.

[0091] Based on the above embodiments, refer to Figure 2 Another embodiment of this application also provides a coiled tubing wellbore operation tool monitoring device, which may include the following modules:

[0092] The first acquisition module 310 is used to acquire monitoring data related to crack propagation;

[0093] Preprocessing module 320 is used to preprocess the data from the multiple sensors;

[0094] The second acquisition module 330 is used to acquire the necessary data from the multiple sensors;

[0095] Anomaly detection module 340 is used to perform anomaly detection based on the necessary data and anomaly detection algorithm;

[0096] The decision module 350 is used to trigger decision-making measures when an abnormal situation is detected.

[0097] The third acquisition module 360 ​​is used to evaluate and optimize the acquired training model;

[0098] The generation module 370 is used to obtain comprehensive features based on the necessary data, and the comprehensive features are used to determine the state of the tool;

[0099] The sending module 380 is used to send the comprehensive features to the monitoring center.

[0100] Based on the above embodiments, this application also provides a computer-readable storage medium, see reference. Figure 3The computer-readable storage medium shown is an optical disc 50, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above method embodiments, such as: acquiring data from multiple sensors; preprocessing the data from the multiple sensors; acquiring necessary data from the multiple sensors; performing anomaly detection based on the necessary data and an anomaly detection algorithm; triggering decision-making measures if an anomaly is detected; acquiring comprehensive features based on the necessary data, the comprehensive features being used to determine the state of the tool; and sending the comprehensive features to the monitoring center. The specific implementation methods of each step will not be repeated here.

[0101] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0102] Furthermore, based on the above embodiments, this application also provides a computing device. Figure 4 A block diagram is shown of an exemplary computing device 60 suitable for implementing embodiments of the present application. The computing device 60 may be a computer system or a server. Figure 4 The computing device 60 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0103] like Figure 4 As shown, the components of computing device 60 may include, but are not limited to: one or more processors or processing units 601, system memory 602, and bus 603 connecting different system components (including system memory 602 and processing unit 601).

[0104] The computing device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 60, including volatile and non-volatile media, removable and non-removable media.

[0105] System memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 6021 and / or cache memory 6022. Computing device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 6023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4(Not shown in the image, usually referred to as "hard drive"). Although not shown in Figure 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to a bus 603 that connects different system components via one or more data media interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0106] A program / utility 6025 having a set (at least one) of program modules 6024 may be stored, for example, in system memory 602, and such program modules 6024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 6024 typically perform the functions and / or methods described in the embodiments of this application.

[0107] The computing device 60 can also communicate with one or more external devices 604 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 605. Furthermore, the computing device 60 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 606. Figure 4 As shown, network adapter 606 communicates with other modules of computing device 60 (such as processing unit 601, etc.) via bus 603, which connects different system components. It should be understood that, although... Figure 4 Other hardware and / or software modules may be used in conjunction with computing device 60, as not shown in the diagram.

[0108] The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602. For example, it acquires data from multiple sensors; preprocesses the data from the multiple sensors; acquires necessary data from the multiple sensors; performs anomaly detection based on the necessary data and an anomaly detection algorithm; triggers decision-making measures if an anomaly is detected; acquires comprehensive features based on the necessary data, which are used to determine the tool's status; and sends the comprehensive features to the monitoring center. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the coiled tubing wellbore operation tool monitoring device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules.

[0109] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0110] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0114] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

[0116] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A method for monitoring coiled tubing wellbore operating tools, characterized in that, The monitoring method for coiled tubing wellbore operating tools includes: Acquire data from multiple sensors; Preprocess the data from the multiple sensors; Acquire the necessary data from multiple sensors; Anomaly detection is performed based on the necessary data and anomaly detection algorithm. If an anomaly is detected, a decision-making action is triggered; Based on the necessary data, comprehensive features are obtained, which are used to determine the state of the tool. The comprehensive features are sent to the monitoring center.

2. The method for monitoring coiled tubing wellbore operating tools according to claim 1, characterized in that, The plurality of sensors include a pressure sensor, a temperature sensor, a vibration sensor, a position sensor, and a liquid level sensor, each of which is mounted on the coiled tubing wellbore working tool.

3. The method for monitoring coiled tubing wellbore operating tools according to claim 1, characterized in that, The preprocessing of data from the multiple sensors includes: The data from the multiple sensors are cleaned; Filter the data after cleaning from the multiple sensors; The filtered data from the multiple sensors are calibrated.

4. The method for monitoring coiled tubing wellbore operation tools according to claim 2, characterized in that, The acquisition of necessary data from the multiple sensors includes: Obtain the maximum pressure value and pressure fluctuation level of the pressure sensor; The average temperature and temperature change rate of the temperature sensor are obtained. The mechanical vibration magnitude of the vibration sensor is obtained; Obtain the geographical location information of the location sensor; Obtain the liquid level information from the liquid level sensor.

5. The method for monitoring coiled tubing wellbore operating tools according to claim 1, characterized in that, The anomaly detection algorithm includes statistical analysis and machine learning.

6. The method for monitoring coiled tubing wellbore operating tools according to claim 1, characterized in that, The decision-making measures include alarms, shutdowns, or remote control.

7. The method for monitoring coiled tubing wellbore operating tools according to claim 1, characterized in that, The process of obtaining comprehensive features based on the necessary data includes: The necessary data is fused based on the logical fusion method; Obtain comprehensive features.

8. The method for monitoring coiled tubing wellbore operating tools according to claim 7, characterized in that, The logical fusion method includes: Based on the logical relationship between the necessary data from the multiple sensors, logical judgment rules are set; The necessary data are fused based on the aforementioned logical judgment rules to obtain comprehensive logical features, which are used to determine the state of the tool.

9. The method for monitoring coiled tubing wellbore operating tools according to claim 8, characterized in that, The logical judgment rules include: If P is abnormally high and T is abnormally high, then a fault is identified. If P is abnormally low and H is abnormally high, then a fault is identified. If V is abnormally large and F is abnormally low, then a fault is identified. If P is abnormally high and V is abnormally large, then a fault is determined to exist; If T increases abnormally and F decreases abnormally, then a fault is identified.

10. The method for monitoring coiled tubing wellbore operating tools according to claim 7, characterized in that, After fusing the necessary data using the logic fusion method, the coiled tubing wellbore operation tool monitoring method further includes: The stress data is smoothed using an exponentially weighted moving average algorithm. Obtain smoothed pressure data, the pressure data including the maximum pressure value and the degree of pressure fluctuation; The pressure data after smoothing is analyzed and processed.

11. A monitoring device for coiled tubing wellbore operation tools, characterized in that, include: The first acquisition module is used to acquire monitoring data related to crack propagation; A preprocessing module is used to preprocess the data from the multiple sensors; The second acquisition module is used to acquire the necessary data from the multiple sensors; An anomaly detection module is used to perform anomaly detection based on the necessary data and anomaly detection algorithm; The decision-making module is used to trigger decision-making measures when an anomaly is detected. The third acquisition module is used to evaluate and optimize the acquired training model; The generation module is used to obtain comprehensive features based on the necessary data, and the comprehensive features are used to determine the state of the tool; The sending module is used to send the comprehensive features to the monitoring center.

12. A computer-readable storage medium, characterized in that, It includes instructions that, when executed on a computer, cause the computer to perform the coiled tubing wellbore operation tool monitoring method according to any one of claims 1 to 9.

13. A computing device, characterized in that, The computing device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the coiled tubing wellbore operation tool monitoring method according to any one of claims 1 to 9.

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