Drill bit working state monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a drill bit working state monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring at least two types of drill bit drilling data; inputting the drilling data of each drill bit into a drill bit life monitoring model and a drill bit accident risk prediction model which are obtained through pre-training, and obtaining a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model; and monitoring the working state of the drill bit according to the drill bit life index and the accident risk index. According to the technical scheme, real-time monitoring of the working state of the drill bit, accurate positioning of the remaining life of the drill bit, early recognition of drill bit safety risks and improvement of the use efficiency of the drill bit can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil drilling, and in particular to a method, a device, an electronic device and a storage medium for monitoring the working status of a drill bit. Background Art

[0002] With the development of oil drilling technology, the number of well completions is increasing every year, and the cost of drill bits is increasing. In actual drilling operations, the following two factors are mainly relied on to evaluate the working status of the drill bit: the rated working time calibrated when the drill bit leaves the factory, and the drilling time change trend and torque change during actual drilling. However, in actual drilling operations, there are still some drill bits that have not fully exerted their drill bit efficiency, and the newness after leaving the well is good, resulting in increased drill bit costs, and some drill bits have secondary engineering complexity or accidents due to excessive use. For example, there are drill bit teeth falling off, PDC (Polycrystalline Diamond Compact bit) drill bit blade falling off, and roller bit roller falling off. Dealing with drill bit accidents delays the drilling operation cycle and increases drilling costs. Therefore, there is an urgent need for a method for real-time monitoring of the working status of the drill bit to conduct real-time remote evaluation of the working status of the drill bit. Summary of the invention

[0003] The present invention provides a method, device, electronic device and storage medium for monitoring the working status of a drill bit, so as to realize real-time monitoring of the working status of the drill bit, accurately locate the remaining life of the drill bit, identify the safety risks of the drill bit at an early stage, and improve the use efficiency of the drill bit.

[0004] In a first aspect, an embodiment of the present invention provides a method for monitoring a working state of a drill bit, the method comprising:

[0005] acquiring at least two types of drill bit drilling data;

[0006] Inputting the drilling data of each drill bit into a pre-trained drill bit life monitoring model and a drill bit accident risk prediction model to obtain a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model;

[0007] The working status of the drill bit is monitored according to the drill bit life index and the accident risk index.

[0008] In a second aspect, an embodiment of the present invention further provides a monitoring device for a drill bit working state, the device comprising:

[0009] A drill bit drilling data acquisition module, used to acquire at least two types of drill bit drilling data;

[0010] A model prediction module is used to input the drilling data of each drill bit into a pre-trained drill bit life monitoring model and a drill bit accident risk prediction model to obtain a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model;

[0011] The drill bit working status monitoring module is used to monitor the drill bit working status according to the drill bit life index and the accident risk index.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for monitoring the working status of a drill bit as described in any one of the embodiments of the present invention is implemented.

[0013] In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a method for monitoring the working status of a drill bit as described in any one of the embodiments of the present invention.

[0014] The technical solution of the embodiment of the present invention collects the real-time drilling data of the drill bit, inputs the drilling data of each drill bit into the pre-trained drill bit life monitoring model and the drill bit accident risk prediction model, obtains the drill bit life index output by the drill bit life monitoring model and the accident risk index output by the drill bit accident risk prediction model, and monitors the working status of the drill bit according to the drill bit life index and the accident risk index. It solves the problem in the prior art that the drill bit working status is monitored according to the rated working time of the drill bit when it leaves the factory and the drilling time change trend and torque change during actual drilling, which is prone to the problem that the drill bit does not fully perform its function or causes drill bit accidents due to excessive use of the drill bit, realizes real-time monitoring of the working status of the drill bit, can accurately locate the remaining life of the drill bit, identify the safety risks of the drill bit at an early stage, and improves the use efficiency of the drill bit.

[0015] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1is a flow chart of a method for monitoring the working status of a drill bit provided in Embodiment 1 of the present invention;

[0018] Figure 2 is a flow chart of a method for monitoring the working status of a drill bit provided in Embodiment 2 of the present invention;

[0019] Figure 3 It is a structural schematic diagram of a monitoring device for the working state of a drill bit provided in Embodiment 3 of the present invention;

[0020] Figure 4 It is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Embodiment 1

[0024] Figure 1 A flowchart of a method for monitoring the working status of a drill bit is provided for the first embodiment of the present invention. This embodiment is applicable to the situation where the working status of a drill bit is monitored during drilling operations. The method can be executed by a monitoring device for the working status of a drill bit. The monitoring device for the working status of a drill bit can be implemented in the form of hardware and / or software, and the monitoring device for the working status of a drill bit can be configured in an electronic device.

[0025] like Figure 1 As shown, the method includes:

[0026] S110, acquiring at least two types of drill bit drilling data.

[0027] The drill bit drilling data is the drill bit drilling related data obtained in real time during the drilling operation. The types of drill bit drilling data include at least two of the following: drilling time, drilling pressure, torque, displacement, vertical pressure, drilling fluid density, and drilling fluid viscosity.

[0028] In this embodiment, the specific type of drill bit drilling data can be determined based on the type of training data used in the drill bit life monitoring model and the drill bit accident risk prediction model when training the model. This embodiment does not limit the specific type of drill bit drilling data and the number of different types.

[0029] In this embodiment, real-time drilling data of the drill bit is obtained so as to evaluate the working status of the drill bit in real time and identify the risk of drill bit accidents in time, thereby ensuring drilling safety.

[0030] S120, inputting the drilling data of each drill bit into a pre-trained drill bit life monitoring model and a drill bit accident risk prediction model, and obtaining a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model.

[0031] Among them, the drill bit life monitoring model and the drill bit accident risk prediction model can be obtained by training a pre-set machine learning model. The types of machine learning models may include K-nearest neighbor, Bayesian, logistic regression, decision tree, vector machine, neural network, etc. This embodiment does not limit the type of machine learning model.

[0032] In this embodiment, the machine learning types of the drill bit life monitoring model and the drill bit accident risk prediction model can be the same or different, and the types of training data required for training the drill bit life monitoring model and the drill bit accident risk prediction model can be the same or different. For example, the drilling time, drilling pressure, torque, displacement and rotary speed related data in the historical drill bit drilling data can be used as training data to train the neural network model to obtain the drill bit life monitoring model, and the drilling time, drilling pressure, torque, displacement, vertical pressure, drilling fluid density, and drilling fluid viscosity related data in the historical drill bit drilling data can be used as training data to train the logistic regression model to obtain the drill bit accident risk prediction model.

[0033] In this embodiment, the drill bit life monitoring model obtains the prediction result of the drill bit life according to the input drill bit drilling data, that is, the drill bit life index. The drill bit life index is used to measure the newness and remaining life of the drill bit. It can be set that the higher the drill bit life index, the higher the newness of the drill bit and the longer the remaining life. The drill bit life index can be expressed by percentages, decimals, etc. For example, the drill bit life index output by the drill bit life monitoring model can be 92.45%. The drill bit accident risk prediction model obtains the prediction result of the drill bit accident risk according to the input drill bit drilling data, that is, the accident risk index. The accident risk index is used to measure the probability of a safety accident occurring at the drill bit. It can be set that the higher the accident risk index, the higher the probability of a safety accident occurring at the drill bit and the greater the risk. Similarly, the accident risk index can be expressed by percentages, decimals, etc.

[0034] This embodiment predicts the life of the drill bit and the risk of drill bit accidents through a drill bit life monitoring model and a drill bit accident risk prediction model, respectively, and combines the drill bit life index and the accident risk index to more accurately and comprehensively monitor the working status of the drill bit.

[0035] S130: Monitor the working status of the drill bit according to the drill bit life index and the accident risk index.

[0036] In this embodiment, the real-time monitoring of the working status of the drill bit can be achieved by collecting the drill bit drilling data in real time, and obtaining the drill bit life index and accident risk index in real time through the drill bit life monitoring model and the drill bit accident risk prediction model. Specifically, the fluctuation of the drill bit life index can be used to determine whether the drill bit is in normal drilling condition, and the drill bit parameters such as vertical pressure, torque, and speed can be adjusted. The size of the accident risk index can be used to determine whether there is a risk of a safety accident occurring in the drill bit, and a drill bit accident warning can be issued in a timely manner.

[0037] Furthermore, S130 may include:

[0038] A1. If it is determined that the accident risk index is greater than or equal to a preset risk index threshold, a drill bit accident risk warning is issued.

[0039] Among them, the preset risk index threshold is a preset value. For example, when the higher the accident risk index is set, the higher the probability of a safety accident occurring at the drill bit is, the preset risk index threshold can be set to 20%. When the accident risk index output by the drill bit accident risk prediction model is greater than or equal to 20%, it is considered that the risk of a safety accident occurring at the drill bit is high at this time, and a drill bit accident risk warning is issued. Specifically, a drill bit accident risk warning can be issued in one or more of the following ways: issuing a risk warning on the user interaction interface; sending an on command to the alarm light to make the alarm light flash; sending an on command to the alarm sound to make the alarm sound play a prompt tone; sending a risk warning SMS to a terminal device bound to a monitoring system for the working status of the drill bit, etc. This embodiment does not limit the specific implementation method of the drill bit accident risk warning.

[0040] A2. If it is determined that the accident risk index is less than a preset risk index threshold, the drill start time is determined according to the drill life index and the drill rated data.

[0041] The drill bit start-up time refers to the time when the drill bit is pulled out of the wellhead, and the drill bit rated data may include rated drilling pressure, rated working time, etc. In this embodiment, the drill bit start-up time is determined based on the drill bit life index and the drill bit rated data. Specifically, when the accident risk index is less than the preset risk index threshold, the drill bit life index can be continuously monitored, and the drill bit life index threshold can be pre-set. For example, the drill bit life index threshold can be set to 5%, and the drill bit start-up time is determined based on the rated working time of the drill bit and the drill bit life index. This embodiment does not limit the specific method of determining the drill bit start-up time based on the drill bit life index and the drill bit rated data.

[0042] In this embodiment, the drill bit start-up time is reasonably arranged, which can maximize the efficiency of the drill bit while avoiding drill bit accidents, thereby saving drill bit costs and ensuring drilling speed.

[0043] The technical solution of the embodiment of the present invention collects the real-time drilling data of the drill bit, inputs the drilling data of each drill bit into the pre-trained drill bit life monitoring model and the drill bit accident risk prediction model, obtains the drill bit life index output by the drill bit life monitoring model and the accident risk index output by the drill bit accident risk prediction model, and monitors the working state of the drill bit according to the drill bit life index and the accident risk index. It solves the problem in the prior art that the drill bit working state is monitored according to the rated working time of the drill bit when it leaves the factory and the drilling time change trend and torque change during actual drilling, which is prone to the problem that the drill bit does not fully perform its function or causes drill bit accidents due to excessive use of the drill bit, realizes real-time monitoring of the working state of the drill bit, can accurately locate the remaining life of the drill bit, identify the safety risks of the drill bit at an early stage, and improves the use efficiency of the drill bit.

[0044] Embodiment 2

[0045] Figure 2 A flow chart of a method for monitoring the working status of a drill bit is provided in Embodiment 2 of the present invention. Based on the above embodiments, the embodiment of the present invention further specifies the training process of the drill bit life monitoring model and the drill bit accident risk prediction model, and the process of monitoring the working status of the drill bit according to the drill bit life index and the accident risk index.

[0046] like Figure 2 As shown, the method includes:

[0047] S210, determining historical drill bit drilling data under a drill bit drilling condition and historical drill bit drilling data under a drill bit accident condition.

[0048] Among them, the drill bit drilling condition refers to the condition under which the drill bit performs normal drilling operations, and the drill bit accident condition refers to the condition under which the drill bit has a safety accident, which may include the cone falling off and the broken palm of the roller drill bit, the blade breaking and the tooth falling off of the PDC drill bit, etc.

[0049] In this embodiment, in order to make the trained drill bit life monitoring model and drill bit accident risk prediction model more robust and more accurate, when selecting training data, the historical drill bit drilling data under the drill bit drilling condition and the historical drill bit drilling data under the drill bit accident condition are determined respectively, and as many different strata, different lithologies, different well types, different pressure systems, different drilling fluid systems and different drilling parameters as possible are covered. At the same time, the drill bit accident type is marked for the historical drill bit drilling data under the drill bit accident condition, and the drill bit life is marked for each historical drill bit drilling data. Specifically, for the historical drill bit drilling data under the drill bit accident condition, the corresponding drill bit life can be determined according to the drilling time and the accident time, and for the historical drill bit drilling data under the drill bit drilling condition, the corresponding drill bit life can be obtained according to the drilling time, the drilling time, and the newness and oldness of the drill bit evaluation when drilling.

[0050] Further, after collecting the historical drill bit drilling data under the drill bit drilling condition and the historical drill bit drilling data under the drill bit accident condition, data preprocessing is performed on each historical drill bit drilling data. Specifically, the data preprocessing may include unification of data file formats, unification of data units, data denoising, missing data completion, erroneous data correction, etc. This embodiment does not limit the type and specific method of data preprocessing.

[0051] S220. According to the historical drill bit drilling data under the drill bit drilling condition, the historical drill bit drilling data under the drill bit accident condition, and the historical drill bit life corresponding to each historical drill bit drilling data, a pre-set machine learning model is trained to obtain a drill bit life monitoring model.

[0052] Specifically, each historical drill bit drilling data and its corresponding historical drill bit life can be divided into a training set and a test set. The pre-set machine learning model is trained through the training set, and the model is continuously iterated and fitted and the algorithm parameters are optimized. The machine learning model is tested through the test set. The drill bit life prediction value of the historical drill bit drilling data is compared with the actual historical drill bit life according to the machine learning model to determine the model accuracy. Iterative training is performed until the model accuracy or the number of iterations meets the model training requirements, and a drill bit life monitoring model is obtained.

[0053] Furthermore, S220 can be implemented by the following steps:

[0054] B1. According to the correlation between different types of historical drill bit drilling data and the correlation between each type of historical drill bit drilling data and the historical drill bit life, a first historical drill bit drilling data set is determined from each type of historical drill bit drilling data;

[0055] B2. According to the first historical drill bit drilling data set and the historical drill bit life corresponding to each historical drill bit drilling data in the first historical drill bit drilling data set, a pre-set machine learning model is trained to obtain a drill bit life monitoring model.

[0056] Specifically, for each type of historical drill bit drilling data, such as drilling time, drilling pressure, torque, displacement, vertical pressure, drilling fluid density, drilling fluid viscosity, etc., the skewness, kurtosis, and value range are extracted respectively, and the correlation between different types of historical drill bit drilling data, and the correlation between different types of historical drill bit drilling data and historical drill bit life are determined. The correlation and correlation analysis can be performed using data analysis tools such as SPSS, and this embodiment does not limit this.

[0057] Further, for different types of historical drill bit drilling data with high correlation, feature merging can be performed, and for each type of historical drill bit drilling data after feature merging, a preset number of types of historical drill bit drilling data with high correlation with historical drill bit life are selected, or historical drill bit drilling data of a type with correlation exceeding a preset correlation threshold are selected to determine a first historical drill bit drilling data set. For example, five types of historical drill bit drilling data, namely, drilling time, drilling pressure, rotation speed, displacement, and torque, can be added to the first historical drill bit drilling data set.

[0058] In this embodiment, by analyzing the association and correlation of different types of historical drill bit drilling data, the number of features required for the trained model can be reduced, thereby reducing the magnitude of the drill bit drilling data input into the model and improving the model prediction speed.

[0059] Furthermore, B2 may include:

[0060] B21. Train at least two pre-set machine learning models to obtain at least two candidate drill bit life monitoring models.

[0061] B22. Determine a target drill bit life monitoring model among the candidate drill bit life monitoring models.

[0062] Specifically, the types of machine learning models may include K-nearest neighbor, Bayesian, logistic regression, decision tree, vector machine, neural network, etc. In this embodiment, after determining the first historical drill bit drilling data set for the drill bit life monitoring model, different types of machine learning models are trained according to the first historical drill bit drilling data set, and the final drill bit life monitoring model is selected from the candidate drill bit life monitoring models obtained through training. Specifically, the target drill bit life monitoring model may be selected based on factors such as model accuracy and prediction speed.

[0063] S230. According to the historical drill bit drilling data under the drill bit drilling condition, the historical drill bit drilling data under the drill bit accident condition, and the drill bit accident types corresponding to each historical drill bit drilling data under the drill bit accident condition, a pre-set machine learning model is trained to obtain a drill bit accident risk prediction model.

[0064] Similarly, each historical drill bit drilling data and its corresponding drill bit accident type can be divided into a training set and a test set. The pre-set machine learning model is trained through the training set, and the model is continuously iterated and fitted and the algorithm parameters are optimized. The machine learning model is tested through the test set. The drill bit accident risk prediction value of the historical drill bit drilling data is compared with the drill bit accident type under the actual drill bit accident working condition according to the machine learning model to determine the model accuracy. Iterative training is performed until the model accuracy or the number of iterations meets the model training requirements, and a drill bit accident risk prediction model is obtained.

[0065] Furthermore, S230 may include:

[0066] C1. According to the correlation between different types of historical drill bit drilling data and the correlation between each type of historical drill bit drilling data and the drill bit accident type, a second historical drill bit drilling data set is determined from each type of historical drill bit drilling data.

[0067] C2. According to the second historical drill bit drilling data set and the drill bit accident types corresponding to each historical drill bit drilling data under the drill bit accident conditions in the second historical drill bit drilling data set, a pre-set machine learning model is trained to obtain a drill bit accident risk prediction model.

[0068] Similarly, for each type of historical drill bit drilling data, the skewness, kurtosis, and value range are extracted respectively, and the correlation between different types of historical drill bit drilling data, as well as the correlation between different types of historical drill bit drilling data and historical drill bit life are determined. For different types of historical drill bit drilling data with high correlation, feature merging can be performed, and for each type of historical drill bit drilling data after feature merging, a preset number of types of historical drill bit drilling data with high correlation with drill bit accidents are selected, or historical drill bit drilling data of a type with correlation exceeding a preset correlation threshold is selected to determine a second set of historical drill bit drilling data. The second set of historical drill bit drilling data may be the same as or different from the first set of historical drill bit drilling data.

[0069] Furthermore, C2 may include:

[0070] C21. Train at least two pre-set machine learning models to obtain at least two candidate drill bit accident risk prediction models.

[0071] C22. Determine a target drill bit accident risk prediction model among the candidate drill bit accident risk prediction models.

[0072] Similarly, different types of machine learning models are trained according to the second historical drill bit drilling data set, and the final drill bit accident risk prediction model is selected from the candidate drill bit accident risk prediction models obtained through training. Specifically, the target drill bit accident risk prediction model can be selected based on factors such as model accuracy and prediction speed.

[0073] S240: Acquire drilling data of at least two types of drill bits.

[0074] S250, inputting the drilling data of each drill bit into a pre-trained drill bit life monitoring model and a drill bit accident risk prediction model, and obtaining a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model.

[0075] S260. Determine whether the accident risk index is greater than or equal to a preset risk index threshold. If so, execute S270; otherwise, execute S280.

[0076] S270. Provide drill accident risk warning.

[0077] S280: Determine a start-of-drilling time of the drill bit according to the drill bit life index and the drill bit rated data.

[0078] After training the drill bit life monitoring model and the drill bit accident risk prediction model, the drill bit drilling data is collected in real time, and the drill bit drilling data is respectively input into the drill bit life monitoring model and the drill bit accident risk prediction model to obtain the drill bit life index and the accident risk index, and the drill bit status monitoring method is performed. The above embodiment has been explained and will not be repeated here in this embodiment.

[0079] The technical solution of the embodiment of the present invention, by determining historical drill bit drilling data covering a variety of working conditions, realizes real-time monitoring of the working status of drill bits of different types, different models, and different newness coefficients in different formations, different lithologies, different well types, different pressure systems, different drilling fluid systems, and different drilling parameters, thereby improving the robustness of drill bit detection; according to the correlation between different types of historical drill bit drilling data, and the correlation between different types of historical drill bit drilling data and drill bit life and drill bit accidents, the historical drill bit drilling data for training the drill bit life monitoring model and the historical drill bit drilling data for training the drill bit accident risk prediction model are respectively determined, thereby improving the model accuracy and prediction speed; multiple types of machine learning models are respectively trained according to the training data, and the optimal model is selected as the final drill bit life monitoring model and drill bit accident risk prediction model, thereby further improving the model accuracy. Based on the drill bit life monitoring model and drill bit accident risk prediction model obtained in this way, the drill bit life index and accident risk index are predicted, so as to monitor the working status of the drill bit, accurately locate the remaining life of the drill bit, identify the safety risks of the drill bit at an early stage, and improve the utilization efficiency of the drill bit.

[0080] Embodiment 3

[0081] Figure 3 This is a schematic diagram of the structure of a monitoring device for the working state of a drill bit provided in the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0082] A drill bit drilling data acquisition module 310 is used to acquire at least two types of drill bit drilling data;

[0083] The model prediction module 320 is used to input the drilling data of each drill bit into the pre-trained drill bit life monitoring model and the drill bit accident risk prediction model to obtain the drill bit life index output by the drill bit life monitoring model and the accident risk index output by the drill bit accident risk prediction model;

[0084] The drill bit working state monitoring module 330 is used to monitor the drill bit working state according to the drill bit life index and the accident risk index.

[0085] The technical solution of the embodiment of the present invention collects the real-time drilling data of the drill bit, inputs the drilling data of each drill bit into the pre-trained drill bit life monitoring model and the drill bit accident risk prediction model, obtains the drill bit life index output by the drill bit life monitoring model and the accident risk index output by the drill bit accident risk prediction model, and monitors the working state of the drill bit according to the drill bit life index and the accident risk index. It solves the problem in the prior art that the drill bit working state is monitored according to the rated working time of the drill bit when it leaves the factory and the drilling time change trend and torque change during actual drilling, which is prone to the problem that the drill bit does not fully perform its function or causes drill bit accidents due to excessive use of the drill bit, realizes real-time monitoring of the working state of the drill bit, can accurately locate the remaining life of the drill bit, identify the safety risks of the drill bit at an early stage, and improves the use efficiency of the drill bit.

[0086] Based on the above embodiment, the device further includes:

[0087] A historical drill bit drilling data determination module is used to determine the historical drill bit drilling data under the drill bit drilling condition and the historical drill bit drilling data under the drill bit accident condition;

[0088] A drill bit life monitoring model training module is used to train a pre-set machine learning model based on historical drill bit drilling data under drill bit drilling conditions, historical drill bit drilling data under drill bit accident conditions, and historical drill bit life corresponding to each historical drill bit drilling data to obtain a drill bit life monitoring model;

[0089] The drill bit accident risk prediction model training module is used to train a pre-set machine learning model based on historical drill bit drilling data under drill bit drilling conditions, historical drill bit drilling data under drill bit accident conditions, and drill bit accident types corresponding to each historical drill bit drilling data under drill bit accident conditions to obtain a drill bit accident risk prediction model.

[0090] Based on the above embodiment, the drill bit life monitoring model training module includes:

[0091] A first historical drill bit drilling data set determining unit, configured to determine a first historical drill bit drilling data set from various types of historical drill bit drilling data according to the correlation between different types of historical drill bit drilling data and the correlation between various types of historical drill bit drilling data and historical drill bit life;

[0092] A drill bit life monitoring model training unit is used to train a preset machine learning model according to the first historical drill bit drilling data set and the historical drill bit life corresponding to each historical drill bit drilling data in the first historical drill bit drilling data set to obtain a drill bit life monitoring model;

[0093] The drill bit accident risk prediction model training module includes:

[0094] A second historical drill bit drilling data set determination unit is used to determine a second historical drill bit drilling data set from each type of historical drill bit drilling data according to the correlation between different types of historical drill bit drilling data and the correlation between each type of historical drill bit drilling data and the drill bit accident type;

[0095] The drill bit accident risk prediction model training unit is used to train a pre-set machine learning model according to the second historical drill bit drilling data set and the drill bit accident type corresponding to each historical drill bit drilling data under the drill bit accident working condition in the second historical drill bit drilling data set to obtain a drill bit accident risk prediction model.

[0096] Based on the above embodiment, the drill bit life monitoring model training unit is specifically used for:

[0097] Training at least two pre-set machine learning models to obtain at least two candidate drill bit life monitoring models;

[0098] Among the candidate drill bit life monitoring models, a target drill bit life monitoring model is determined.

[0099] The drill bit accident risk prediction model training unit is specifically used for:

[0100] Training at least two pre-set machine learning models to obtain at least two candidate drill bit accident risk prediction models;

[0101] Among the candidate drill bit accident risk prediction models, a target drill bit accident risk prediction model is determined.

[0102] Based on the above embodiment, the types of the drill bit drilling data include at least two of the following: drilling time, bit pressure, torque, displacement, vertical pressure, drilling fluid density, and drilling fluid viscosity.

[0103] Based on the above embodiment, the drill bit working status monitoring module 330 includes:

[0104] The accident risk warning unit is used to issue a drill bit accident risk warning if it is determined that the accident risk index is greater than or equal to a preset risk index threshold.

[0105] Based on the above embodiment, the drill bit working status monitoring module 330 includes:

[0106] The drill bit start-up time determination unit is used to determine the drill bit start-up time according to the drill bit life index and the drill bit rated data if it is determined that the accident risk index is less than a preset risk index threshold.

[0107] The drill bit working status monitoring device provided in the embodiment of the present invention can execute the drill bit working status monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0108] Embodiment 4

[0109] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for monitoring the working status of a drill bit.

[0113] In some embodiments, the method for monitoring the working state of the drill bit may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for monitoring the working state of the drill bit described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for monitoring the working state of the drill bit in any other appropriate manner (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0116] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the working status of a drill bit, characterized in that: include: acquiring at least two types of drill bit drilling data; Inputting the drilling data of each drill bit into a pre-trained drill bit life monitoring model and a drill bit accident risk prediction model to obtain a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model; The working status of the drill bit is monitored according to the drill bit life index and the accident risk index.

2. The method according to claim 1, characterized in that The training process of the drill bit life monitoring model and the drill bit accident risk prediction model includes: Determine the historical drill bit drilling data under the drill bit drilling condition and the historical drill bit drilling data under the drill bit accident condition; According to the historical drill bit drilling data under the drill bit drilling condition, the historical drill bit drilling data under the drill bit accident condition, and the historical drill bit life corresponding to each historical drill bit drilling data, a pre-set machine learning model is trained to obtain a drill bit life monitoring model; According to the historical drill bit drilling data under the drill bit drilling condition, the historical drill bit drilling data under the drill bit accident condition, and the drill bit accident types corresponding to each historical drill bit drilling data under the drill bit accident condition, the pre-set machine learning model is trained to obtain the drill bit accident risk prediction model.

3. The method according to claim 2, characterized in that According to the historical drill bit drilling data under the drill bit drilling condition, the historical drill bit drilling data under the drill bit accident condition, and the historical drill bit life corresponding to each historical drill bit drilling data, the pre-set machine learning model is trained to obtain the drill bit life monitoring model, including: According to the correlation between different types of historical drill bit drilling data and the correlation between each type of historical drill bit drilling data and the historical drill bit life, a first historical drill bit drilling data set is determined from each type of historical drill bit drilling data; According to the first historical drill bit drilling data set and the historical drill bit life corresponding to each historical drill bit drilling data in the first historical drill bit drilling data set, a pre-set machine learning model is trained to obtain a drill bit life monitoring model; According to the historical drill bit drilling data under the drill bit drilling condition, the historical drill bit drilling data under the drill bit accident condition, and the drill bit accident type corresponding to each historical drill bit drilling data under the drill bit accident condition, the pre-set machine learning model is trained to obtain a drill bit accident risk prediction model, including: According to the correlation between different types of historical drill bit drilling data and the correlation between each type of historical drill bit drilling data and the type of drill bit accident, a second historical drill bit drilling data set is determined from each type of historical drill bit drilling data; According to the second historical drill bit drilling data set and the drill bit accident types corresponding to each historical drill bit drilling data under the drill bit accident conditions in the second historical drill bit drilling data set, the pre-set machine learning model is trained to obtain a drill bit accident risk prediction model.

4. The method according to claim 3, characterized in that The pre-set machine learning model is trained to obtain a drill bit life monitoring model, including: Training at least two pre-set machine learning models to obtain at least two candidate drill bit life monitoring models; Determine a target drill bit life monitoring model among candidate drill bit life monitoring models; The pre-set machine learning model is trained to obtain a drill bit accident risk prediction model, including: Training at least two pre-set machine learning models to obtain at least two candidate drill bit accident risk prediction models; Among the candidate drill bit accident risk prediction models, a target drill bit accident risk prediction model is determined.

5. The method according to claim 1, characterized in that The types of the drill bit drilling data include at least two of the following: drilling time, bit pressure, torque, displacement, vertical pressure, drilling fluid density, and drilling fluid viscosity.

6. The method according to claim 1, characterized in that Monitoring the working state of the drill bit is performed according to the drill bit life index and the accident risk index, including: If it is determined that the accident risk index is greater than or equal to a preset risk index threshold, a drill bit accident risk warning is issued.

7. The method according to claim 6, characterized in that Monitoring the working state of the drill bit according to the drill bit life index and the accident risk index also includes: If it is determined that the accident risk index is less than a preset risk index threshold, the drill start time is determined according to the drill life index and the drill rated data.

8. A monitoring device for the working status of a drill bit, characterized in that: include: A drill bit drilling data acquisition module, used to acquire at least two types of drill bit drilling data; A model prediction module is used to input the drilling data of each drill bit into a pre-trained drill bit life monitoring model and a drill bit accident risk prediction model to obtain a drill bit life index output by the drill bit life monitoring model and an accident risk index output by the drill bit accident risk prediction model; The drill bit working status monitoring module is used to monitor the drill bit working status according to the drill bit life index and the accident risk index.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for monitoring the working status of the drill bit as described in any one of claims 1-7 is implemented.

10. A storage medium storing computer executable instructions, characterized in that: The computer executable instructions are used to execute the method for monitoring the working status of a drill bit as described in any one of claims 1 to 7 when executed by a computer processor.

Images