CN113338894A - Control method of small intelligent drilling machine - Google Patents
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- 238000005553 drilling Methods 0.000 title claims abstract description 134
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- E—FIXED CONSTRUCTIONS
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- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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Abstract
A control method of a small intelligent drilling machine comprises the following steps; the method comprises the following steps: designing a control system of the small intelligent drilling machine and designing a control block diagram of an automatic bit feeding system by analyzing the whole process of the automatic bit feeding control system; step two: establishing a neural network lithologic recognition model by adopting a BP neural network algorithm, constructing a drilling parameter sample, and performing data training, verification and testing by adopting a neural network pattern recognition toolbox in software MATLAB to realize lithologic recognition; step three: combining with a simulated rock sample drilling test, obtaining drilling parameters including drilling speed, rotating speed, drilling pressure sensitivity and the like as training samples to carry out lithology recognition, independently analyzing a mathematical physical model of each control link of the automatic drilling part, designing a transfer function of each link, and connecting the three control links through interfaces by combining with a drilling relation of an actual automatic drilling control system. The invention is beneficial to reasonably selecting the type of the drill bit, adjusting the drilling parameters in time and improving the drilling efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent drilling machines, in particular to a control method of a small intelligent drilling machine.
Background
The intelligent petroleum drilling machine can be separated from human intervention, automatically complete the whole drilling process, and can perform big data fusion with drilling, geology, logging and other major, thereby effectively improving the drilling efficiency and the operation safety. In the face of complex stratum environments, lithology identification is one of important research contents in the aspects of stratum evaluation, reservoir description, real-time drilling monitoring and the like. The well drilling process is a dynamic and complex nonlinear process influenced by stratum factors and human factors, and is difficult to describe by establishing an accurate mathematical model by using a traditional method, so that the research on a novel oil drilling machine is particularly important.
The intelligent control algorithm adopted by the existing automatic bit-feeding control system is mostly PID control, has the defect of time lag, and aims to realize lithology recognition research under complex geological conditions, the neural network control technology based on the BP algorithm is applied to the control of a small intelligent drilling machine based on theoretical and actual requirements, the network is trained after sample data is carefully selected, the network after successful training can effectively control the drilling machine, the development cost of the control system is greatly reduced, and the operation efficiency of drilling is improved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a control method of a small intelligent drilling machine, wherein a neural network identification lithology model is established by adopting a BP neural network algorithm, a drilling parameter sample is constructed, lithology identification is carried out by utilizing a neural network toolbox in software MATLAB, an automatic drilling feeding system control block diagram is designed in Simulink, and the control method is applied to an oil drilling machine to realize constant drilling pressure drilling, so that the reasonable selection of the type of a drill bit, the timely adjustment of drilling parameters and the improvement of the drilling efficiency are facilitated.
In order to achieve the purpose, the invention adopts the technical scheme that:
a control method of a small intelligent drilling machine comprises the following steps;
the method comprises the following steps: determining the composition of a small intelligent drilling machine according to the drilling process requirement, designing the control system of the small intelligent drilling machine in detail by analyzing the whole process of an automatic drilling control system, obtaining a transfer function according to drilling parameters and designing a control block diagram of the automatic drilling control system in Simulink;
step two: establishing a neural network identification lithology model by adopting a BP neural network algorithm, adopting a three-layer BP neural network structure, wherein an input layer comprises 4 neurons which respectively correspond to input average bit pressure, average bit rotation speed, average torque and average mechanical drilling speed, an output layer comprises 2 neurons which respectively correspond to 2 types of lithology to be identified, constructing a drilling parameter sample, adopting a neural network mode identification toolbox in software MATLAB, inputting a drilling parameter data set, outputting a lithology data matrix, setting the number of nodes of a hidden layer to be 10, and carrying out data training, verification and testing to realize lithology identification;
step three: combining the first step and the second step, combining a simulated rock sample drilling test, obtaining drilling parameters including drilling speed, rotating speed, drilling pressure sensitivity and the like, using the drilling parameters as training samples to carry out lithology recognition, independently analyzing a mathematical physical model of each control link of the automatic drilling part, designing a transfer function of each link, and connecting the three control links through interfaces by combining a drilling relation of an actual automatic drilling control system.
The control system in the first step is a three-loop control system and comprises a hydraulic link, a winch drilling speed adjusting link and a drilling pressure link for controlling the lifting and lowering of a drilling tool, the hydraulic system adopts valve control electro-hydraulic servo control, the input quantity is a current signal at the end of an electro-hydraulic servo valve, the output quantity is the output pressure of a hydraulic cylinder, and the transfer function of the electro-hydraulic servo valve is determined as
The speed ring being formed by a hydraulic discThe braking force of the brake is realized, and a control block diagram of a speed loop is established by analyzing the relation between the pressure P and the lowering speed V of the drilling tool; and designing a drilling pressure regulator according to the relation between the drilling speed and the drilling pressure.
The hydraulic link, the link of regulating the drilling speed by a winch and the link of controlling the drill bit pressure by lifting and lowering the drilling tool are connected by an interface and controlled by BP-PID, and the controller comprises two parts: the system comprises a classical incremental PID controller and a BP neural network, wherein the classical PID controller directly carries out closed-loop control on a controlled object, three parameters kp, ki and kd of the controller are in an online adjustment mode, and the neural network adjusts the parameters of the PID controller according to the running state of the system so as to achieve optimization of a certain performance index.
The BP neural network has any nonlinear expression capability, a PID controller with a proportional link parameter Kp, an integral link Ki and a derivative link Kd self-tuning can be established by the BP neural network, and the output u (k) ═ u (k-1) + delta u (k) of the PID controller, wherein delta u (k) ═ Kp (e (k) — e (k-1)) + kie (k) + Kd (e (k) — 2e (k-1) + e (k-2)), and e (k)) is a time error. The system performs neural network learning, adjusts the weighting coefficient and the PID controller parameter in an online and self-adaptive manner, and realizes the constant bit pressure drilling of the drilling machine in real time.
The control system automatically detects the bit pressure, compares the bit pressure with a set bit pressure value, performs calculation through a control algorithm if deviation exists, outputs a set signal (O-20 mA current signal) of brake force to the hydraulic disc brake mechanism, so that the disc brake mechanism generates a certain brake force, the drilling tool starts to change speed under the brake force, the final speed is stable, the bit pressure reaches the set value under the speed, the functions of automatically identifying lithology and controlling drilling parameters in the normal drilling process of the drilling machine are realized, and the purpose of intelligently controlling the drilling of the drilling machine is achieved.
The invention has the beneficial effects that:
1. the lithology information of the current position of the drill bit can be identified aiming at strata with different lithologies, and drilling parameters can be reasonably selected aiming at the characteristics of the lithology of the strata;
2. aiming at the problem that the control mode of the petroleum drilling machine is backward, the BP algorithm is applied to an automatic bit feeding control system of the drilling machine, and the effective control on the drilling parameters can be improved.
The invention realizes real-time dynamic monitoring of drilling parameters in the drilling process by determining the basic composition of the drilling machine and designing the small intelligent drilling machine control system in detail, identifies lithology and automatically adjusts various drilling parameters, is suitable for on-site on-line monitoring, and has the advantage of intelligently controlling the drilling of the drilling machine.
Drawings
FIG. 1 is a block diagram of a small intelligent drilling rig.
FIG. 2 is a diagram of a lithology recognition neural network.
Fig. 3 is a diagram of an automatic bit feed control system.
FIG. 4 is a block diagram of a PID control system based on a BP neural network.
Fig. 5 is a flow chart of intelligent drill control.
Detailed Description
The present invention will be described in further detail with reference to examples.
Referring to fig. 1, the basic components of the small intelligent drilling machine are determined, including a bit pressure sensor, a top drive motor and the like, during the drilling process of a well bottom drill bit, the real-time bit pressure of the well bottom is obtained by solving the hook lifting force measured by the underground sensor and the gravity of a drilling tool, the well bottom bit pressure is compared with the given bit pressure on the ground through a pressure transmitter, and the comparison result is processed in a controller, so that the automatic drilling feeding with constant bit pressure is realized.
Referring to fig. 2, a neural network lithology recognition model is established by combining with simulated rock sample drilling tests and obtaining sensitive drilling parameters including drilling speed, rotating speed, drilling pressure and the like, and is used as a training sample for cluster analysis to perform lithology recognition.
Referring to fig. 3, a transfer function of the automatic drilling control process is designed and calculated, and the transfer function mainly comprises an inner ring hydraulic pressure ring, a middle ring speed ring and an outer ring drilling pressure ring, the transfer function is built in MATLAB/Simulink by combining with the drilling relation of an actual automatic drilling control system, the three control links are connected through interfaces, and an intelligent controller is applied to the three control links for simulation optimization, so that the automatic drilling simulation optimization process can be simulated better.
Referring to fig. 4, the neural network has arbitrary nonlinear expression capability, and PID control with an optimal combination can be realized through learning of system performance. By utilizing the BP neural network, a PID controller with self-tuning parameters Kp, Ki and Kd can be established.
The invention will be described in further detail with reference to the accompanying drawings in accordance with the normal drilling process of a drilling machine.
Step one, in the drilling process of the drilling machine, a sensor can acquire actual physical quantities of drilling, including bit pressure, drilling speed, torque and mechanical vibration, and sends the actual physical quantities into a neural network lithology recognition model, and the neural network lithology recognition model is used as a training sample for cluster analysis to perform lithology recognition, such as sandstone or granite.
And step two, the bottom hole drill bit of the automatic bit feeding system drills under the constant bit pressure, and when the actual bit pressure deviates from the given bit pressure, the bit pressure difference value is processed in the controller, so that a control cycle is formed, the bit pressure is controlled in real time, the bit pressure difference value is maintained within a range, and the safe and stable operation of the drill bit is guaranteed.
And step three, combining the step one and the step two, and under the condition that the lithology of the stratum is identified, giving the input weight as the optimal weight range value of the lithology. By utilizing the BP neural network, a PID controller with self-tuning parameters Kp, Ki and Kd can be established, and the drilling pressure is controlled in real time.
Claims (5)
1. A control method of a small intelligent drilling machine is characterized by comprising the following steps;
the method comprises the following steps: determining the composition of a small intelligent drilling machine according to the drilling process requirement, setting the control system of the small intelligent drilling machine in detail by analyzing the whole process of the automatic drilling control system, obtaining a transfer function according to drilling parameters and designing a control block diagram of the automatic drilling control system in Simulink;
step two: establishing a neural network identification lithology model by adopting a BP neural network algorithm, adopting a three-layer BP neural network structure, wherein an input layer comprises 4 neurons which respectively correspond to input average bit pressure, average bit rotation speed, average torque and average mechanical drilling speed, an output layer comprises 2 neurons which respectively correspond to 2 types of lithology to be identified, constructing a drilling parameter sample, adopting a neural network mode identification toolbox in software MATLAB, inputting a drilling parameter data set, outputting a lithology data matrix, setting the number of nodes of a hidden layer to be 10, and carrying out data training, verification and testing to realize lithology identification;
step three: combining the first step and the second step, combining a simulated rock sample drilling test, obtaining sensitive drilling parameters including drilling speed, rotating speed and drilling pressure as training samples to carry out lithology recognition, independently analyzing a mathematical physical model of each control link of the automatic drilling part, designing a transfer function of each link, and connecting the three control links through interfaces by combining a drilling relation of an actual automatic drilling control system.
2. The method as claimed in claim 1, wherein the control system in the first step is a three-loop control system including a hydraulic link, a winch speed-adjusting link and a bit pressure link for controlling the raising and lowering of the drilling tool, the hydraulic system is controlled by a valve-controlled electro-hydraulic servo, the input is a current signal at an end of the electro-hydraulic servo valve, the output is an output pressure of the hydraulic cylinder, and a transfer function of the electro-hydraulic servo valve is determined as
The speed loop is realized by the braking force of a hydraulic disc brake, and a control block diagram of the speed loop is established by analyzing the relation between the pressure P and the lowering speed V of the drilling tool; and designing a drilling pressure regulator according to the relation between the drilling speed and the drilling pressure.
3. The control method of the small intelligent drilling machine according to claim 2, wherein the hydraulic link, the link of regulating the drilling speed by the winch and the link of controlling the bit pressure of the drilling tool during the lifting and the lowering are connected through interfaces and controlled by BP-PID, and the controller comprises two parts: the system comprises a classical incremental PID controller and a BP neural network, wherein the classical PID controller directly carries out closed-loop control on a controlled object, three parameters kp, ki and kd of the controller are in an online adjustment mode, and the neural network adjusts the parameters of the PID controller according to the running state of the system so as to achieve optimization of a certain performance index.
4. The control method of the small intelligent drilling machine according to claim 3, wherein the BP neural network has any nonlinear expression capability, and a PID controller with the proportional link parameter Kp, the integral link Ki and the derivative link Kd self-tuning can be established by the BP neural network, and the output u (k) ═ u (k-1) + Δ u (k) of the PID controller, wherein Δ u (k) ═ Kp (e (k) — e (k-1)) + kie (k) + Kd (e (k) — 2e (k-1) + e (k-2)), and e (k)) is a time error. The system performs neural network learning, adjusts the weighting coefficient and the PID controller parameter in an online and self-adaptive manner, and realizes the constant bit pressure drilling of the drilling machine in real time.
5. The control method of a small intelligent drilling machine according to claim 1, characterized in that the control system automatically detects the bit pressure, then compares the bit pressure with the set value of the bit pressure, if there is deviation, the control algorithm calculates the deviation, and then outputs a set signal of braking force (0-20 mA current signal) to the hydraulic disc brake mechanism, so that the disc brake mechanism generates a certain braking force, the drilling tool starts to change speed under the braking force, the final speed is stable, the bit pressure reaches the set value under the speed, and the functions of automatically identifying lithology and controlling drilling parameters in the normal drilling process of the drilling machine are realized.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113792936A (en) * | 2021-09-28 | 2021-12-14 | 中海石油(中国)有限公司 | Intelligent lithology while drilling identification method, system, equipment and storage medium |
| CN114000862A (en) * | 2021-10-26 | 2022-02-01 | 中国地质大学（武汉） | Geological drilling process drilling speed intelligent control system based on dynamic optimization |
| CN115387777A (en) * | 2022-08-09 | 2022-11-25 | 中煤科工集团西安研究院有限公司 | Feeding and rotating control method of hydraulic tunnel drilling machine based on coal rock sensing |
| CN115680645A (en) * | 2022-09-27 | 2023-02-03 | 成都理工大学 | Rock mass characteristic real-time prediction method and system based on multi-source information fusion while drilling |
| CN117189071A (en) * | 2023-11-07 | 2023-12-08 | 克拉玛依市远山石油科技有限公司 | Automatic control method for core drilling rig operation |
| CN117287179A (en) * | 2023-11-27 | 2023-12-26 | 张家港市胜港机械制造有限公司 | Remote control system and method for precision drilling and production equipment |
| CN117910538A (en) * | 2024-03-19 | 2024-04-19 | 成都三一能源环保技术有限公司 | Downhole drilling instrument running state monitoring system based on machine learning |
| CN115387777B (en) * | 2022-08-09 | 2024-05-31 | 中煤科工集团西安研究院有限公司 | Feeding and rotation control method of hydraulic tunnel drilling machine based on coal rock sensing |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004090285A1 (en) * | 2003-03-31 | 2004-10-21 | Baker Hughes Incorporated | Real-time drilling optimization based on mwd dynamic measurements |
| CN101422825A (en) * | 2008-09-18 | 2009-05-06 | 深圳市金洲精工科技股份有限公司 | Method for detecting micro drill-bit wear degree, method using the said method and drilling device |
| CN110852018A (en) * | 2019-10-21 | 2020-02-28 | 中国石油集团长城钻探工程有限公司 | PSO drilling parameter optimization method based on neural network |
| CN112431585A (en) * | 2020-11-30 | 2021-03-02 | 西安石油大学 | Automatic bit feeding method based on deep belief network |
-
2021
- 2021-07-15 CN CN202110800830.2A patent/CN113338894B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2004090285A1 (en) * | 2003-03-31 | 2004-10-21 | Baker Hughes Incorporated | Real-time drilling optimization based on mwd dynamic measurements |
| CN101422825A (en) * | 2008-09-18 | 2009-05-06 | 深圳市金洲精工科技股份有限公司 | Method for detecting micro drill-bit wear degree, method using the said method and drilling device |
| CN110852018A (en) * | 2019-10-21 | 2020-02-28 | 中国石油集团长城钻探工程有限公司 | PSO drilling parameter optimization method based on neural network |
| CN112431585A (en) * | 2020-11-30 | 2021-03-02 | 西安石油大学 | Automatic bit feeding method based on deep belief network |
Non-Patent Citations (1)
| Title |
|---|
| 雷俊红;张伟;: "基于BP算法的自动送钻钻压优化控制技术研究", no. 01, pages 52 - 56 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113792936A (en) * | 2021-09-28 | 2021-12-14 | 中海石油(中国)有限公司 | Intelligent lithology while drilling identification method, system, equipment and storage medium |
| CN114000862A (en) * | 2021-10-26 | 2022-02-01 | 中国地质大学（武汉） | Geological drilling process drilling speed intelligent control system based on dynamic optimization |
| CN115387777A (en) * | 2022-08-09 | 2022-11-25 | 中煤科工集团西安研究院有限公司 | Feeding and rotating control method of hydraulic tunnel drilling machine based on coal rock sensing |
| CN115387777B (en) * | 2022-08-09 | 2024-05-31 | 中煤科工集团西安研究院有限公司 | Feeding and rotation control method of hydraulic tunnel drilling machine based on coal rock sensing |
| CN115680645A (en) * | 2022-09-27 | 2023-02-03 | 成都理工大学 | Rock mass characteristic real-time prediction method and system based on multi-source information fusion while drilling |
| CN117189071A (en) * | 2023-11-07 | 2023-12-08 | 克拉玛依市远山石油科技有限公司 | Automatic control method for core drilling rig operation |
| CN117287179A (en) * | 2023-11-27 | 2023-12-26 | 张家港市胜港机械制造有限公司 | Remote control system and method for precision drilling and production equipment |
| CN117287179B (en) * | 2023-11-27 | 2024-02-06 | 张家港市胜港机械制造有限公司 | Remote control system and method for precision drilling and production equipment |
| CN117910538A (en) * | 2024-03-19 | 2024-04-19 | 成都三一能源环保技术有限公司 | Downhole drilling instrument running state monitoring system based on machine learning |
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