阅读说明：本技术 一种基于LabVIEW智能化钻井系统及其应用 (Intelligent drilling system based on LabVIEW and application thereof ) 是由 罗光强 周策 陈文俊 李扬 于 2019-08-22 设计创作，主要内容包括：本发明提供一种基于LabVIEW智能化钻井系统及其应用,包括以下步骤,利用多路数据传感器采集钻井时各项数据,将采集的各项数据传输至数据采集卡,将数据采集卡内的数据传送至现场设备显示,并且通过电台发送至20km外的野外基地,野外基地通过其安装LabVIEW软件完成相关数据的处理和二维、三维图像的生成,并利用阈值判断、数据融合、神经网络实时实现事故的初步诊断和预测,将初步诊断和预测并传输至设备更完备的大型基地,完成多地专家会诊,最后将会诊结果发送至现场用于高效、专业的完成钻井工程。本发明可以实现井涌、井漏、岩心堵塞、取心工具到位报信四种复杂工况识别及卡钻、埋钻、烧钻、钻具断裂四种事故的及时诊断。(The invention provides an intelligent drilling system based on LabVIEW and application thereof, comprising the following steps of collecting various data during drilling by using a plurality of data sensors, transmitting the collected various data to a data collection card, transmitting the data in the data collection card to a field device for display, transmitting the data to a field base beyond 20km through a radio station, finishing the processing of related data and the generation of two-dimensional and three-dimensional images by the field base through installing LabVIEW software, realizing the preliminary diagnosis and prediction of accidents in real time by using threshold judgment, data fusion and a neural network, transmitting the preliminary diagnosis and prediction to a large base with more complete equipment, finishing the consultation of multiple experts, and finally transmitting the diagnosis result to the field for efficiently and professionally finishing drilling engineering. The invention can realize identification of four complex working conditions of well kick, well leakage, core blockage and in-place reporting of a coring tool and timely diagnosis of four accidents of drill sticking, buried drilling, drill burning and drill breakage.)

1. A LabVIEW intelligent drilling system is characterized by comprising the following modules:

the wireless receiving module receives a string of transmitted character string data in real time through a serial port;

the data intercepting and restoring module intercepts a string of effective character strings at one time according to the read string of character string data after 2 times of intercepting character string commands, intercepts data of a group of sensors at the other time, and restores the numerical values of the sensors through the fraction/index character string-to-numerical value conversion commands;

the secondary processing module is used for completing secondary operation, comparison, judgment and addition, subtraction, multiplication and division of the sensor numerical value to obtain secondary processing data;

the complex working condition judging module compares the data after the secondary processing module with a threshold value set in the system to judge the condition of the corresponding complex working condition;

the accident diagnosis module is used for establishing a deep learning sample by using the discrimination module result of the complex working condition and the normal drilling data, then carrying out normalization processing, then entering a BP neural network for training, outputting weight values of 8 characteristic variables corresponding to 5 working conditions, and carrying out accident diagnosis discrimination on the data after the secondary processing module;

the data display module is used for displaying the complex working conditions or the accident conditions in real time, and displaying curves and three-dimensional curves of partial data;

a remote transmission module; and sending the data of the complex working conditions for multi-place consultation.

2. The application of the intelligent drilling system based on LabVIEW in complex drilling conditions is characterized by comprising the following steps:

step 1, collecting data of drilling pressure, wellhead flow, drilling speed, pump capacity, mud outlet flow, mud pit volume, pump pressure, torque and mud inlet flow of a drilling well by adopting a plurality of multi-path sensors;

step 2, transmitting the data in the sensor in the step 1 to a USB data acquisition card;

step 3, transmitting the data in the USB data acquisition card in the step 2 to the drilling field acquisition equipment for display; and the data in the data acquisition card in the step 2 is sent to a radio receiving station outside 20km through a radio sending station;

step 4, the wireless receiving radio station sends the received data to field base acquisition equipment, and the judgment and diagnosis of working conditions and accident conditions are finished through a LabVIEW intelligent system in the wireless receiving radio station, and the working conditions and the accident conditions are displayed in real time;

s1, a wireless receiving radio station receives sent information in real time through a serial port;

s2, receiving information by the wireless receiving module, and restoring the numerical value of the sensor by the data intercepting and restoring module;

s2, the secondary processing module obtains the secondary processing data through secondary operation, comparison, judgment, addition, subtraction, multiplication and division;

s3, sending the secondary data to a complex working condition judging module to judge complex working conditions, and identifying well kick, well leakage, core blockage and in-place reporting complex working condition information in the drilling process engineering;

wherein the judgment standard is that,

(1) judging the well kick: in the normal drilling process, the outlet flow of the mud is larger than the inlet flow, the volume of the mud pit is increased, and whether the volume of the current mud pit is larger than that of the mud pit before 1min or not is judged;

(2) judging the well leakage: in the normal drilling process, the pump pressure suddenly drops, the outlet flow of the slurry is reduced, and the volume of a slurry pool is small;

(3) and (3) judging core blockage: during normal coring drilling, the pump pressure continues to increase suddenly and the outlet flow decreases;

(4) and (3) judging the in-place report of the coring tool: pumping the coring tool, the pump pressure spikes, and subsequently the pump pressure stabilizes.

Step 5, if any abnormal working condition is judged not to occur, the LabVIEW system keeps normal operation;

and if the abnormal working condition is judged, the diagnosis result is displayed through the display module, is transmitted through the remote transmission module, is subjected to multi-place consultation and is sent to the site for adjusting the drilling process.

3. An application of an intelligent drilling system based on LabVIEW in drilling accidents is characterized by comprising the following steps of 1, establishing an accident judgment reference of the intelligent drilling system based on LabVIEW;

firstly, working conditions in normal drilling, drilling sticking, drilling burying, drilling burning and drilling tool fracture 5 are used as learning samples, 8 drilling parameters including drilling speed, torque, rotating speed, hook load, pumping pressure, inlet flow, outlet flow and mud pit volume are used as characteristic variables to establish the learning samples, then normalization processing is carried out, then the learning samples enter a BP neural network for training, and weight values corresponding to 8 characteristic variables and 5 working conditions are output;

step 2, acquiring data of drilling pressure, wellhead flow, drilling speed, pump capacity, mud outlet flow, mud pool volume, pump pressure, torque and mud inlet flow of the drilled well by adopting a plurality of multi-path sensors;

step 3, transmitting the data in the sensor in the step 1 to a USB data acquisition card;

step 4, the data in the USB data acquisition card in the step 3 is transmitted to the drilling field acquisition equipment for display; and the data in the data acquisition card in the step 3 is sent to a 20km external wireless receiving radio station through a wireless sending radio station;

step 5, the wireless receiving radio station sends the received data to field base acquisition equipment, and the accident condition is judged and diagnosed by a LabVIEW intelligent system in the field base acquisition equipment and displayed in real time;

s1, a wireless receiving radio station receives a string of transmitted character string data in real time through a serial port;

s2, receiving information by the wireless receiving module, and restoring the numerical value of the sensor;

s2, the secondary processing module obtains the data of the secondary processing through secondary operation, comparison, judgment, addition, subtraction, multiplication and division, and then the data is input into the LabVIEW intelligent system accident diagnosis module for identification and diagnosis, thus obtaining the diagnosis results in normal drilling, drill sticking, drill burying, drill burning and drill breakage 5;

if an accident occurs, the diagnosis result is displayed through the display module, the remote transmission module is used for transmitting the diagnosis result, optimized drilling parameters are obtained through multiple consultation, the drilling process is adjusted, and the drilling process is sent to the site to be convenient for completing the drilling project in a high-efficiency professional manner;

and if the LabVIEW system is judged not to have any abnormal working condition, the LabVIEW system keeps normal operation.

Technical Field

The invention is applied to the working condition recognition and the accident diagnosis of three drilling instruments in scientific drilling or deep drilling, is convenient and quick, has strong applicability, and particularly relates to a method applied to complex working conditions and accident diagnosis.

Background

The existing working condition identification is mainly suitable for petroleum drilling, applies Beidou transmission, has high cost and cannot be applied to geological drilling; in geological drilling, the working condition identification is not perfect enough, the application rate is extremely low, and the accident diagnosis function is avoided.

Disclosure of Invention

The invention aims to overcome the defects in the prior art and provides an intelligent drilling system based on LabVIEW, which can realize identification of four complex working conditions including kick, lost circulation, core blockage and core in-place alarm and timely diagnosis of four accidents including drill jamming, drill burying, drill burning and drill tool fracture.

The invention adopts the following technical scheme:

a LabVIEW intelligent drilling parameter analysis system comprises the following modules:

the wireless receiving module receives a string of transmitted character string data in real time through a serial port;

the data intercepting and restoring module intercepts a string of effective character strings at one time according to the read string of character string data after 2 times of intercepting character string commands, intercepts data of a group of sensors at the other time, and restores the numerical values of the sensors through the fraction/index character string-to-numerical value conversion commands;

the secondary processing module is used for completing secondary operation, comparison, judgment and addition, subtraction, multiplication and division of the sensor numerical value to obtain secondary processing data;

the complex working condition judging module compares the data after the secondary processing module with a threshold value set in the system to judge the condition of the corresponding complex working condition;

the accident diagnosis module is used for establishing a deep learning sample by using the discrimination module result of the complex working condition and the normal drilling data, then carrying out normalization processing, then entering a BP neural network for training, outputting weight values of 8 characteristic variables corresponding to 5 working conditions, and carrying out accident diagnosis discrimination on the data after the secondary processing module;

the data display module is used for displaying the complex working conditions or the accident conditions in real time, and displaying curves and three-dimensional curves of partial data;

a remote transmission module; and sending the data of the complex working conditions for multi-place consultation.

An application of intelligent drilling system based on LabVIEW in complex drilling conditions comprises the following steps:

step 1, collecting data of drilling pressure, wellhead flow, drilling speed, pump capacity, mud outlet flow, mud pit volume, pump pressure, torque, mud inlet flow and the like of a drilling well by adopting a plurality of multi-path sensors;

step 2, transmitting the data in the sensor in the step 1 to a USB data acquisition card;

after the analog signal of the sensor enters the acquisition card, the acquisition card outputs a digital signal, namely 4-20mA, and then the digital signal is converted into a 1-5V signal, and then the measured value of the sensor is correspondingly calculated according to the measuring range of the sensor;

step 3, transmitting the data in the USB data acquisition card in the step 2 to the drilling field acquisition equipment for display; and the data in the data acquisition card in the step 2 is sent to a radio receiving station outside 20km through a radio sending station;

the well drilling field acquisition equipment converts all sensor data into a 2-bit decimal character string, then connects characters, connects all data to be transmitted into a string of character strings, and then transmits the string of character strings to a base within the range of 20Km according to a transmission command;

step 4, the wireless receiving radio station sends the received data to field base acquisition equipment, and the judgment and diagnosis of working conditions and accident conditions are finished through a LabVIEW intelligent system in the wireless receiving radio station, and the working conditions and the accident conditions are displayed in real time;

the wireless receiving radio station receives a string of transmitted character string data in real time through a serial port;

the LabVIEW intelligent system intercepts the read character string according to the intercepted character string for 2 times, intercepts a string of effective character strings at one time, intercepts data of a group of sensors at the other time, and restores the numerical value of the sensors through a fraction/number character string-to-numerical value conversion command;

then, the LabVIEW intelligent system obtains secondary processing data through secondary operation, comparison, judgment, addition, subtraction, multiplication and division, and then displays the obtained data in real time;

finally, comprehensive analysis and judgment are carried out by taking the bit pressure, the rotating speed, the drilling speed, the pump pressure, the pump capacity, the torque, the bit position, the well depth and the like as input parameter signals, different working conditions are jointly identified through the threshold judgment of the parameters, and the complex working condition information such as well kick, well leakage, core blockage, in-place reporting and the like in the drilling process engineering is identified;

(1) judging the well kick: in the normal drilling process, the outlet flow of the slurry is larger than the inlet flow, and the volume of the mud pit is increased, and the mud pit is judged to be a well kick (the corresponding relation is that the parameters such as the bit pressure, the rotating speed, the pump pressure, the pump amount, the torque, the position of a drill bit, the well depth and the like are not necessarily all parameters, and are a part of parameters, or virtual fingers);

(2) judging the well leakage: in the normal drilling process, the pump pressure suddenly drops, the outlet flow of the slurry is reduced, the volume of a slurry pool is small, and the slurry pool is judged to be lost circulation;

(3) and (3) judging core blockage: in the normal coring drilling process, the pump pressure is continuously increased suddenly, the outlet flow is reduced, and the core blockage is judged;

(4) and (3) judging the in-place alarm of the coring tool: when the coring tool is pumped, the pump pressure is suddenly increased, and then the pump pressure is stable, and the coring tool is judged to be in a fixed position.

Step 5, if the data are judged to have no accident or abnormal working condition, the LabVIEW system keeps normal operation;

and if the abnormal working condition is judged, transmitting the diagnosis result, obtaining optimized drilling parameters through multiple consultation, adjusting the drilling process, and sending the drilling parameters to the site to adjust the drilling process.

The application of the intelligent drilling system based on LabVIEW in drilling accidents comprises the following steps:

step 1, establishing an accident judgment reference of a LabVIEW intelligent drilling system;

firstly, working conditions in normal drilling, jamming drilling, burying drilling, burning drilling and drilling tool fracture 5 are used as learning samples, 8 drilling parameters including drilling speed, torque, rotating speed, hook load, pumping pressure, inlet flow, outlet flow and mud pit volume are used as characteristic variables to establish the learning samples, then normalization processing is carried out, then training is carried out in a BP neural network, weight values corresponding to 8 characteristic variables and 5 working conditions are output, and then received multipath drilling parameters are input into the neural network for recognition and diagnosis, so that diagnosis results in the normal drilling, jamming drilling, burying drilling, burning drilling and drilling tool fracture 5 can be obtained;

step 2, acquiring data of drilling pressure, wellhead flow, drilling speed, pump capacity, mud outlet flow, mud pool volume, pump pressure, torque and mud inlet flow of the drilled well by adopting a plurality of multi-path sensors;

step 3, transmitting the data in the sensor in the step 1 to a USB data acquisition card;

step 4, the data in the USB data acquisition card in the step 3 is transmitted to the drilling field acquisition equipment for display; and the data in the data acquisition card in the step 3 is sent to a 20km external wireless receiving radio station through a wireless sending radio station;

step 5, the wireless receiving radio station sends the received data to field base acquisition equipment, and the accident condition is judged and diagnosed by a LabVIEW intelligent system in the field base acquisition equipment and displayed in real time;

s1, a wireless receiving radio station receives a string of transmitted character string data in real time through a serial port;

s2, receiving information by the wireless receiving module, and restoring the numerical value of the sensor;

s2, the secondary processing module obtains the data of the secondary processing through secondary operation, comparison, judgment, addition, subtraction, multiplication and division, and then the data is input into the LabVIEW intelligent system accident diagnosis module for identification and diagnosis, thus obtaining the diagnosis results in normal drilling, drill sticking, drill burying, drill burning and drill breakage 5;

if an accident occurs, the diagnosis result is displayed through the display module, the remote transmission module is used for transmitting the diagnosis result, optimized drilling parameters are obtained through multiple consultation, the drilling process is adjusted, and the drilling process is sent to the site to be convenient for completing the drilling project in a high-efficiency professional manner;

and if the data are judged to have no accident or abnormal working condition, the LabVIEW system keeps normal operation.

The invention has the beneficial effects that:

the invention provides a drilling parameter condition identification and accident diagnosis system suitable for scientific drilling or deep drilling, which can realize timely analysis and diagnosis of four accidents of drilling blockage, buried drilling, burning drilling and drilling tool breakage under four complex conditions of well kick, well leakage, core blockage and in-place core tool reporting.

The system adopts a modular programming design, can add/modify judgment standards of complex working conditions or accident diagnosis according to needs, is convenient and quick, does not need to wholly eliminate related software, and can meet the requirements of various drilling geological conditions only by carrying out corresponding parameter design.

Benefit 1: the prior art system only has the functions of data acquisition, storage and simple working condition identification (such as lifting, lowering, drilling and the like); the existing system can realize short-distance monitoring and long-distance network monitoring within a range of 20km on the basis of the prior art, and simultaneously realize the identification of four complex working conditions and the diagnosis of four drilling complex accidents. The invention is applied to the discrimination mode of complex working conditions, different from the prior art, the prior art can only compare one by one or manually obtain working condition results according to data experience, but the invention can automatically realize discrimination without human participation, and does not carry out threshold value judgment on data one by one but selectively judge (for example, parameters for judging lost circulation, well kick and core blockage are different), thereby reducing the program execution time and the memory size of the system.

Benefit 2: the advantages brought by the working condition identification are as follows: complex working conditions are quickly identified and pre-judged in advance, so that drillers can conveniently adjust drilling parameters in time, and technicians can conveniently adjust the drilling process;

benefit 3: the drilling accident is difficult to be expressed by a simple mathematical model because the drilling engineering can not be seen at the well bottom, and the drilling process has many uncertain and complex conditions. Different from the existing neural network autonomous learning, the method adopts the judgment reference based on the complex working condition and the running condition of the normal working condition as the learning sample, reduces the amount of the sample, avoids repeated judgment, can solve the problem of system memory, improves the detection efficiency, and saves time for preventing drilling accidents.

Drawings

FIG. 1 is a flow chart of an application of a LabVIEW-based intelligent drilling system in drilling accident diagnosis;

FIG. 2 is a diagram of LabVIEW software processing data transmission software;

FIG. 3 is a diagram of processing software for data reception of LabVIEW software;

FIG. 4 is a diagram of a remote monitoring architecture for accident diagnosis;

FIG. 5 is a flow chart of the LabVIEW-based intelligent drilling system applied to the judgment of complex drilling conditions;

FIG. 6 is a schematic diagram showing the fact transmission of LabVIEW software;

FIG. 7 is a schematic diagram of LabVIEW software for remote network transmission monitoring;

fig. 8 is a flow chart of the LabVIEW software system program execution.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described below clearly and completely, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 2, 3 and 8, the intelligent LabVIEW drilling parameter analysis system comprises the following modules,

the wireless receiving module receives a string of transmitted character string data in real time through a serial port;

and the data intercepting and restoring module intercepts a string of effective character strings at one time according to the read string of character string data for 2 times of intercepting a character string command, intercepts data of a group of sensors at the other time, and restores the numerical value of the sensor through a fraction/index character string to numerical value conversion command.

And the secondary processing module is used for finishing secondary operation, comparison, judgment and addition, subtraction, multiplication and division of the sensor numerical value to obtain secondary processing data.

The complex working condition judging module compares the data after the secondary processing module with a threshold value set in the system to judge the condition of the corresponding complex working condition;

and the accident diagnosis module is used for establishing a deep learning sample by using the discrimination module result of the complex working condition and the normal drilling data, then carrying out normalization processing, then entering a BP neural network for training, outputting weight values of 8 characteristic variables corresponding to 5 working conditions, and carrying out accident diagnosis discrimination on the data after the secondary processing module.

And the data display module displays the complex working conditions or the accident conditions in real time, and displays curves and three-dimensional curves of partial data.

A remote transmission module; and sending the data of the complex working conditions for multi-place consultation.

As shown in fig. 2, 3, 6 and 7, an application of the intelligent drilling system based on LabVIEW in drilling complex working conditions comprises the following steps:

step 1, collecting data of drilling pressure, wellhead flow, drilling speed, pump capacity, mud outlet flow, mud pit volume, pump pressure, torque, mud inlet flow and the like of a drilling well by adopting a plurality of multi-path sensors;

step 2, transmitting the data in the sensor in the step 1 to a USB data acquisition card;

after the analog signal of the sensor enters the acquisition card, the acquisition card outputs a digital signal, namely 4-20mA, and then the digital signal is converted into a 1-5V signal, and then the measured value of the sensor is correspondingly calculated according to the measuring range of the sensor.

Step 3, transmitting the data in the USB data acquisition card in the step 2 to the drilling field acquisition equipment for display; and the data in the data acquisition card in the step 2 is sent to a radio receiving station outside 20km through a radio sending station;

the well drilling field acquisition equipment converts all sensor data into a 2-bit decimal character string, then connects characters, connects all data to be transmitted into a string (all parameters such as time, well depth, drilling pressure, rotating speed and the like), and then transmits the string to a base within the range of 20Km according to a transmission command.

Step 4, the wireless receiving radio station sends the received data to field base acquisition equipment, and the judgment and diagnosis of working conditions and accident conditions are finished through a LabVIEW intelligent system in the wireless receiving radio station;

a radio receiving station receives a string of transmitted character string data in real time through a serial port, as shown in fig. 3, which is a schematic diagram of data reception.

The LabVIEW intelligent system intercepts the read character string according to the intercepted character string for 2 times, intercepts a string of effective character strings at one time, intercepts data of one group of sensors at the other time (the same string of character strings intercepts the whole string of effective character strings at the first time, intercepts the data of each group of sensors at the second time, wherein the whole string of effective character strings contain a plurality of groups of sensor data), and restores the numerical value of the sensors through a fraction/number character string-to-numerical value conversion command.

Then, the LabVIEW intelligent system obtains the data of secondary processing through secondary operation, comparison, judgment, addition, subtraction, multiplication and division.

And finally, performing comprehensive analysis and judgment by taking the data after secondary operation as an input parameter signal, and identifying complex working condition information such as well kick, well leakage, core blockage, in-place reporting and the like in the drilling process engineering by jointly identifying different working conditions through the threshold judgment of the parameters and multiple parameters.

If the core is blocked, the identification is as follows: in the normal core drilling process, the pump pressure continuously increases suddenly, and the outlet flow is reduced, so that the core blockage can be judged.

Programming judgment language: the bit pressure is more than 0, the rotating speed is more than 0, the pump amount is more than 0, the pump pressure is more than 1.25 times of the pump pressure before 10 seconds, the pump pressure is more than 1.25 times of the pump pressure before 60 seconds, and the outlet flow of the slurry is less than 0.5 times of the outlet flow before 10 seconds.

(1) Judging the well kick: during normal drilling, the outlet flow of the mud is greater than the inlet flow, and the volume of the mud pit is increased, so that the well kick can be judged. Programming judgment language: bit pressure >0, and rotational speed >0, and pump rate >0, outlet flow rate of mud > inlet flow rate, volume of mud pit > mud pit volume before 10 minutes.

(2) Judging the well leakage: in the normal drilling process, the pump pressure suddenly drops, the outlet flow of the mud is reduced, the volume of the mud pool is small, and the well leakage can be judged. Programming judgment language: bit pressure >0, and rotational speed >0, and pump volume >0, and pump pressure < 0.8 times pump pressure before 10 seconds, outlet flow of mud < 0.75 times outlet flow before 10 seconds, volume of mud pit < mud pit volume before 10 minutes.

(3) And (3) judging core blockage: in the normal core drilling process, the pump pressure continuously increases suddenly, and the outlet flow is reduced, so that the core blockage can be judged. Programming judgment language: bit pressure >0, and rotational speed >0, and pump volume >0, and pump pressure > 1.25 times pump pressure 10 seconds ago, and pump pressure > 1.25 times pump pressure 60 seconds ago, and outlet flow of mud < 0.5 times outlet flow 10 seconds ago.

(4) And (3) judging the in-place report of the coring tool: when the coring tool is pumped, the pump pressure is suddenly increased, and then the pump pressure is stable, so that the coring tool can be judged to be in a fixed position. Programming judgment language: the bit pressure is 0, the rotation speed is 0, the pump amount is 0, the pump pressure is 1.25 times of the pump pressure before 1 second, and after 10 seconds, the pump pressure is approximately equal to 0.8 times of the pump pressure before 1 second.

If the data are judged to have no accidents or abnormal working conditions, the LabVIEW system keeps normal operation;

and if the abnormal working condition is judged, displaying the diagnosis result in real time, transmitting the diagnosis result, acquiring optimized drilling parameters through multiple consultation, adjusting the drilling process and transmitting the drilling parameters to a site to conveniently finish the drilling engineering in a high-efficiency professional manner, wherein the diagram is a schematic diagram of data processing software transmission as shown in fig. 2.

1-4 and 5-7, the application of the intelligent LabVIEW-based drilling system in the diagnosis of drilling accidents comprises

Step 1, establishing an accident judgment reference of a LabVIEW intelligent drilling system;

the method comprises the steps of firstly utilizing the working conditions of normal drilling, jamming drilling, burying drilling, burning drilling and drilling tool fracture 5 as learning samples, establishing the learning samples by taking 8 drilling parameters including drilling speed, torque, rotating speed, hook load, pumping pressure, inlet flow, outlet flow and mud pit volume as characteristic variables, then carrying out normalization processing, then entering a BP neural network for training, outputting weight values of the 8 characteristic variables corresponding to the 5 working conditions, and then inputting the received multipath drilling parameters into the neural network for recognition and diagnosis to obtain the diagnosis results of the normal drilling, jamming drilling, burying drilling, burning drilling and drilling tool fracture 5.

Step 2, acquiring data of drilling pressure, wellhead flow, drilling speed, pump capacity, mud outlet flow, mud pool volume, pump pressure, torque and mud inlet flow of the drilled well by adopting a plurality of multi-path sensors;

step 3, transmitting the data in the sensor in the step 1 to a USB data acquisition card;

after the analog signal of the sensor enters the acquisition card, the acquisition card outputs a digital signal, namely 4-20mA, and then the digital signal is converted into a 1-5V signal, and then the measured value of the sensor is correspondingly calculated according to the measuring range of the sensor;

step 4, the data in the USB data acquisition card in the step 3 is transmitted to the drilling field acquisition equipment for display; and the data in the data acquisition card in the step 3 is sent to a 20km external wireless receiving radio station through a wireless sending radio station;

the method comprises the following steps that (1) drilling site acquisition equipment converts all sensor data into a 2-bit decimal character string, then connects characters, connects all data to be sent into a string of character strings, and sends the string of character strings to a base within the range of 20Km according to a sending command;

the method comprises the following steps that (1) drilling site acquisition equipment converts all sensor data into a 2-bit decimal character string, then connects characters, connects all data to be sent into a string of character strings, and sends the string of character strings to a base within the range of 20Km according to a sending command;

step 5, the wireless receiving radio station sends the received data to field base acquisition equipment, and the accident condition is judged and diagnosed by a LabVIEW intelligent system in the field base acquisition equipment and displayed in real time;

s1, a wireless receiving radio station receives a string of transmitted character string data in real time through a serial port;

the LabVIEW intelligent system receives the character string information through the wireless receiving module, the read character string is intercepted by the data intercepting and restoring module according to the character string intercepted 2 times, one effective character string is intercepted at one time, the data of one group of sensors is intercepted at the other time, and the numerical value of the sensors is restored through the fraction/number character string-to-numerical value conversion command;

s2, the secondary processing module obtains the data of the secondary processing through secondary operation, comparison, judgment, addition, subtraction, multiplication and division, and then the data is input into the LabVIEW intelligent system accident diagnosis module for identification and diagnosis, thus obtaining the diagnosis results in normal drilling, drill sticking, drill burying, drill burning and drill breakage 5;

step 5, if the data are judged to have no accident or abnormal working condition, the LabVIEW system keeps normal operation;

and if the abnormal working condition or the accident is judged, displaying the diagnosis result through the display module, transmitting the diagnosis result, obtaining optimized drilling parameters through multiple consultation, adjusting the drilling process, and sending the drilling parameters to a site to conveniently finish the drilling project in a high-efficiency professional manner, as shown in fig. 4 and 8.

The method specifically comprises the following steps: and (3) complex working condition identification: