Abnormity detection method and device for safe logging and readable storage medium

Abstract

The invention discloses an anomaly detection method and device for safe logging and a readable storage medium, and relates to the field of electronic information. According to the anomaly detection method for safe logging, the detection data of the winch is detected, logging information is imported, the deviation value between the detection data and the standard data is judged, the standard data is data, obtained through a pre-experiment, of normal operation of the winch in logging, and whether the winch is abnormal or not is judged according to the deviation value; compared with a previous manual calculation mode, the method has the advantages that the standard data are obtained through experiments in advance, and whether the winch is abnormal or not is judged according to the standard data, so that an accurate result of anomaly detection can be obtained only through basic data comparison without manual calculation.

Description

Technical Field

[0001] The present application relates to the field of electronic information, and in particular to an abnormality detection method, device and readable storage medium for safety well logging. Background Art

[0002] In recent years, with the development of information technology, all walks of life have carried out relative detection of data. For the well logging industry, most of the existing technologies are through conventional control panels, which only display basic parameters such as depth, speed, tension, etc., and the display method is digital display or one-dimensional graphic display.

[0003] In the prior art, the control panel of the mainstream logging system only displays basic parameters such as depth, speed, tension and differential tension in digital or one-dimensional graphical form. Therefore, abnormality detection is mainly based on the experience of the staff, and abnormality judgment based on the above parameters depends on the experience of the technicians. At the same time, since the control panel of the existing logging system only displays basic parameters in digital or one-dimensional graphical form, the technicians calculate based on the parameters to obtain whether an abnormal state occurs. When the deviation of the calculation result reaches 15-30%, it cannot truly reflect the downhole situation, and since the specific solution cannot be obtained due to manual calculation, it cannot be processed in time.

[0004] In view of the above-mentioned technologies, it is an urgent problem to be solved by those skilled in the art to find an abnormality detection method for safety logging that can timely and truly reflect the downhole situation. Summary of the invention

[0005] The purpose of the present application is to provide a method, device and readable storage medium for detecting anomalies in safety logging, so as to solve the problem that the current anomaly detection in safety logging is mainly based on the experience of the staff, and thus the anomaly judgment is made based on the above parameters, which depends on the experience of the technicians. At the same time, since the control panel of the existing logging system only displays basic parameters in digital or one-dimensional graphical form, the technicians calculate based on the parameters to obtain whether an abnormal state occurs. When the deviation of the calculation result reaches 15-30%, it cannot truly reflect the downhole condition, and since the specific solution cannot be obtained due to manual calculation, the problem cannot be dealt with in time.

[0006] To solve the above problems, the present application provides a method for detecting anomalies in safety well logging, the method comprising:

[0007] Detect the winch test data and import the logging information;

[0008] Determine a deviation value between the detection data and standard data, wherein the standard data is data of normal operation of the drawworks in the well logging obtained through a preliminary experiment;

[0009] Determining whether the deviation value meets a preset standard;

[0010] If not, it is determined that the winch is abnormal.

[0011] The generation of the standard data includes the following steps:

[0012] Importing a data file, wherein the data file includes well deviation data, azimuth data, and depth data;

[0013] Preprocessing the well deviation data to generate dogleg, displacement, and vertical depth, and establishing a well trajectory model;

[0014] Obtaining the theoretical tension of the drawworks, and dividing the well logging into a vertical well section, a deflected well section, a build-up section, a large dogleg section, and a bottom well section according to the wellbore trajectory model, the azimuth data, and the depth data;

[0015] The data of the normal operation of the winch in different sections are obtained and used as standard data.

[0016] Preferably, the detection data includes speed, tension and depth, and judging whether the deviation value meets a preset standard according to the deviation value includes:

[0017] The deviation values ​​of the tension, speed and depth of the winch in the well section and the standard tension, speed and depth are detected in sequence according to the priority, and it is determined whether the deviation values ​​meet the preset standards.

[0018] Preferably, after determining that the winch is abnormal, the method further includes:

[0019] The tension, speed and depth related anomalies are processed in order according to the priority.

[0020] Preferably, it also includes:

[0021] Counting the well sections where the frequency of occurrence of the abnormal state of the winch exceeds a threshold, and defining the well sections as dangerous well sections;

[0022] When the winch is located in the dangerous well section, a warning command is issued.

[0023] Preferably, it also includes:

[0024] At every preset time, historical data of the normal operation of the drawworks within the preset time are counted, and the historical data are used as the normal operation data of the drawworks in the well logging to correct the standard data.

[0025] Preferably, it also includes:

[0026] If an abnormality is detected in the winch, an alarm is issued.

[0027] In order to solve the above problems, the present application also provides a well logging safety detection device, comprising:

[0028] The detection module is used to detect the detection data of the winch and import the logging information;

[0029] A determination module, used to determine a deviation value between the detection data and standard data, wherein the standard data is data of normal operation of the drawworks in the well logging obtained through a preliminary experiment;

[0030] A judgment module, used to judge whether the deviation value meets the preset standard, and if not, activate the abnormal module;

[0031] An abnormality module, used for determining that an abnormality occurs in the winch;

[0032] An import module is used to import data files, wherein the data files include well deviation data, azimuth data, and depth data;

[0033] A preprocessing module, used for preprocessing the well deviation data, generating doglegs, displacements, vertical depths, and establishing a well trajectory model;

[0034] A calculation module, used for obtaining the theoretical tension of the drawworks, and dividing the well logging into a vertical well section, a deflected well section, a build-up section, a large dogleg section, and a bottom well section according to the wellbore trajectory model, the azimuth data, and the depth data;

[0035] The acquisition module is used to acquire the data of the normal operation of the winch in different sections and use the data as standard data.

[0036] To solve the above problems, the present application also provides a well logging safety detection device, including a memory for storing a computer program;

[0037] A processor is used to implement the steps of the well logging safety detection device as described above when executing the computer program.

[0038] To solve the above problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the anomaly detection method for safety logging as described above are implemented.

[0039] The abnormality detection method for safety logging provided in the present application detects the detection data of the winch and imports the logging information to determine the deviation value between the detection data and the standard data. The standard data is the data of the normal operation of the winch in the logging obtained through preliminary experiments. According to the deviation value, it is determined whether the winch is abnormal. Compared with the previous method of manual calculation, the standard data is obtained through preliminary experiments in the present scheme, and the abnormality of the winch is determined based on the standard data. Therefore, there is no need for manual calculation, and only basic data comparison is required to obtain accurate results of abnormality detection.

[0040] The anomaly detection device for safety logging and the computer-readable storage medium provided in the embodiments of the present application correspond to the anomaly detection method for safety logging described above, and therefore the beneficial effects are the same as above and will not be elaborated herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A flow chart of an abnormality detection method for safety well logging provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of an abnormality detection device for safety well logging provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of an abnormality detection device for safety well logging provided in another embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The core of the present application is to provide a method, device and readable storage medium for detecting anomalies in safety logging, so as to solve the problem that the current anomaly detection in safety logging is mainly based on the experience of the staff, and thus the anomaly judgment is made based on the above parameters, which depends on the experience of the technicians. At the same time, since the control panel of the existing logging system only displays the basic parameters in digital or one-dimensional graphical form, the technicians calculate based on the parameters to obtain whether an abnormal state occurs. When the deviation of the calculation result reaches 15-30%, it cannot truly reflect the downhole condition, and since the specific solution cannot be obtained due to manual calculation, the problem cannot be dealt with in time.

[0047] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0048] Figure 1 A flow chart of an abnormality detection method for safety logging provided in an embodiment of the present application is as follows: Figure 1 As shown, the method includes:

[0049] S10: Detect the detection data of the winch and import the logging information;

[0050] It should be noted that well logging, also known as geophysical logging or mine geophysics, or logging for short, is a method of measuring geophysical parameters by using the geophysical properties of rock formations, such as electrochemical properties, electrical properties, acoustic properties, and radioactivity. It is one of the applied geophysical methods (including gravity, magnetic, electrical, seismic, and nuclear). When drilling for oil, well logging, also known as completion electrical logging, must be carried out after drilling to the designed well depth to obtain various petroleum geological and engineering technical data as the original data for completion and development of oil fields. This type of logging is customarily called open hole logging. The second series of logging carried out after the casing is lowered into the oil well is customarily called production logging or development logging. Its development has generally gone through four stages: analog logging, digital logging, CNC logging, and imaging logging.

[0051] A winch is a light and small lifting equipment that uses a drum to wind a wire rope or chain to lift or pull heavy objects. It is also called a hoist. The winch can be used alone or as a component of machinery such as lifting, road construction, and mine hoisting. It is widely used because of its simple operation, large rope winding capacity, and convenient relocation. It is mainly used for lifting or towing materials in construction, water conservancy projects, forestry, mines, docks, etc. In this embodiment, the winch is a detection device for characterizing well logging. It should be noted that the specific types of well logging and winch are not limited in this embodiment. It can be understood that the well logging information may include but is not limited to depth, temperature, and ambient humidity, etc. In this embodiment, the well logging information and the specific method of obtaining the information are not limited.

[0052] S11: determining a deviation value between the detection data and standard data, wherein the standard data is data of normal operation of the drawworks in the well logging obtained through a preliminary experiment;

[0053] Among them, the generation of standard data includes the following steps: importing data files, the data files include well inclination data, azimuth data, depth data, pre-processing the well inclination data, generating dogleg, displacement, vertical depth, establishing a wellbore trajectory model, obtaining the theoretical tension of the drawworks, dividing the logging into vertical well section, inclined well section, inclination section, large dogleg section, and bottom hole section according to the wellbore trajectory model as well as the azimuth data and depth data, obtaining data on the normal operation of the drawworks in different sections, and using the data as standard data.

[0054] It should be noted that well inclination is a professional term for drilling, also known as well inclination angle, which is usually defined as the angle between the tangent of a point on the wellbore axis and the plumb line. Dogleg degree: the angle at which the wellbore's forward direction changes from one point to another in the wellbore. This angle reflects both the change in well inclination angle and the change in azimuth angle, and is usually called the full-angle change rate or wellbore curvature. The deviation value is obtained by comparing the acquired detection data with the standard data. In this embodiment, the specific method for obtaining the deviation value and the subsequent comparison are not limited. It can be understood that the data recorded when the winch is operating normally all belong to the category of standard data, and the standard data is a fluctuating value, which fluctuates within a range. Therefore, it is necessary to take a number as the standard data in the interval of normal operation of the winch. In this embodiment, the specific method for taking the standard data is not limited. It can be understood that the median or average of the normal operation data of multiple winches is generally taken as the standard data of the winch.

[0055] S12: Determine whether the deviation value meets the preset standard, if not, proceed to S13;

[0056] It can be understood that the preset standard is also the threshold range of the deviation value obtained through preliminary experiments, that is, the fluctuation range of the operating data measured while ensuring the normal operation of the winch. It can be understood that the accuracy of the preset standard can be improved through multiple tests. In this embodiment, the specific method for obtaining the preset standard is not limited.

[0057] S13: Determine whether the winch is abnormal.

[0058] It should be noted that in this embodiment, the specific steps, i.e., the processing methods, after determining that the winch is abnormal are not limited. It can be understood that some abnormalities caused by the operation itself can be adjusted by adjusting the winch by the system, and the abnormalities caused by the logging environment can be prompted by an alarm to prompt the user to perform corresponding abnormal processing.

[0059] In this embodiment, the specific implementation method of the logging system, that is, the information transmission method and the corresponding abnormal confirmation, etc. are not limited. A preferred solution is provided here. In a specific embodiment, for example, when a logging system generates multiple messages within a period of time, the logging system can only display one message at a time (limited by the subsequent implementation of voice prompts), so it is necessary to coordinate multiple messages that exist at the same time. The system characterizes the message with multiple parameters (voice right, set voice right, message purpose, real-time validity, automatic revocation, duration), and compares and judges the parameters to achieve message coordination that meets the safety logic of the logging site, and the only message in the logging system will be immediately displayed on the screen; when multiple messages are generated simultaneously or sequentially, the first message generated will be displayed first, and after that, Messages with higher priorities will be arranged first and replace the current display to display the highest priority message; non-real-time messages that are not displayed after being generated can be cached until a message with a higher voice is displayed and then displayed; when a non-real-time message is cached, its validity will be judged periodically. If the information is invalid, the non-real-time message will be discarded; non-real-time messages that have not been displayed for a sufficient period of time can be re-cached until a message with a higher voice is displayed and then displayed again; overspeed warnings or tension abnormalities require real-time updates of overspeed values ​​or tension values, so the latest message display can be kept all the time; if a certain danger is judged from existing to non-existing, the danger warning can be revoked immediately.

[0060] The abnormality detection method for safety logging provided in the present application detects the detection data of the winch and imports the logging information to determine the deviation value between the detection data and the standard data. The standard data is the data of the normal operation of the winch in the logging obtained through preliminary experiments. According to the deviation value, it is determined whether the winch is abnormal. Compared with the previous method of obtaining data manually, the standard data is obtained through preliminary experiments in the present scheme, and the winch is determined to be abnormal based on the standard data. Therefore, there is no need for manual calculation, and only basic data comparison is required to obtain accurate results of abnormality detection.

[0061] The above embodiments do not limit the specific types of detection data. Here, a preferred solution is provided. The detection data includes speed, tension and depth. According to the deviation value, judging whether the deviation value meets the preset standard includes:

[0062] The deviation between the tension, speed and depth of the winch in the well section and the standard tension, speed and depth is detected in sequence according to the priority, and it is determined whether the deviation meets the preset standard.

[0063] It is easy to understand that the speed, tension and depth in the detection data are the speed of the winch, the tension of the winch rope and the depth of the winch in the logging. No further explanation is given hereinafter. There is no limitation on the acquisition method and deviation value calculation method of different data, namely speed, tension and depth. It can be understood that tension, speed and depth can generally be acquired by sensors.

[0064] In this embodiment, the types of detection data are limited, including the speed, tension and depth of the winch. The speed can reflect the overall operating status of the winch, the tension indicates the bearing status of the winch, and the depth can indicate the environmental information in the well logging. By detecting the above three types of data, the basic operating information of the winch in the well logging can be obtained more completely, and more accurate information can be obtained with fewer calculations. By sorting the priorities of the three types of data, three types of more important information are obtained in turn to facilitate subsequent fault handling.

[0065] In the above embodiment, the detection data of the winch is limited, and the specific steps after determining that the winch is abnormal are not limited. Therefore, a preferred solution is provided here. A preferred solution is proposed here. After determining that the winch is abnormal, it also includes:

[0066] Handle tension, speed, and depth related exceptions in order of priority.

[0067] It should be noted that the specific processing method for abnormalities related to tension, speed and depth is not limited. It is understandable that a delay strategy can be adopted to distinguish between two abnormalities during different abnormality processing. In this embodiment, the priority of abnormality processing is graded by setting priorities. Tension and safety are the top priority, followed by abnormalities related to speed and depth. The processing can be postponed by delaying. For example, depth-related abnormalities may be caused by changes in the logging environment, so the abnormality may disappear automatically after the delay, thereby reducing the processing steps.

[0068] Considering the early warning state of the winch to ensure timely handling of abnormalities, a preferred solution is provided here, which also includes:

[0069] Count the well sections where the frequency of winch abnormality exceeds the threshold, and define the well sections as dangerous well sections;

[0070] When the winch is located in a dangerous well section, a warning command is issued.

[0071] It should be noted that in this embodiment, the target object of the warning instruction can be understood as a terminal operated by the user. In this embodiment, the specific value of the threshold is not limited. For example, the threshold is 3 times a day. If the winch has three abnormal conditions in the inclined well section in one day, the inclined well section is defined as a dangerous well section. Therefore, the status of whether the well section is a dangerous well section is updated over time.

[0072] In this embodiment, by setting the dangerous well section and sending the corresponding warning instructions, timely processing of the winch at the abnormal and frequent moments is ensured, and the safety of well logging is ensured at the same time.

[0073] Considering that the corresponding data information needs to be corrected after the winch is running, a preferred solution is provided here, which also includes:

[0074] At preset intervals, historical data of the normal operation of the winch within the preset time are counted, and the historical data are used as the normal operation data of the winch in well logging to correct the standard data.

[0075] The standard data used as the basis for judgment is corrected through the data of normal operation in the business, thus ensuring the accuracy of the data.

[0076] The above embodiments do not limit the specific processing method after the abnormality, and a preferred solution is provided here, which also includes:

[0077] If an abnormality is detected in the winch, an alarm will be issued.

[0078] In this embodiment, the specific type of device used for alarm processing is not limited. It can be understood that it can include but not limited to warning lights, buzzers, etc., and it can also be an alarm by sending an alarm command to the control terminal. By adding an alarm, timely processing of abnormalities can be guaranteed to ensure the safe operation of the winch.

[0079] In the above embodiments, the anomaly detection method for safety logging is described in detail, and the present application also provides an embodiment corresponding to the anomaly detection device for safety logging. It should be noted that the present application describes the embodiments of the device part from two perspectives, one is based on the perspective of functional modules, and the other is based on the perspective of hardware.

[0080] Figure 2 A schematic diagram of an abnormality detection device for safety well logging provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the device comprises:

[0081] The detection module 10 is used to detect the detection data of the drawworks and import the logging information;

[0082] A determination module 11 is used to determine the deviation value between the detection data and the standard data, where the standard data is the normal operation data of the drawworks in the well logging obtained through a preliminary experiment;

[0083] The judging module 12 is used to judge whether the deviation value meets the preset standard, and if not, activate the abnormal module;

[0084] An abnormality module 13, used to determine if an abnormality occurs in the winch;

[0085] The import module 14 is used to import data files, the data files include well deviation data, azimuth data, and depth data;

[0086] The preprocessing module 15 is used to preprocess the well deviation data, generate the dogleg, displacement, vertical depth, and establish the well trajectory model;

[0087] The calculation module 16 is used to obtain the theoretical tension of the drawworks, and divide the well logging into a vertical well section, a deflected well section, a deflection section, a large dogleg section, and a bottom well section according to the wellbore trajectory model, the azimuth data, and the depth data;

[0088] The acquisition module 17 is used to acquire the data of the normal operation of the winch in different sections and use the data as standard data.

[0089] Since the embodiments of the device part correspond to the embodiments of the method part, the embodiments of the device part and their corresponding beneficial effects can be found in the description of the embodiments of the method part, which will not be repeated here.

[0090] Figure 3 A structural diagram of an abnormality detection device for safety logging provided in another embodiment of the present application is shown in FIG. Figure 3 As shown, the abnormality detection device for safety logging includes: a memory 20 for storing a computer program;

[0091] The processor 21 is used to implement the steps of the abnormality detection method for safety well logging mentioned in the above embodiment when executing the computer program.

[0092] The abnormality detection device for safety logging provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer or a desktop computer.

[0093] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0094] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the anomaly detection method for safety logging disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, the data involved in the above-mentioned anomaly detection method for safety logging, etc.

[0095] In some embodiments, the abnormality detection device for safety logging may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power supply 25 , and a communication bus 26 .

[0096] Those skilled in the art will understand that Figure 3 The structure shown in the figure does not constitute a limitation on the abnormality detection device for safety well logging, and may include more or fewer components than those shown in the figure.

[0097] The anomaly detection device for safety logging provided in the embodiment of the present application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: the anomaly detection method for safety logging involved in the above embodiment.

[0098] Since the embodiments of the device part correspond to the embodiments of the method part, the embodiments of the device part and their corresponding beneficial effects can be found in the description of the embodiments of the method part, which will not be repeated here.

[0099] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps recorded in the above method embodiment are implemented.

[0100] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0101] Since the embodiments of the readable storage medium part correspond to the embodiments of the method part, the embodiments of the device part and their corresponding beneficial effects can be found in the description of the embodiments of the method part, which will not be repeated here.

[0102] The above is a detailed introduction to a method, device and computer-readable storage medium for detecting anomalies in safety logging provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

[0103] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. A method for detecting anomalies in safety logging, characterized in that: include: Detect the winch test data and import the logging information; Determine a deviation value between the detection data and standard data, wherein the standard data is data of normal operation of the drawworks in the well logging obtained through a preliminary experiment; Determining whether the deviation value meets a preset standard; If not, it is determined that the winch is abnormal; The generation of the standard data includes the following steps: Importing a data file, wherein the data file includes well deviation data, azimuth data, and depth data; Preprocessing the well deviation data to generate dogleg, displacement, and vertical depth, and establishing a well trajectory model; Obtaining the theoretical tension of the drawworks, and dividing the well logging into a vertical well section, a deflected well section, a build-up section, a large dogleg section, and a bottom well section according to the wellbore trajectory model, the azimuth data, and the depth data; The data of the normal operation of the winch in different sections are obtained and used as standard data.

2. The anomaly detection method for safety well logging according to claim 1, characterized in that: The detection data includes speed, tension and depth, and judging whether the deviation value meets the preset standard according to the deviation value includes: The deviation values ​​of the tension, speed and depth of the winch in the well section and the standard tension, speed and depth are detected in sequence according to the priority, and it is determined whether the deviation values ​​meet the preset standards.

3. The anomaly detection method for safety logging according to claim 2, characterized in that: After determining that the winch is abnormal, the method further includes: The tension, speed and depth related anomalies are processed in order according to the priority.

4. The anomaly detection method for safety logging according to claim 2, characterized in that: Also includes: Counting the well sections where the frequency of occurrence of the abnormal state of the winch exceeds a threshold, and defining the well sections as dangerous well sections; When the winch is located in the dangerous well section, a warning command is issued.

5. The anomaly detection method for safety logging according to any one of claims 1 to 4, characterized in that: Also includes: At every preset time, historical data of the normal operation of the drawworks within the preset time are counted, and the historical data are used as the normal operation data of the drawworks in the well logging to correct the standard data.

6. The anomaly detection method for safety logging according to claim 5, characterized in that: Also includes: If an abnormality is detected in the winch, an alarm is issued.

7. A well logging safety detection device, characterized in that: include: The detection module is used to detect the detection data of the winch and import the logging information; A determination module, used to determine a deviation value between the detection data and standard data, wherein the standard data is data of normal operation of the drawworks in the well logging obtained through a preliminary experiment; A judgment module, used to judge whether the deviation value meets the preset standard, and if not, activate the abnormal module; An abnormality module, used for determining that an abnormality occurs in the winch; An import module is used to import data files, wherein the data files include well deviation data, azimuth data, and depth data; A preprocessing module, used for preprocessing the well deviation data, generating doglegs, displacements, vertical depths, and establishing a well trajectory model; A calculation module, used for obtaining the theoretical tension of the drawworks, and dividing the well logging into a vertical well section, a deflected well section, a build-up section, a large dogleg section, and a bottom well section according to the wellbore trajectory model, the azimuth data, and the depth data; The acquisition module is used to acquire the data of the normal operation of the winch in different sections and use the data as standard data.

8. A well logging safety detection device, characterized in that: including a memory for storing an acquisition machine program; A processor, configured to implement the steps of the well logging safety detection device as claimed in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the anomaly detection method for safety well logging according to any one of claims 1 to 6 are implemented.

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