Drilling machine equipment life cycle management method, device and system and terminal equipment

Abstract

The invention provides a drilling machine equipment life cycle management method, device and system and terminal equipment, and relates to the technical field of drilling machine equipment management.The method comprises the steps that in response to an equipment management operation instruction of a user for target drilling machine equipment, current operation parameters of the target drilling machine equipment and equipment parameters of the target drilling machine equipment are obtained; the current operation parameters of the target drilling machine equipment are displayed in the first display area, and the equipment parameters of the target drilling machine equipment are displayed in the second display area; and obtaining historical operation parameters of the target drilling machine equipment, taking the historical operation parameters of the target drilling machine equipment as input, calling the equipment state prediction model to output a predicted equipment state of the target drilling machine equipment, and displaying the predicted equipment state in a third display area, the equipment state prediction model is obtained by training an improved support vector machine model through historical operation parameters of the target drilling machine equipment in different equipment states. The management efficiency of the drilling machine equipment and the prediction precision of the equipment state are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of drilling rig equipment management, and specifically to a drilling rig equipment lifecycle management method, a drilling rig equipment lifecycle management device, a drilling rig equipment lifecycle management system, a computer-readable storage medium and a terminal device. Background Art

[0002] With the development of oil drilling technology, drilling operations have made great progress in automation and intelligence. Various automated equipment and automatic operation methods have greatly improved the efficiency of drilling operations. However, due to the increasing number and complexity of the equipment used, how to achieve good management and efficient maintenance of equipment has gradually become a new problem. At present, in other industries, especially in the warehousing and logistics industry, the solution to this problem is mainly to establish a data management system for the equipment life cycle management, judge the equipment status, and diagnose the equipment health based on the analysis of historical data. In particular, the establishment of the full life cycle can fully reflect all the states of the equipment from commissioning to retirement, and also provide comprehensive data support for equipment updates and upgrades, and provide a reference for purchasing supplier equipment. However, due to the large variety of oil drilling equipment and the complex design, if a fault occurs, it will be difficult to determine the cause of the fault and find the fault point. In addition, the management of spare parts is also difficult, which brings great challenges to the maintenance of drilling equipment. Taking the winch and disc brake system in the drilling rig equipment as an example, the weight of the large rope in the system, the usage time of the large rope, the system operation time, the wear degree of the disc brake pad, etc. are all important factors that affect whether the system is operating in a good working condition. Therefore, during drilling operations, extra attention needs to be paid to the above equipment parameters. If the above reference parameters are completely measured and recorded manually, there will be many problems such as untimely, inaccurate and incomplete data recording, and excessively high labor costs. At the same time, it is difficult for humans to accurately predict the status of the equipment. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a drilling rig equipment lifecycle management method, a drilling rig equipment lifecycle management device, a drilling rig equipment lifecycle management system, a computer-readable storage medium and a terminal device to solve the above-mentioned problems.

[0004] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a drilling equipment lifecycle management method, comprising: In response to a user's equipment management operation instruction for a target drilling rig equipment, obtaining current operating parameters of the target drilling rig equipment and equipment parameters of the target drilling rig equipment; Displaying the current operating parameters of the target drilling equipment in a first display area, and displaying the equipment parameters of the target drilling equipment in a second display area; and Acquire 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, and 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 historical operating parameters of the target drilling rig equipment under different equipment states.

[0005] Optionally, the target drilling rig equipment includes a winch and a disc brake device, and the current operating parameters of the target drilling rig equipment include: The winch and disc brake equipment's accumulated winch operating time, rope ton-kilometers, disc brake operating time, disc brake system pressure, left working clamp pressure, right working clamp pressure, air source pressure, winch oil pressure, lubricating oil pressure, hook hanging weight, motor current and winch speed; The equipment parameters of the target drilling equipment include: Winch parameters, disc brake parameters, motor parameters, rope parameters and oil pump parameters.

[0006] Optionally, the device status of the target drilling rig device at least includes: A first device state and a second device state; The first device status is used to indicate that the target drilling equipment is in a normal operating state, and the second device status is used to indicate that the target drilling equipment is in an abnormal operating state; When the predicted equipment state of the target drilling equipment is abnormal, an alarm signal is generated, including: When the predicted device state of the target drilling rig device is abnormal, the predicted device state of the target drilling rig device is marked with a specified color in the third display area, and / or an alarm signal indicating that the predicted device state of the target drilling rig device is abnormal is sent to a specified terminal device.

[0007] Optionally, the training process of the device state prediction model includes: Acquire historical operating parameters of the target drilling rig equipment under normal operating conditions and historical operating parameters of the target drilling rig equipment under different abnormal operating conditions, and construct a training sample set based on the historical operating parameters of the target drilling rig equipment; The support vector machine model is trained by taking the historical operating parameters of the target drilling rig equipment as input and the equipment status corresponding to the target drilling rig equipment as output, and during the training process, the hyperparameters of the support vector machine model are optimized by an improved parameter optimization algorithm to determine the optimal hyperparameters of the support vector machine model.

[0008] Optionally, the hyperparameters of the support vector machine model include a penalty parameter and a kernel parameter of a kernel function, and the hyperparameters of the support vector machine model are optimized by an improved parameter optimization algorithm, including: Constructing a fitness function based on the error between the predicted value and the actual value of the support vector machine model; The penalty parameter and kernel parameter of the support vector machine model are mapped to the particle positions in the particle swarm algorithm, and based on the fitness function, the penalty function and kernel function of the support vector machine are optimized by the improved particle swarm algorithm.

[0009] Optionally, the improved particle swarm algorithm comprises: S10, initializing the particle swarm, determining the number of particles in the particle swarm and the initial position of each particle; S20, determining the fitness value of each particle based on the fitness function, and determining the individual extreme value and the group extreme value of each particle; S30, based on the individual extreme value and group extreme value of each particle, if the current particle meets the preset first update condition, the speed and position of each particle are updated by the preset speed update function and position update function, and step S40 is executed; otherwise, the first three particles with the best fitness values ​​are determined to be α wolf, β wolf and δ wolf, and the current particle is used as the current gray wolf, the position of the current gray wolf is determined by the gray wolf algorithm, and the position of the current particle is updated with the position of the current gray wolf, and step S40 is executed; S40, based on the fitness function, determine the individual extreme value and the group 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 searching, and output the current group extreme value as the optimal penalty function and the optimal kernel function of the support vector machine.

[0010] Optionally, 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 position update function comprises: 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 inertia weight, c 1 and c 2 is the learning factor, p ib is the individual extreme value of particle i, g ib is the group extreme value of the entire particle swarm, r 1 and r 2 is a random number, x 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 comprises: The inertia weight is updated by a preset weight update function, wherein the weight update function includes: w=w base *w factor Among them, w base is the basic inertia weight, w factor is the inertia weight adjustment factor.

[0011] Optionally, the calculation formula of 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 represents the maximum inertia weight, w min represents the minimum inertia weight, iter represents the current number of iterations, iter max represents the maximum number of iterations, rank represents the ranking of the fitness value of the current particle among the fitness values ​​of all particles, S represents the number of particles in the particle swarm, and p is a positive real number.

[0012] Optionally, the first update condition includes: Random number r 1 and r 2 Any one of them is less than the preset random number threshold.

[0013] Optionally, the grey wolf algorithm comprises: S31, initializing the gray wolf population, using the number of particles in the particle swarm as the population number of the gray wolf population, initializing the position and position update parameters of the gray wolf, wherein the position update parameters include a distance control parameter a, a coefficient vector A, and a coefficient vector C; S32, update the distance control parameter a, coefficient vector A and coefficient vector C of α wolf, β wolf and δ wolf by the following formula:

[0014]

[0015] a(t)=2-2iter / itermax S33, based on the updated distance control parameter a, coefficient vector A and coefficient vector C, update the position of the gray wolf by the following formula:

[0016] Among them, 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, and X 1 , X 2 , X 3 They represent the positions of the current gray wolf inferred from the positions of wolf α, wolf β, and wolf δ, 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.

[0017] Optionally, the second update condition includes: The current number of iterations reaches the preset iteration threshold.

[0018] A second aspect of the present application provides a drilling equipment lifecycle management device, comprising: A data acquisition module is configured to acquire current operating parameters of the target drilling rig equipment and equipment parameters of the target drilling rig equipment in response to a user's equipment management operation instruction for the target drilling rig equipment; A parameter display module, configured to display the current operating parameters of the target drilling equipment in a first display area, and display the equipment parameters of the target drilling equipment in a second display area; The equipment status prediction module is configured to obtain the historical operating parameters of the target drilling rig equipment, take 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, and 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 based on the historical operating parameters of the target drilling rig equipment under different equipment states.

[0019] The third aspect of the present application provides a drilling equipment lifecycle management system, comprising: The drilling rig equipment lifecycle management device as described above; and A drilling rig equipment parameter acquisition device, wherein the equipment parameter acquisition device is used to acquire operating parameters and equipment parameters of target drilling rig equipment.

[0020] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the drilling equipment lifecycle management method as described above.

[0021] The fifth aspect of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned drilling equipment lifecycle management method when executing the computer program.

[0022] The embodiments provided in this application have the following beneficial effects: This application effectively improves the management efficiency of drilling rig equipment and can effectively predict the equipment status of the drilling rig equipment.

[0023] Other features and advantages of the embodiments or implementations of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 A method flow chart schematically showing a drilling equipment lifecycle management method according to an embodiment of the present application; Figure 2 A schematic diagram of a winch management interface according to an embodiment of the present application is schematically shown; Figure 3 A schematic block diagram schematically shows a drilling equipment lifecycle management device according to an embodiment of the present application; Figure 4 A schematic diagram of a system network architecture of an embodiment of the present application is shown schematically; Figure 5 A schematic diagram of a generator health management interface according to an embodiment of the present application is schematically shown; Figure 6 A schematic diagram of a device communication management interface of an embodiment of the present application is schematically shown; Figure 7 A schematic diagram of a historical project management interface of an implementation method of the present application is schematically shown; Figure 8 The following is a schematic diagram of a parameter management interface of an embodiment of the present application; Fig. 9A schematic diagram of the structure of a terminal device according to an embodiment of the present application is shown schematically.

[0025] Description of Reference Numerals 10-terminal device, 100-processor, 101-memory, 102-computer program. DETAILED DESCRIPTION

[0026] The specific implementation of the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present application, and is not used to limit the embodiment of the present application.

[0027] In order to solve the above problems, Figure 1 As shown, the first aspect of the present application provides a drilling equipment lifecycle management method, comprising: S100, in response to a user's equipment management operation instruction for a target drilling rig equipment, obtaining current operating parameters of the target drilling rig equipment and equipment parameters of the target drilling rig equipment; S200, displaying the current operating parameters of the target drilling equipment in the first display area, and displaying the equipment parameters of the target drilling equipment in the second display area; S300, obtaining historical operating parameters of the target drilling rig equipment, taking the historical operating parameters of the target drilling rig equipment as input, calling a preset equipment status prediction model to output the predicted equipment status of the target drilling rig equipment, and displaying the predicted equipment status of the target drilling rig equipment in a third display area, and generating 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 historical operating parameters of the target drilling rig equipment under different equipment states.

[0028] In this way, the present application effectively improves the management efficiency of the drilling rig equipment and can effectively predict the equipment status of the drilling rig equipment.

[0029] In step S100, after receiving the equipment management operation instruction of the user for the target drilling equipment, firstly, the relevant parameters of the target drilling equipment are collected. For example, the target drilling equipment is a winch and a disc brake device. Figure 2 As shown in the figure, the current operating parameters of the winch and disc brake equipment include the accumulated operating time of the winch, the ton-kilometer of the large rope, the operating time of the disc brake, the pressure of the disc brake system, the pressure of the left working clamp of the disc brake, the pressure of the right working clamp of the disc brake, the air source pressure, the oil pressure of the winch, the lubricating oil pressure, the hanging weight of the large hook, the motor current and the speed of the winch; the equipment parameters of the winch and disc brake equipment include the winch parameters, the disc brake parameters, the motor parameters, the rope parameters, the oil pump parameters, etc. Among them, the current operating parameters and equipment parameters of the target drilling rig equipment can be directly obtained through the existing driller system.

[0030] In step S200, after the operating parameters and equipment parameters of the target drilling rig equipment are obtained, the operating parameters of the target drilling rig equipment, such as the accumulated operating time of the winch, the ton-kilometer of the large rope, the operating time of the disc brake, the pressure of the disc brake system, the pressure of the left working clamp of the disc brake, the pressure of the right working clamp of the disc brake, the air source pressure, the oil pressure of the winch, the lubricating oil pressure, the hanging weight of the large hook, the motor current and the winch speed are displayed in the first display area on the left side of the display interface; the equipment parameters of the target drilling rig equipment, such as the winch parameters, the disc brake parameters, the motor parameters, the rope parameters, the oil pump parameters, etc. are displayed in the second display area on the right side of the display interface.

[0031] In step S300, after receiving the equipment management operation instruction of the target drilling equipment from the user, the historical operation parameters of the target drilling equipment are obtained at the same time, the pre-trained equipment status prediction model is called to predict the equipment status of the current target drilling equipment, and the equipment status prediction result of the current target drilling 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 equipment status of the target drilling equipment at least includes: a first equipment status and a second equipment status, the first equipment status is used to indicate that the target drilling equipment is in a normal operating state, and the second equipment status is used to indicate that the target drilling equipment is in an abnormal operating state. The first equipment status includes but is not limited to excellent, good, general, etc., and the second equipment status includes but is not limited to poor, relatively poor, faulty, etc. When the predicted equipment status of the target drilling equipment is abnormal, an alarm signal is generated, including: when the predicted equipment status of the target drilling equipment is abnormal, the predicted equipment status of the target drilling equipment is marked as a specified color in the third display area, and / or an alarm signal indicating that the predicted equipment status of the target drilling equipment is abnormal is sent to a specified terminal device. For example, when it is predicted that the equipment status of the target drilling rig equipment is in a bad, relatively bad, etc. state, the status of the corresponding equipment will be marked in yellow in the third display area, or the alarm information of the equipment with a bad, relatively bad, etc. state will be sent to the corresponding management terminal to prompt the management personnel that the equipment status is abnormal.

[0032] In step S300, the device state prediction model can be obtained by training based on a support vector machine algorithm. The training process of the device state prediction model of the present application includes: S310. Obtain historical operating parameters of the target drilling rig equipment under normal operating conditions and historical operating parameters of the target drilling rig equipment under different abnormal operating conditions, and construct a training sample set based on the historical operating parameters of the target drilling rig equipment; for example, taking the winch and disc brake system as an example, obtain historical data on the disc brake system pressure under normal conditions, and historical data on the disc brake system pressure under conditions such as disc brake system failure and aging to construct a training sample set.

[0033] S320, using the historical operating parameters of the target drilling rig equipment as input, using the equipment status corresponding to the target drilling rig equipment as output to train the support vector machine model, and during the training process, optimizing the hyperparameters of the support vector machine model through the improved parameter optimization algorithm to determine the optimal hyperparameters of the support vector machine model. For example, when training the support vector machine algorithm, using the historical data of the disc brake system pressure of the winch and the disc brake system under normal working conditions and the historical data of the disc brake system pressure of the winch and the disc brake system under working conditions such as disc brake system failure and aging as input, using the equipment status of the disc brake system as output, the support vector machine algorithm is trained, and the hyperparameters of the support vector machine algorithm are optimized through the improved parameter optimization algorithm to determine the optimal hyperparameters of the support vector machine algorithm, thereby improving the prediction accuracy of the model.

[0034] Specifically, in step S320, the hyperparameters of the support vector machine model include a penalty parameter, namely the C parameter, and a kernel parameter, namely the Gamma parameter, of the kernel function. 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 to the training data. However, if the C value is too large, the model may be too complex, resulting in overfitting, that is, it performs well on the training data but poorly on the test data. The Gamma parameter is a hyperparameter that controls the influence range of the kernel function in the SVM. It determines the degree of influence of the sample point on the model, that is, the closer the data to the sample point is, the greater the weight in the model. For the case of using a Gaussian radial basis function (RBF) or a polynomial kernel function, the selection of the Gamma parameter is particularly important. For the RBF kernel function, a smaller Gamma value indicates a larger influence range, which may result in 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 result in a more complex and detailed decision boundary. For the polynomial kernel function, the Gamma parameter determines the similarity of features in the feature space.

[0035] It can be understood that the main idea of ​​SVM is to establish an optimal decision hyperplane so that the distance between the two types of samples closest to the plane on both sides of the plane is maximized, thereby providing good generalization ability for classification problems. Specifically, the objective function of the SVM model can be expressed as:

[0036]

[0037] Among them, ai and aj are the Lagrange multipliers corresponding to the training sample data xi and xj. The support vector can be determined by solving ai and aj. yi and yj represent the classification labels of samples xi and xj. K(xi, xj) represents the kernel function, which is used to map samples xi and xj to a higher-dimensional feature space, so as to achieve linear separability in this space. xi and xj are input feature vectors, that is, sample data points. In SVM, all data points xi and xj are used to construct the optimal decision hyperplane. m represents the number of samples, and C represents the penalty parameter.

[0038] In this application, the kernel function adopts the radial basis kernel function, which can be expressed as:

[0039] in, is the kernel parameter.

[0040] In this application, the hyperparameters of the support vector machine model are optimized by an improved parameter optimization algorithm, including: S321, constructing a fitness function based on the error between the predicted value and the actual value of the support vector machine model, for example, constructing a fitness function with the maximum accuracy of the prediction result of the model, and taking the accuracy of the prediction result of the model as the fitness function, for example, the fitness function can be expressed as: , where m represents the total number of device status categories. For example, if the device status categories include good, general, and poor, then m is 3, pj is the number of samples, and qj is the number of samples with incorrect prediction results.

[0041] S322, mapping the penalty parameter and kernel parameter of the support vector machine model to the particle position in the particle swarm algorithm, and optimizing the penalty function and kernel function of the support vector machine by the improved particle swarm algorithm based on the fitness function. In this application, the penalty function and kernel function of the support vector machine are optimized by the improved particle swarm algorithm. Specifically, the improved particle swarm algorithm includes: S10, initializing the particle swarm, determining the number of particles in the particle swarm and the initial position of each particle, the position of each particle corresponds to a solution, that is, the value of the penalty parameter and the kernel parameter of the support vector machine model.

[0042] S20, determining the fitness value of each particle based on the fitness function, and determining the individual extreme value and the group extreme value of each particle.

[0043] S30, based on the individual extreme value and group extreme value of each particle, if the current particle meets the preset first update condition, update the speed and position of each particle through the preset speed update function and position update function, and execute step S40; otherwise, determine the first three particles with the best fitness value 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 through the gray wolf algorithm, and update the position of the current particle with the position of the current gray wolf, and execute step S40. Among them, the first update condition includes: random number r 1 and r 2 Any one of them is less than a preset random number threshold. For example, the random number threshold can be set to 0.5.

[0044] S40, based on the fitness function, determine the individual extreme value and group 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 searching and output the current group extreme value as the optimal penalty function and optimal kernel function of the support vector machine.

[0045] Among them, 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)) Position update function, including: 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 inertia weight, c 1 and c 2 is the learning factor, p ib is the individual extreme value of particle i, g ib is the group extreme value of the entire particle swarm, r 1 and r 2 is a random number, x 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 of the present application further includes: updating the inertia weight by a preset weight update function, the weight update function including: w=w base *w factor Among them, w base is the basic inertia weight, w factor is the inertia weight adjustment factor.

[0046] Optionally, the calculation formula of 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 represents the maximum inertia weight, w min represents the minimum inertia weight, iter represents the current number of iterations, iter max represents the maximum number of iterations, rank represents the ranking of the fitness value of the current particle among the fitness values ​​of all particles, S represents the number of particles in the particle swarm, and p is a positive real number that can be set as required.

[0047] When updating the inertia weight, the present application first uses a linear decreasing weight to control the overall change trend of the inertia weight, and then adjusts the specific weight of the particle in combination with the power-law distribution function. The weight of each particle is calculated according to the power-law distribution function in each iteration, and is used together with the current speed and acceleration to update the position of the particle, so that a larger inertia weight can be used for global search at the beginning of the search, and then the inertia weight is gradually reduced and the specific weight of each particle is adjusted according to the power-law distribution function to increase the ability of local search, thereby balancing the effects of global search and local search and improving the performance of the particle swarm optimization algorithm.

[0048] In this application, the Grey Wolf Algorithm includes: S31, initializing the gray wolf population, using the number of particles in the particle swarm as the population number of the gray wolf population, initializing the position and position update parameters of the gray wolf, the position update parameters including the distance control parameter a, the coefficient vector A and the coefficient vector C; S32, update the distance control parameter a, coefficient vector A and coefficient vector C of α wolf, β wolf and δ wolf by the following formula:

[0049]

[0050] a(t)=2-2iter / itermax S33, based on the updated distance control parameter a, coefficient vector A and coefficient vector C, update the position of the gray wolf by the following formula:

[0051] Among them, 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, and X 1 , X 2 , X 3 They represent the positions of the current gray wolf inferred from the positions of wolf α, wolf β, and wolf δ, respectively, and X(t+1) represents the updated position of the current gray wolf; S34, if the second update condition is met, the position of the current particle is updated with the current position of the gray wolf, otherwise, return to step S32. The second update condition includes: the current iteration number reaches a preset iteration number threshold.

[0052] like Figure 3 As shown, the second aspect of the present application provides a drilling equipment lifecycle management device, comprising: A data acquisition module is configured to acquire current operating parameters of the target drilling rig equipment and equipment parameters of the target drilling rig equipment in response to a user's equipment management operation instruction for the target drilling rig equipment; A parameter display module, configured to display current operating parameters of the target drilling equipment in a first display area, and display equipment parameters of the target drilling equipment in a second display area; The equipment status prediction module is configured to obtain historical operating parameters of the target drilling rig equipment, take 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, and 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 historical operating parameters of the target drilling rig equipment under different equipment states.

[0053] It is understandable that the technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0054] The third aspect of the present application provides a drilling rig equipment lifecycle management system, comprising: a drilling rig equipment lifecycle management device as described above; and a drilling rig equipment parameter collection device, wherein the equipment parameter collection device is used to collect operating parameters and equipment parameters of the target drilling rig equipment.

[0055] Specifically, in this application, the drilling equipment lifecycle management system relies on the equipment parameters provided by the driller control system and other systems. When the parameter quantity reaches a certain level and the sample quantity is sufficient, the equipment status is predicted through relevant algorithms to provide a reference for the equipment maintenance plan. Figure 4 As shown, each device and control system communicates with each other through a switch and a network cable. During the data exchange process, the data server automatically saves the key parameters of the device, and the file server is mainly used to provide services such as user interaction, operation and maintenance management, and model calculation. In this application, the equipment parameters can be directly obtained through the existing driller control system, then the equipment parameter acquisition device of this application can be the corresponding data interface; or, the equipment parameters can also be directly obtained through the sensor set on the corresponding device, then the equipment parameter acquisition device can be the corresponding sensor.

[0056] In this application, the life cycle management system is mainly divided into a driller prompt subsystem, a data interaction management subsystem and an engineer management subsystem. Among them, the driller prompt subsystem only needs to briefly prompt the driller's equipment status, so it can be directly integrated into the existing driller control system.

[0057] The driller prompt subsystem can be accessed by the following methods: After the driller room is powered on, first enter the main interface of the driller control system, 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 prompt subsystem of the equipment health management system; take the generator set as an example, click the "Generator Set" image, and the page will jump to the following: Figure 5 The information prompt interface of the generator set shown in the figure allows you to observe the health status of the equipment more intuitively.

[0058] In the data interaction management subsystem, the data acquisition service of this application can be implemented using KEPServerEX software, which can run in an industrial computer to collect PLC and related equipment data in real time; after the target device data is collected, it can be shared through the OPC protocol; in addition, this application can also realize data exchange between PLCs and devices in other networks through wired protocols such as Profinet, Modbus, Profibus, etc.; among them, the remote engineer station needs to rely on the Internet to connect the remote device with the local device, and install an industrial router with SIM card or WIFI access in the local network. After encryption, the data is uploaded to the Internet. The remote engineer accesses this router through the Internet and can access the data in the local network after the connection is established.

[0059] In this application, the engineer management subsystem is the core module of the drilling rig equipment life cycle management. After entering the engineer management subsystem access interface, you can view the current communication status and network quality of the equipment by clicking the "Communication Management" interface in the navigation bar below the system. Figure 6 As shown, in addition, click the blank area below the project list to view the device's historical communication status list; by clicking "Project Management" in the system navigation bar, as shown Figure 7 As shown in the figure, you can view the operation status of this set of drilling equipment in the past well site and the historical operation data of the corresponding equipment; among them, "Parameter Management" and "Alarm Management" can view the historical data and status of the equipment in the current project, such as Figure 8 Click "Device Management Interface" to manage all devices in the current project. Click the corresponding device screen to directly enter the detailed management interface of the device, such as Figure 2 As shown, taking the winch system as an example, the middle part is the current operating data of the equipment, the right part is the initial parameters, fixed parameters, etc. of the equipment and its components, and the lower left corner is the current status of the equipment estimated based on the historical status of the equipment.

[0060] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the drilling equipment lifecycle management method as described above.

[0061] The fifth aspect of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned drilling equipment lifecycle management method when executing the computer program.

[0062] like Fig. 9 FIG. 1 is a schematic diagram of a terminal device provided in an embodiment of the present application. Fig. 9 As shown, 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, the steps in the above method embodiment are implemented. Alternatively, when the processor 100 executes the computer program 102, the functions of each module / unit in the above device embodiments are implemented.

[0063] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more modules / units are stored in the memory 101, and are executed by the processor 100 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the terminal device 10.

[0064] The terminal device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that Fig. 9 It is only an example of the terminal device 10 and does not constitute a limitation of the terminal device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0065] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0066] The memory 101 may be an internal storage unit of the terminal device 10, such as a hard disk or memory of the terminal device 10. The memory 101 may also be an external storage device of the terminal device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), etc. equipped on the terminal device 10. Further, the memory 101 may also include both an internal storage unit of the terminal device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0067] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0068] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0069] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A drilling equipment life cycle management method, characterized in that: include: In response to a user's equipment management operation instruction for a target drilling rig equipment, obtaining current operating parameters of the target drilling rig equipment and equipment parameters of the target drilling rig equipment; Displaying the current operating parameters of the target drilling equipment in a first display area, and displaying the equipment parameters of the target drilling equipment in a second display area; as well as Acquire 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, and 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 historical operating parameters of the target drilling rig equipment under different equipment states.

2. The drilling equipment life cycle management method according to claim 1, characterized in that: The target drilling rig equipment includes a winch and a disc brake device, and the current operating parameters of the target drilling rig equipment include: The winch and disc brake equipment's accumulated winch operating time, rope ton-kilometers, disc brake operating time, disc brake system pressure, left working clamp pressure, right working clamp pressure, air source pressure, winch oil pressure, lubricating oil pressure, hook hanging weight, motor current and winch speed; The equipment parameters of the target drilling equipment include: Winch parameters, disc brake parameters, motor parameters, rope parameters and oil pump parameters.

3. The drilling equipment life cycle management method according to claim 1, characterized in that: The equipment status of the target drilling equipment at least includes: A first device state and a second device state; The first device status is used to indicate that the target drilling equipment is in a normal operating state, and the second device status is used to indicate that the target drilling equipment is in an abnormal operating state; When the predicted equipment state of the target drilling equipment is abnormal, an alarm signal is generated, including: When the predicted device state of the target drilling rig device is abnormal, the predicted device state of the target drilling rig device is marked with a specified color in the third display area, and / or an alarm signal indicating that the predicted device state of the target drilling rig device is abnormal is sent to a specified terminal device.

4. The drilling equipment life cycle management method according to claim 1, characterized in that: The training process of the equipment state prediction model includes: Acquire historical operating parameters of the target drilling rig equipment under normal operating conditions and historical operating parameters of the target drilling rig equipment under different abnormal operating conditions, and construct a training sample set based on the historical operating parameters of the target drilling rig equipment; The support vector machine model is trained by taking the historical operating parameters of the target drilling rig equipment as input and the equipment status corresponding to the target drilling rig equipment as output, and during the training process, the hyperparameters of the support vector machine model are optimized by an improved parameter optimization algorithm to determine the optimal hyperparameters of the support vector machine model.

5. The drilling equipment life cycle 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 by an improved parameter optimization algorithm, including: Constructing a fitness function based on the error between the predicted value and the actual value of the support vector machine model; The penalty parameter and kernel parameter of the support vector machine model are mapped to the particle positions in the particle swarm algorithm, and based on the fitness function, the penalty function and kernel function of the support vector machine are optimized by the improved particle swarm algorithm.

6. The drilling equipment life cycle management method according to claim 5, characterized in that: The improved particle swarm algorithm comprises: S10, initializing the particle swarm, determining the number of particles in the particle swarm and the initial position of each particle; S20, determining the fitness value of each particle based on the fitness function, and determining the individual extreme value and the group extreme value of each particle; S30, based on the individual extreme value and group extreme value of each particle, if the current particle meets the preset first update condition, the speed and position of each particle are updated by the preset speed update function and position update function, and step S40 is executed; otherwise, the first three particles with the best fitness values ​​are determined to be α wolf, β wolf and δ wolf, and the current particle is used as the current gray wolf, the position of the current gray wolf is determined by the gray wolf algorithm, and the position of the current particle is updated with the position of the current gray wolf, and step S40 is executed; S40, based on the fitness function, determine the individual extreme value and the group 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 searching, and output the current group extreme value as the optimal penalty function and the optimal kernel function of the support vector machine.

7. The drilling equipment life cycle 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 position update function comprises: 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 inertia weight, c1 and c2 are learning factors, p ib is the individual extreme value of particle i, g ib is the population extreme value of the entire particle swarm, r1 and r2 are random numbers, x 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 comprises: The inertia weight is updated by a preset weight update function, wherein the weight update function includes: in=in base *In factor Among them, w base is the basic inertia weight, w factor is the inertia weight adjustment factor.

8. The drilling equipment life cycle management method according to claim 7, characterized in that: The calculation formula of the basic inertia weight includes: In base =in max -(In max -In min )*(iter / iter max ) w factor =(rank / S) p Among them, w max represents the maximum inertia weight, w min represents the minimum inertia weight, iter represents the current number of iterations, iter max represents the maximum number of iterations, rank represents the ranking of the fitness value of the current particle among the fitness values ​​of all particles, S represents the number of particles in the particle swarm, and p is a positive real number.

9. The drilling equipment life cycle management method according to claim 7, characterized in that: The first update condition includes: Either of the random numbers r1 and r2 is smaller than a preset random number threshold.

10. The drilling equipment life cycle management method according to claim 6, characterized in that: The grey wolf algorithm comprises: S31, initializing the gray wolf population, using the number of particles in the particle swarm as the population number of the gray wolf population, initializing the position and position update parameters of the gray wolf, wherein the position update parameters include a distance control parameter a, a coefficient vector A, and a coefficient vector C; S32, update the distance control parameter a, coefficient vector A and coefficient vector C of α wolf, β wolf and δ wolf by the following formula: a(t)=2-2iter / itermax S33, based on the updated distance control parameter a, coefficient vector A and coefficient vector C, update the position of the gray wolf by the following formula: Where X is the current position of the gray wolf, Xα, Xβ, and Xδ represent the positions of the 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 the 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 equipment life cycle management method according to claim 10, characterized in that: The second update condition includes: The current number of iterations reaches the preset iteration threshold.

12. A drilling equipment life cycle management device, characterized in that: include: A data acquisition module is configured to acquire current operating parameters of the target drilling rig equipment and equipment parameters of the target drilling rig equipment in response to a user's equipment management operation instruction for the target drilling rig equipment; A parameter display module, configured to display the current operating parameters of the target drilling equipment in a first display area, and display the equipment parameters of the target drilling equipment in a second display area; The equipment status prediction module is configured to obtain the historical operating parameters of the target drilling rig equipment, take 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, and 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 based on the historical operating parameters of the target drilling rig equipment under different equipment states.

13. A drilling equipment life cycle management system, characterized in that: include: The drilling equipment life cycle management device according to claim 12; as well as A drilling rig equipment parameter acquisition device, wherein the equipment parameter acquisition device is used to acquire operating parameters and equipment parameters of target drilling rig equipment.

14. A computer-readable storage medium, characterized in that: A computer program is stored which, when executed by a processor, causes the processor to execute the drilling equipment lifecycle management method according to any one of claims 1 to 11.

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 the computer program, the drilling equipment life cycle management method described in any one of claims 1 to 11 is implemented.

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