Method, apparatus and system for lifecycle management of drilling rig device, and terminal device
The equipment status prediction system trained by support vector machine model and optimization algorithm solves the problems of fault diagnosis and spare parts management in oil drilling equipment management, and improves the accuracy of equipment status prediction and management efficiency.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2025-12-22
- Publication Date
- 2026-07-23
AI Technical Summary
Oil drilling equipment is diverse and complex in design, making fault diagnosis and spare parts management difficult. Manual records are often untimely and inaccurate, resulting in high maintenance costs and difficulty in predicting equipment status.
The equipment status prediction is trained using a support vector machine model. By acquiring the current and historical operating parameters of the drilling rig, the equipment status is displayed and alarm signals are generated. The model hyperparameters are optimized by combining improved particle swarm optimization and gray wolf algorithms.
It improved the efficiency of drilling equipment management, enabled accurate prediction and timely alarm of equipment status, and reduced labor costs and maintenance difficulty.
Smart Images
Figure CN2025144372_23072026_PF_FP_ABST
Abstract
Description
Drilling rig equipment life cycle management method, device, system and terminal equipment
[0001] Cross-reference to Related Applications
[0002] This application claims the benefit of Chinese Patent Application No. 202510064427.6, filed January 15, 2025, the contents of which are incorporated by reference herein. TECHNICAL FIELD
[0003] The present application relates to the technical field of drilling rig equipment management, in particular to a drilling rig equipment life cycle management method, a drilling rig equipment life cycle management device, a drilling rig equipment life cycle management system, a computer readable storage medium and a terminal equipment. BACKGROUND
[0004] With the development of oil drilling technology, drilling operations have made great progress in automation and intelligentization. Various automated equipment and automatic operation methods have greatly improved the efficiency of drilling operations. However, as the types of equipment used become more and more complex, how to achieve good management and efficient maintenance of the equipment has become a new problem. At present, in other industries, especially in the warehousing and logistics industry, the main method to solve this problem is to establish a data management device life cycle management system to judge the state of the equipment and diagnose the health of the equipment based on analysis of historical data. In particular, the establishment of a full life cycle can fully reflect all states of the equipment from use to retirement, and provide comprehensive data support for equipment updating and upgrading, and provide a reference basis for purchasing suppliers. However, due to the large number of types of oil drilling equipment and the complex design, it is difficult to determine the cause of the failure and find the fault point if a fault occurs. In addition, the management of spare parts is also a great difficulty, which brings great challenges to the maintenance of drilling equipment. Taking the drawworks and disc brake system in the drilling equipment as an example, the large rope suspension weight, the large rope use time, the system running time, and the disc brake pad wear degree are all important factors affecting whether the system is running in good working condition. Therefore, during drilling operations, additional attention needs to be paid to the above equipment parameters. If the above reference parameters are completely measured and recorded by manual measurement, there will be many problems such as data recording not being timely, not being accurate, not being comprehensive, and labor cost being too high, and it is difficult for manual measurement to accurately predict the state of the equipment. SUMMARY
[0005] The purpose of the embodiments of the present application is to provide a drilling rig equipment life cycle management method, a drilling rig equipment life cycle management device, a drilling rig equipment life cycle management system, a computer readable storage medium and a terminal equipment to solve the above problems.
[0006] To achieve the above purpose, the first aspect of the present application provides a drilling rig equipment life cycle management method, comprising:
[0007] In response to a device management operation instruction of a target drilling rig device by a user, current operation parameters of the target drilling rig device and device parameters of the target drilling rig device are acquired.
[0008] The current operation parameters of the target drilling rig device are displayed in a first display area, and the device parameters of the target drilling rig device are displayed in a second display area.
[0009] The historical operation parameters of the target drilling rig device are acquired, the historical operation parameters of the target drilling rig device are input, a preset device state prediction model is called to output a predicted device state of the target drilling rig device, the predicted device state of the target drilling rig device is displayed in a third display area, and an alarm signal is generated when the predicted device state of the target drilling rig device is abnormal, wherein the device state prediction model is obtained by training an improved support vector machine model by the historical operation parameters of the target drilling rig device in different device states.
[0010] The second aspect of the present application provides a drilling rig device life cycle management device, comprising:
[0011] A data acquisition module is configured to acquire current operation parameters of a target drilling rig device and device parameters of the target drilling rig device in response to a device management operation instruction of the target drilling rig device by a user.
[0012] A parameter display module is configured to display the current operation parameters of the target drilling rig device in a first display area and display the device parameters of the target drilling rig device in a second display area.
[0013] A device state prediction module is configured to acquire historical operation parameters of the target drilling rig device, input the historical operation parameters of the target drilling rig device, call a preset device state prediction model to output a predicted device state of the target drilling rig device, display the predicted device state of the target drilling rig device in a third display area, and generate an alarm signal when the predicted device state of the target drilling rig device is abnormal, wherein the device state prediction model is obtained by training an improved support vector machine model by the historical operation parameters of the target drilling rig device in different device states.
[0014] The third aspect of the present application provides a drilling rig device life cycle management system, comprising:
[0015] The drilling rig device life cycle management device as described above; and
[0016] A drilling rig device parameter acquisition device is configured to acquire operation parameters and device parameters of a target drilling rig device.
[0017] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program that, when executed by a processor, causes the processor to perform the rig equipment life cycle management method as described above.
[0018] The fifth aspect of the present application provides a terminal device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the rig equipment life cycle management method as described above when executing the computer program.
[0019] The embodiments provided by the present application have the following beneficial effects:
[0020] The present application effectively improves the management efficiency of the rig equipment, and can effectively predict the equipment state of the rig equipment.
[0021] Other features and advantages of the embodiments or the implementation of the present application will be described in detail in the following specific implementation part. DETAILED DESCRIPTION
[0022] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific implementation part to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:
[0023] FIG. 1 schematically shows a method flowchart of the rig equipment life cycle management method of the embodiments of the present application;
[0024] FIG. 2 schematically shows a winch management interface schematic diagram of the embodiments of the present application;
[0025] FIG. 3 schematically shows a schematic block diagram of the rig equipment life cycle management device of the embodiments of the present application;
[0026] FIG. 4 schematically shows a system network architecture schematic diagram of the embodiments of the present application;
[0027] FIG. 5 schematically shows a generator health management interface schematic diagram of the embodiments of the present application;
[0028] FIG. 6 schematically shows a device communication management interface schematic diagram of the embodiments of the present application;
[0029] FIG. 7 schematically shows a historical project management interface schematic diagram of the embodiments of the present application;
[0030] FIG. 8 schematically shows a parameter management interface schematic diagram of the embodiments of the present application;
[0031] FIG. 9 schematically shows a terminal device structure schematic diagram of the embodiments of the present application. DETAILED DESCRIPTION
[0032] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the present application, and are not intended to limit the present application.
[0033] To solve the above problems, as shown in FIG. 1, the first aspect of the present application provides a drilling rig equipment life cycle management method, comprising:
[0034] S100, in response to a user's equipment management operation instruction on a target drilling rig equipment, obtaining the current running parameters of the target drilling rig equipment and the equipment parameters of the target drilling rig equipment;
[0035] S200, displaying the current running parameters of the target drilling rig equipment in a first display area, and displaying the equipment parameters of the target drilling rig equipment in a second display area;
[0036] S300, obtaining the historical running parameters of the target drilling rig equipment, taking the historical running parameters of the target drilling rig equipment as input, calling a preset equipment state prediction model to output the predicted equipment state of the target drilling rig equipment, and displaying the predicted equipment state of the target drilling rig equipment in a third display area, and generating an alarm signal when the predicted equipment state of the target drilling rig equipment is abnormal, wherein the equipment state prediction model is obtained by training an improved support vector machine model with the historical running parameters of the target drilling rig equipment under different equipment states.
[0037] In this way, the management efficiency of the drilling rig equipment is effectively improved, and the equipment state of the drilling rig equipment can be effectively predicted.
[0038] In step S100, after receiving the user's equipment management operation instruction on the target drilling rig equipment, the related parameters of the target drilling rig equipment are first collected. Taking the drawworks and disc brake equipment as an example, as shown in FIG. 2, the current running parameters of the drawworks and disc brake equipment include drawworks cumulative running time, large rope ton-kilometers, disc brake running time, disc brake system pressure, disc brake left work clamp pressure, disc brake right work clamp pressure, air supply pressure, drawworks oil pressure, lubricating oil pressure, hook load, motor current and drawworks speed, etc. The equipment parameters of the drawworks and disc brake equipment include drawworks parameters, disc brake parameters, motor parameters, rope system parameters, oil pump parameters, etc. Among them, the current running parameters and equipment parameters of the target drilling rig equipment can be obtained directly through the existing driller system.
[0039] In step S200, after obtaining the operation parameters and device parameters of the target drilling rig equipment, the operation parameters of the target drilling rig equipment, such as the cumulative running time of the drawworks, the ton-kilometer of the rope, the running time of the disc brake, the disc brake system pressure, the disc brake left jaw pressure, the disc brake right jaw pressure, the air source pressure, the drawworks oil pressure, the lubricating oil pressure, the hook load, the motor current and the drawworks speed, are displayed in the first display area on the left side of the display interface; and the device parameters of the target drilling rig equipment, such as the drawworks parameters, the disc brake parameters, the motor parameters, the rope system parameters and the oil pump parameters, are displayed in the second display area on the right side of the display interface.
[0040] In step S300, after receiving the device management operation instruction of the target drilling rig equipment, the historical operation parameters of the target drilling rig equipment are obtained, the pre-trained device state prediction model is called to predict the device state of the current target drilling rig equipment, and the device state prediction result of the current target drilling rig equipment is displayed in the third display area of the display interface, for example, the third display area can be located below the first / second display area. The device state of the target drilling rig equipment at least includes a first device state and a second device state, the first device state is used to represent that the target drilling rig equipment is in a normal running state, and the second device state is used to represent that the target drilling rig equipment is in an abnormal running state. The first device state includes but is not limited to excellent, good, general, etc., and the second device state includes but is not limited to poor, relatively poor, fault, etc. When the predicted device state of the target drilling rig equipment is abnormal, an alarm signal is generated, including: when the predicted device state of the target drilling rig equipment is abnormal, the predicted device state of the target drilling rig equipment is marked as a specified color in the third display area, and / or an alarm signal of the predicted device state of the target drilling rig equipment is sent to a specified terminal device. For example, when the predicted device state of the target drilling rig equipment is poor, relatively poor, etc., the state of the corresponding device is marked yellow in the third display area, or the alarm information of the device state of the corresponding device is sent to the corresponding management personnel terminal to prompt the management personnel that the device state is abnormal.
[0041] In step S300, the device state prediction model can be obtained based on the support vector machine algorithm after training. The training process of the device state prediction model of the present application includes:
[0042] In S310, the historical operation parameters of the target drilling rig equipment in the normal running state and the historical operation parameters of the target drilling rig equipment in different abnormal running states are obtained, and a training sample set is constructed based on the historical operation parameters of the target drilling rig equipment; for example, taking the drawworks and the disc brake system as an example, the historical data of the disc brake system pressure under normal working conditions and the historical data of the disc brake system pressure under disc brake system failure, aging and other working conditions are obtained to construct the training sample set.
[0043] S320, training the support vector machine model with the historical running parameters of the target drilling rig equipment as input and the corresponding equipment state of the target drilling rig equipment as output, and optimizing the hyperparameters of the support vector machine model through the improved parameter optimization algorithm during the training process to determine the optimal hyperparameters of the support vector machine model. For example, when training the support vector machine algorithm, the historical data of the disc brake system pressure of the drawworks and disc brake system under normal working conditions and the historical data of the disc brake system pressure of the drawworks and disc brake system under disc brake system failure, aging and other working conditions are taken as input, the equipment state of the disc brake system is taken as output, the support vector machine algorithm is trained, and the optimal hyperparameters of the support vector machine algorithm are determined through the improved parameter optimization algorithm to optimize the hyperparameters of the support vector machine algorithm, thereby improving the prediction accuracy of the model.
[0044] Specifically, in step S320, the hyperparameters of the support vector machine model include a penalty parameter, i.e., a C parameter, and a kernel parameter of a kernel function, i.e., a Gamma parameter. Among them, the C parameter is also called a regularization parameter in SVM, which is used to control the complexity of the model. The larger the C value, the higher the complexity of the model, and the stronger the fitting ability of the training data. However, if the C value is too large, the model may be too complex, leading to overfitting, i.e., good performance on training data but poor performance on test data. The Gamma parameter is a hyperparameter in SVM that controls the influence range of the kernel function. It determines the influence of sample points on the model, i.e., the data closer to the sample point has a larger weight in the model. For the case of using Gaussian radial basis function (RBF) or polynomial kernel function, the selection of Gamma parameter is particularly important. For RBF kernel function, a smaller Gamma value indicates a larger influence range, which may lead to a smoother decision boundary; while a larger Gamma value will make the model pay more attention to the local area of each training sample, which may lead to a more complex and detailed decision boundary. For polynomial kernel function, the Gamma parameter determines the similarity of features in the feature space.
[0045] It can be understood that the main idea of SVM is to establish an optimal decision hyperplane to maximize the distance between the two classes of samples closest to the hyperplane on both sides of the hyperplane, thereby providing good generalization ability for classification problems. Specifically, the objective function of the SVM model can be represented as:
[0046] where a i , a j are Lagrange multipliers corresponding to the training sample data x i , x j , and the support vectors can be determined by solving a i , a j , y i and y j represent the sample xi x j Category tags, K(x) i x j ) represents the kernel function, used to process sample x i x j Mapping to a higher-dimensional feature space, thereby achieving linear separability in that space, x i x j For the input feature vector, i.e., the sample data points, in SVM, all data points x i and x j Used to construct the optimal decision hyperplane, where m represents the number of samples and C represents the penalty parameter.
[0047] In this application, the kernel function is a radial basis kernel function, which can be expressed as:
[0048] Where σ is the kernel parameter.
[0049] In this application, the hyperparameters of the support vector machine model are optimized using an improved parameter optimization algorithm, including:
[0050] S321. Construct a fitness function based on the error between the predicted and actual values of the support vector machine model. For example, construct the fitness function with the highest accuracy of the model's prediction results. The fitness function can be expressed as:
[0051] Where m represents the total number of equipment status categories. For example, if the equipment status categories include good, fair, and poor, then m is 3, and p j Let q be the sample size. j The number of samples with incorrect predictions.
[0052] S322. The penalty parameters and kernel parameters of the support vector machine model are mapped to particle positions in the particle swarm optimization algorithm. Based on the fitness function, the penalty function and kernel function of the support vector machine are optimized using an improved particle swarm optimization algorithm. In this application, the penalty function and kernel function of the support vector machine are optimized using an improved particle swarm optimization algorithm. Specifically, the improved particle swarm optimization algorithm includes:
[0053] S10. Initialize the particle swarm, determine the number of particles in the swarm and the initial position of each particle. The position of each particle corresponds to a solution, that is, the values of the penalty parameters and kernel parameters of the support vector machine model.
[0054] S20. Determine the fitness value of each particle based on the fitness function, and determine the individual extreme value and the population extreme value of each particle.
[0055] S30. Based on the individual and group extreme values of each particle, if the current particle meets the preset first update condition, update the velocity and position of each particle using the preset velocity update function and position update function, and proceed to step S40. Otherwise, determine the three particles with the best fitness values as α wolf, β wolf, and δ wolf, and use the current particle as the current gray wolf. Determine the position of the current gray wolf using the gray wolf algorithm, update the position of the current particle using the position of the current gray wolf, and proceed to step S40. The first update condition includes: either random number r1 or r2 is less than a preset random number threshold, for example, the random number threshold can be set to 0.5.
[0056] S40. Determine the individual extreme value and the population extreme value of each particle after the update based on the fitness function. If the convergence condition is not met, return to step S30. If the convergence condition is met, stop the search and output the current population extreme value as the optimal penalty function and optimal kernel function of the support vector machine.
[0057] The velocity update function includes: v i (t+1)=w * v i (t)+c 1* r 1* (p ib -x i (t))+c 2* r 2* (g ib -x i (t))
[0058] Position update functions include: x i (t+1)=x i (t)+v i (t)
[0059] Among them, v i (t+1) represents the velocity of particle i at time t+1, v i (t) represents the velocity of particle i at time t, w is the inertial weight, c1 and c2 are learning factors, and p ib Let g be the individual extreme value of particle i. ib Let r1 and r2 be the population extrema of the entire particle swarm, and x be random numbers. i (t+1) represents the position of particle i at time t+1, x i (t) represents the position of particle i at time t;
[0060] The method of this application further includes: updating the inertia weight through a preset weight update function, wherein the weight update function includes: w = w base *w factor
[0061] Among them, wbase Based on inertia weights, w factor This is the inertia weight adjustment factor.
[0062] Optionally, the formula for calculating the basic inertia weight includes: w base =w max -(w max -w min )*(iter / iter max ) w factor =(rank / S) p
[0063] Among them, w max w represents the maximum inertia weight. min It represents the minimum inertia weight, and iter represents the current iteration number. max This represents the maximum number of iterations, rank represents the ranking of the current particle's fitness value among all particles' fitness values, S represents the number of particles in the particle swarm, and p is a positive real number that can be set according to requirements.
[0064] In updating the inertia weight, this application first uses a linearly decreasing weight to control the overall trend of the inertia weight change. Then, it combines a power-law distribution function to adjust the specific weight of the particles. In each iteration, the weight of each particle is calculated according to the power-law distribution function and used together with the current velocity and acceleration to update the particle's position. This allows for the use of a larger inertia weight for global search in the early stages of the search, followed by a gradual reduction of the inertia weight and adjustment of the specific weight of each particle according to the power-law distribution function to increase the ability of local search. This balances the effects of global search and local search, thereby improving the performance of the particle swarm optimization algorithm.
[0065] In this application, the Grey Wolf algorithm includes:
[0066] S31. Initialize the gray wolf population, using the number of particles in the particle swarm as the population size of the gray wolf population, and initialize the position of the gray wolves and the position update parameters. The position update parameters include the distance control parameter a, the coefficient vector A, and the coefficient vector C.
[0067] S32. Update the distance control parameters a, coefficient vector A, and coefficient vector C for α wolf, β wolf, and δ wolf using the following formula: A i =2ar1-a, i=1,2,3 C i =2r2,i=1,2,3 a(t)=2-2iter / iter max
[0068] S33. Based on the updated distance control parameter a, coefficient vector A, and coefficient vector C, update the gray wolf's position using the following formula: X1 = X α+A1·|C1·X α -X| X2=X β +A2·|C2·X β -X| X3=X δ +A3·|C3·X δ -X|
[0069] Where X is the current position of the gray wolf, Xα, Xβ, and Xδ represent the positions of α wolf, β wolf, and δ wolf, respectively, X1, X2, and X3 represent the positions of the current gray wolf inferred from the positions of α wolf, β wolf, and δ wolf, respectively, and X(t+1) represents the updated position of the current gray wolf;
[0070] S34. If the second update condition is met, update the current particle's position with the current position of the gray wolf; otherwise, return to step S32. The second update condition includes: the current iteration count reaches a preset iteration count threshold.
[0071] As shown in Figure 3, a second aspect of this application provides a drilling rig equipment lifecycle management device, comprising: a data acquisition module configured to acquire the current operating parameters and equipment parameters of the target drilling rig equipment in response to a user's equipment management operation command for the target drilling rig equipment; a parameter display module configured to display the current operating parameters of the target drilling rig equipment in a first display area and the equipment parameters of the target drilling rig equipment in a second display area; and an equipment status prediction module configured to acquire the historical operating parameters of the target drilling rig equipment, use the historical operating parameters of the target drilling rig equipment as input, call a preset equipment status prediction model to output the predicted equipment status of the target drilling rig equipment, display the predicted equipment status of the target drilling rig equipment in a third display area, and generate an alarm signal when the predicted equipment status of the target drilling rig equipment is abnormal, wherein the equipment status prediction model is obtained by training an improved support vector machine model with the historical operating parameters of the target drilling rig equipment under different equipment states.
[0072] It is understood that those skilled in the art will clearly recognize that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0073] A third aspect of this application provides a drilling rig equipment lifecycle management system, including: a drilling rig equipment lifecycle management device as described above; and a drilling rig equipment parameter acquisition device, wherein the device is used to acquire the operating parameters and equipment parameters of the target drilling rig equipment.
[0074] Specifically, in this application, the drilling rig equipment lifecycle management system relies on equipment parameters provided by systems such as the driller's control system. When the number of parameters reaches a certain level and the sample size is sufficient, relevant algorithms predict the equipment status, providing a reference for equipment maintenance plans. The system network architecture of this application is shown in Figure 4. Each device and control system communicates with each other via a switch and network cable. During data exchange, the data server automatically saves key equipment parameters, while the file server primarily provides services such as user interaction, operation and maintenance management, and model calculation. In this application, equipment parameters can be directly obtained through the existing driller's control system; therefore, the equipment parameter acquisition device in this application can be the corresponding data interface. Alternatively, equipment parameters can also be directly obtained through sensors installed on the corresponding equipment; therefore, the equipment parameter acquisition device can be the corresponding sensor.
[0075] In this application, the lifecycle management system is mainly divided into a driller's prompting subsystem, a data interaction management subsystem, and an engineer management subsystem. Among them, the driller's prompting subsystem only needs to briefly indicate the status of the driller's equipment, so it can be directly integrated into the existing driller's control system.
[0076] The driller's prompting subsystem can be accessed in the following ways: After the driller's cabin is powered on, first enter the main interface of the driller's control system, and then click the "Settings" button in the navigation bar to enter the settings interface; click "Equipment Health Management" in the lower right corner of the settings interface to enter the driller's prompting subsystem of the equipment health management system; taking the generator set as an example, click the "Generator Set" image, and the page will jump to the generator set information prompt interface as shown in Figure 5, where the health status of the equipment can be observed more intuitively.
[0077] In the data interaction management subsystem, the data acquisition service of this application can be implemented using KEPServerEX software. This software can run on an industrial control computer to collect data from PLCs and related equipment in real time. After collecting data from the target equipment, it can be shared via the OPC protocol. In addition, this application can also realize data exchange between PLCs and between PLCs and other devices in the network through wired protocols such as Profinet, Modbus, and Profibus. Among them, the remote engineer station needs to connect the remote device to the local device via the Internet. An industrial router with SIM card Internet access or WIFI Internet access is installed in the local network. After encryption, the data is uploaded to the Internet. The remote engineer can access the router through the Internet and access the data in the local network after establishing a connection.
[0078] In this application, the engineer management subsystem is the core module of drilling rig equipment lifecycle management. After entering the engineer management subsystem access interface, users can view the current communication status and network quality of the equipment by clicking the "Communication Management" interface in the system's bottom navigation bar, as shown in Figure 6. In addition, clicking the blank area below the project list allows users to view the equipment's historical communication status list. By clicking "Project Management" in the system navigation bar, as shown in Figure 7, users can view the operation status of this drilling equipment in previous well sites and the historical operation data of the corresponding equipment. Among them, "Parameter Management" and "Alarm Management" allow users to view the historical data and status of the equipment in the current project, as shown in Figure 8. Clicking "Equipment Management Interface" allows users to manage all equipment in the current project. Clicking the screen of the corresponding equipment will directly take users to the detailed management interface of that equipment, as shown in Figure 2. Taking the winch system as an example, the middle part shows the current operating data of the equipment, the right part shows the initial parameters and fixed parameters of the equipment and its components, and the lower left corner shows the current status of the equipment estimated based on the equipment's historical status.
[0079] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the drilling equipment lifecycle management method described above.
[0080] The fifth aspect of this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described drilling equipment lifecycle management method.
[0081] Figure 9 is a schematic diagram of a terminal device provided in an embodiment of this application. As shown in Figure 9, the terminal device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the above method embodiments. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the above device embodiments.
[0082] For example, computer program 102 may be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 102 in terminal device 10.
[0083] Terminal device 10 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will understand that FIG9 is merely an example of terminal device 10 and does not constitute a limitation on terminal device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device may also include input / output devices, network access devices, buses, etc.
[0084] The processor 100 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.
[0085] The memory 101 can be an internal storage unit of the terminal device 10, such as a hard disk or RAM of the terminal device 10. The memory 101 can also be an external storage device of the terminal device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal device 10. Furthermore, the memory 101 can include both internal and external storage units of the terminal device 10. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.
[0086] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0087] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0088] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for lifecycle management of drilling equipment, characterized in that, include: In response to the user's equipment management operation command for the target drilling rig, the current operating parameters and equipment parameters of the target drilling rig are obtained; The current operating parameters of the target drilling rig are displayed in the first display area, and the equipment parameters of the target drilling rig are displayed in the second display area. as well as The historical operating parameters of the target drilling rig are obtained. Using the historical operating parameters of the target drilling rig as input, a preset equipment status prediction model is called to output the predicted equipment status of the target drilling rig. The predicted equipment status of the target drilling rig is displayed in a third display area. When the predicted equipment status of the target drilling rig is abnormal, an alarm signal is generated. The equipment status prediction model is obtained by training an improved support vector machine model with the historical operating parameters of the target drilling rig under different equipment statuses.
2. The drilling rig equipment lifecycle management method according to claim 1, characterized in that, The target drilling rig equipment includes a winch and disc brake system, and the current operating parameters of the target drilling rig equipment include: The winch and disc brake equipment's cumulative running time, main rope ton-kilometers, disc brake running time, disc brake system pressure, left working clamp pressure, right working clamp pressure, air source pressure, winch oil pressure, lubricating oil pressure, hook suspension weight, motor current, and winch speed. The equipment parameters of the target drilling rig include: Winch parameters, disc brake parameters, motor parameters, rope system parameters, and oil pump parameters.
3. The drilling rig equipment lifecycle management method according to claim 1, characterized in that, The equipment status of the target drilling rig equipment includes at least the following: First device status and second device status; The first equipment status is used to indicate that the target drilling rig is in normal operating condition, and the second equipment status is used to indicate that the target drilling rig is in abnormal operating condition; When the predicted equipment status of the target drilling rig is abnormal, an alarm signal is generated, including: When the predicted equipment status of the target drilling rig is abnormal, the predicted equipment status of the target drilling rig is marked with a specified color in the third display area, and / or an alarm signal indicating that the predicted equipment status of the target drilling rig is abnormal is sent to a specified terminal device.
4. The drilling rig equipment lifecycle management method according to claim 1, characterized in that, The training process of the equipment status prediction model includes: The historical operating parameters of the target drilling rig under normal operating conditions and the historical operating parameters of the target drilling rig under different abnormal operating conditions are obtained, and a training sample set is constructed based on the historical operating parameters of the target drilling rig. The support vector machine model is trained using the historical operating parameters of the target drilling rig as input and the corresponding equipment status as output. During the training process, the hyperparameters of the support vector machine model are optimized using an improved parameter optimization algorithm to determine the optimal hyperparameters of the support vector machine model.
5. The drilling rig equipment lifecycle management method according to claim 4, characterized in that, The hyperparameters of the support vector machine model include penalty parameters and kernel parameters of the kernel function. The hyperparameters of the support vector machine model are optimized using an improved parameter optimization algorithm, including: A fitness function is constructed based on the error between the predicted and actual values of the support vector machine model. The penalty parameters and kernel parameters of the support vector machine model are mapped to particle positions in the particle swarm optimization algorithm. Based on the fitness function, the penalty function and kernel function of the support vector machine are optimized by an improved particle swarm optimization algorithm.
6. The drilling rig equipment lifecycle management method according to claim 5, characterized in that, The improved particle swarm optimization algorithm includes: S10. Initialize the particle swarm, determine the number of particles in the swarm and the initial position of each particle. S20. Determine the fitness value of each particle based on the fitness function, and determine the individual extreme value and the population extreme value of each particle; S30. Based on the individual extreme value and the group extreme value of each particle, if the current particle meets the preset first update condition, update the velocity and position of each particle through the preset velocity update function and position update function, and execute step S40; otherwise, determine the top three particles with the best fitness values as α wolf, β wolf and δ wolf, and take the current particle as the current gray wolf, determine the position of the current gray wolf through the gray wolf algorithm, update the position of the current particle with the position of the current gray wolf, and execute step S40. S40. Based on the fitness function, determine the individual extreme value and the population extreme value of each particle after the update. If the convergence condition is not met, return to step S30. If the convergence condition is met, stop the search and output the current population extreme value as the optimal penalty function and optimal kernel function of the support vector machine.
7. The drilling rig equipment lifecycle management method according to claim 6, characterized in that, The speed update function includes: v i (t+1)=w * v i (t)+c 1* r 1* (p ib -x i (t))+c 2* r 2* (g ib -x i (t)) The location update function includes: x i (t+1)=x i (t)+v i (t) Among them, v i (t+1) represents the velocity of particle i at time t+1, v i (t) represents the velocity of particle i at time t, w is the inertial weight, c1 and c2 are learning factors, and p ib Let g be the individual extreme value of particle i. ib Let r1 and r2 be the population extrema of the entire particle swarm, and x be random numbers. i (t+1) represents the position of particle i at time t+1, x i (t) represents the position of particle i at time t; The method further includes: The inertia weight is updated by a preset weight update function, wherein the weight update function includes: w=w base *In factor Among them, w base Based on inertia weights, w factor This is the inertia weight adjustment factor.
8. The drilling rig equipment lifecycle management method according to claim 7, characterized in that, The formula for calculating the basic inertia weight includes: w base =w max -(w max -w min )*(iter / iter max ) w factor =(rank / S) p Among them, w max w represents the maximum inertia weight. min It represents the minimum inertia weight, and iter represents the current iteration number. max represents the maximum number of iterations, rank represents the ranking of the current particle's fitness value among all particles' fitness values, S represents the number of particles in the swarm, and p is a positive real number.
9. The drilling rig equipment lifecycle management method according to claim 7, characterized in that, The first update condition includes: Either random number r1 or r2 is less than a preset random number threshold.
10. The drilling rig equipment lifecycle management method according to claim 8, characterized in that, The Grey Wolf algorithm includes: S31. Initialize the gray wolf population, using the number of particles in the particle swarm as the population size of the gray wolf population, and initialize the position of the gray wolves and the position update parameters. The position update parameters include the distance control parameter a, the coefficient vector A, and the coefficient vector C. S32. Update the distance control parameters a, coefficient vector A, and coefficient vector C for α wolf, β wolf, and δ wolf using the following formulas: A i =2ar1-a,i=1,2,3 C i =2ar2,i=1,2,3 a(t)=2-2iter / iter max S33. Based on the updated distance control parameter a, coefficient vector A, and coefficient vector C, update the gray wolf's position using the following formula: X1=X α +A1·|C1·X α -X| X2=X β +A2·|C2·X β -X| X3=X δ +A3·|C3·X δ -X| Where X is the current position of the gray wolf, Xα, Xβ, and Xδ represent the positions of α wolf, β wolf, and δ wolf, respectively, X1, X2, and X3 represent the positions of the current gray wolf inferred from the positions of α wolf, β wolf, and δ wolf, respectively, and X(t+1) represents the updated position of the current gray wolf; S34. If the second update condition is met, update the position of the current particle with the current position of the gray wolf; otherwise, return to step S32.
11. The drilling rig equipment lifecycle management method according to claim 10, characterized in that, The second update condition includes: The current iteration count has reached the preset iteration count threshold.
12. A drilling rig equipment lifecycle management device, characterized in that, include: The data acquisition module is configured to acquire the current operating parameters and equipment parameters of the target drilling rig in response to the user's equipment management operation command for the target drilling rig. The parameter display module is configured to display the current operating parameters of the target drilling rig in a first display area and the equipment parameters of the target drilling rig in a second display area. The equipment status prediction module is configured to acquire historical operating parameters of the target drilling rig, use the historical operating parameters of the target drilling rig as input, call a preset equipment status prediction model to output the predicted equipment status of the target drilling rig, display the predicted equipment status of the target drilling rig in a third display area, and generate an alarm signal when the predicted equipment status of the target drilling rig is abnormal. The equipment status prediction model is obtained by training an improved support vector machine model with historical operating parameters of the target drilling rig under different equipment states.
13. A drilling rig equipment lifecycle management system, characterized in that, include: The drilling rig equipment lifecycle management device as described in claim 12; as well as A drilling rig equipment parameter acquisition device, which is used to acquire the operating parameters and equipment parameters of the target drilling rig equipment.
14. A computer-readable storage medium, characterized in that, The computer program stores a method for managing the lifecycle of drilling equipment as claimed in any one of claims 1-11, which, when executed by a processor, causes the processor to perform such method.
15. A terminal 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 a computer program, it implements the drilling equipment lifecycle management method as described in any one of claims 1-11.