CN110458824B - Drill bit wear detection method - Google Patents
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- 238000005299 abrasion Methods 0.000 claims abstract description 48
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- 238000003708 edge detection Methods 0.000 claims description 12
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- 238000005457 optimization Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
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Abstract
The invention discloses a method for detecting the abrasion of a drill bit, which can acquire a key characteristic value of the abrasion of the drill bit by utilizing image recognition, analyze and recognize information of a drill bit body, a joint, a tire body, a cutting tooth, a nozzle, a blade, a bearing, a palm, a cone and the like, compare with a preset database, output the abrasion detection and evaluation results of the drill bit and give suggestions for optimizing and improving the drill bit. If the situation that the detection and the identification cannot be carried out occurs, self-learning training can be carried out, a database is expanded, and the accuracy rate of drill bit abrasion identification is improved. The technical problems that the wear rate of a traditional manual detection drill bit is inaccurate in result and low in detection efficiency are solved.
Description
Technical Field
The invention belongs to the technical field of petroleum drilling, and particularly relates to a drill bit abrasion detection method.
Background
In oil drilling, a drill bit is the primary tool for breaking rock and a borehole is formed by the drill bit breaking rock. The drill bit has a great influence on the drilling quality, the drilling speed and the drilling cost. The abrasion of the drill bit is a main factor influencing the drilling speed, and the detection of the abrasion degree of the drill bit is beneficial to the performance evaluation of the drill bit, the optimization and the improvement of the drill bit and the preparation of reasonable drilling measures by field engineers. International IADC employs a bit wear leveling system table that includes various codes required to wear level roller cone bits and fixed cutter bits. However, the existing drill bit wear detection still depends on manual experience rough analysis, the subjectivity is strong, the manual detection can judge the drill bit wear defects differently due to different judgment standards and experience differences of different people, the detection efficiency is low, automatic, intelligent, qualitative and quantitative accurate detection and evaluation can not be carried out on the drill bit wear, and the optimization and improvement of the drill bit are restricted.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a drill bit wear detection method, which can effectively identify the wear condition of a drill bit and solve the technical problems of inaccurate wear rate result and low detection efficiency of the traditional manual detection of the drill bit.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for detecting the abrasion of a drill bit is characterized by comprising the following steps:
a. presetting a database, recording an IADC drill bit abrasion grading standard in the database, establishing an IADC drill bit abrasion grading table, and simultaneously establishing a one-to-one corresponding relation among abrasion characteristics, abrasion reasons and abrasion prevention measures;
b. judging whether an image needs to be analyzed through the image collector, if so, executing the step c, otherwise, continuously executing the step;
c. the drill bit comprises a drill bit body, a joint, a tire body, cutting teeth, a nozzle, blades, a bearing, a leg and a cone, wherein each component has different geometric characteristics, whether the drill bit exists in an image is analyzed by using a geometric characteristic method and a local characteristic analysis method, if so, the step d is executed, otherwise, the step is continuously executed;
d. extracting each component of the drill bit in the image by adopting a SURF algorithm, determining the area of each component, determining the size, the position and the distance attribute of each component, and calculating the geometric characteristic quantity of each component according to the attribute to obtain the type and the quantity of each component; detecting and extracting the edges of the components by using a Canny edge detection algorithm, comparing the extracted edges of the components with an IADC (intrinsic direct current) drill bit abrasion grading table in a database, outputting 7 contents of inner row teeth, outer row teeth, abrasion characteristics, positions, seals/bearings, gauge diameters and other abrasion characteristics according to an IADC drill bit abrasion grading standard in the database after comparison, if the abrasion detection is normal, normally outputting an IADC drill bit abrasion code, executing the step e, otherwise, executing the step f;
e. prompting a user to additionally input a tripping reason, combining the tripping reason with 7 items of obtained inner row teeth, outer row teeth, abrasion characteristics, positions, seals/bearings, gauge diameter and other abrasion characteristics, outputting an IADC drill bit abrasion grading result, and executing the step g;
f. inputting the types and the quantity of all components of the drill bit into a database, converting all components of the drill bit with different defects into corresponding images, establishing a wear image detection training model by using an unsupervised layer-by-layer training strategy and a BP algorithm for wear image recognition training, when the training recognition rate reaches more than 90%, using the wear image detection training model to recognize and detect the wear image of the drill bit, and executing the step e after the detection is finished;
g. and outputting a wear prevention measure of the drill bit according to the wear characteristics and the wear reasons, and finishing the detection of the drill bit wear.
The detection flow of the Canny edge detection algorithm in the step d is as follows:
(1) Using a Gaussian filter to smooth the image and filter noise of the image;
(2) Calculating the gradient strength and direction of each pixel point in the image;
(3) Applying non-maximum suppression to eliminate spurious responses due to edge detection;
(4) Applying dual threshold detection to determine true and potential edges of various components of the drill bit;
(5) And finally finishing edge detection and extraction by restraining isolated weak edges.
The invention has the advantages that:
the invention can acquire the worn drill bit image by using the image collector, acquire the key characteristic value of drill bit wear by using the image identification method, analyze and identify the wear characteristics and parameter information of a tire body, a blade, a cutting tooth and the like, compare with an IADC drill bit wear grading table, output the drill bit wear detection and evaluation results and give suggestions for drill bit optimization and improvement. If the situation that the detection and the identification cannot be carried out occurs, self-learning training can be carried out, a database is expanded, and the accuracy rate of drill bit abrasion identification is improved. The intelligent image recognition with high accuracy is realized, the conditions that the traditional manual detection judgment results are different and the detection efficiency is low are changed, the intelligent drill bit recognition system can be widely applied to the petroleum and natural gas drilling industry, intelligent drill bit management is facilitated, a drill bit user can optimize the drill bit, and the application prospect is wide.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of an apparatus used in the present invention;
FIG. 3 is a diagram illustrating a search of a matching criteria comparison database according to the present invention.
Detailed Description
The invention provides a drill bit abrasion detection method, which comprises the following steps:
a. presetting a database, recording an IADC drill bit abrasion grading standard in the database, establishing an IADC drill bit abrasion grading table, and establishing a one-to-one correspondence relationship among abrasion characteristics, abrasion reasons and abrasion prevention measures.
Specifically, 8 items of contents including inner row teeth, outer row teeth, wear characteristics, positions, gauge diameters, seals/bearings, other wear characteristics and tripping reasons describe the wear grading of the drill bit, namely compared with a new drill bit, the most significant state change characteristics and tripping reasons of the worn drill bit, establish a one-to-one correspondence relationship between the wear grading codes of the drill bit and the wear characteristics, and simultaneously establish a one-to-one correspondence relationship between the wear characteristics and the wear reasons and effective measures for preventing wear.
b. And (4) judging whether an image needs to be analyzed or not through the image collector, if so, executing the step c, otherwise, continuously executing the step.
c. The drill bit comprises a drill bit body, a joint, a tire body, cutting teeth, a nozzle, blades, a bearing, a leg and a cone, wherein different geometrical characteristics exist in all components, whether the drill bit exists in an image is analyzed by using a geometrical characteristic method and a local characteristic analysis method, if yes, the step d is executed, and otherwise, the step is continuously executed.
d. Extracting each component of a drill bit in an image by adopting an SURF algorithm, specifically extracting a bit body, a joint, a matrix, cutting teeth, a nozzle, blades, a bearing, a leg, a cone and the like, determining the area of each component, determining the size, the position and the distance attribute of each component, and calculating the geometric characteristic quantity of each component according to the attribute to obtain the type and the quantity of each component, namely obtaining the type and the quantity of the bit body, the type and the quantity of the joint, the type and the quantity of the matrix, the type and the quantity of the cutting teeth, the type and the quantity of the nozzle, the type and the quantity of the blades, the type and the quantity of the bearing, the type and the quantity of the leg, the type and the quantity of the cone and the like; and then detecting and extracting the edges of the components by using a Canny edge detection algorithm, comparing the extracted edges of the components with an IADC (intrinsic induced wear) drill bit wear grading table in a database, outputting 7 contents of inner row teeth, outer row teeth, wear characteristics, positions, seals/bearings, gauge diameters and other wear characteristics according to an IADC drill bit wear grading standard in the database after comparison, if the wear detection identification is normal, normally outputting an IADC drill bit wear code, executing the step e, and otherwise, executing the step f.
In this step, the detection flow of the Canny edge detection algorithm is as follows:
(1) Using a Gaussian filter to smooth the image and filtering out noise of the image;
(2) Calculating the gradient strength and direction of each pixel point in the image;
(3) Applying Non-Maximum Suppression (Non-Maximum Suppression) to eliminate spurious responses caused by edge detection;
(4) Applying a Double-Threshold (Double-Threshold) test to determine the true and potential edges of the various components of the drill bit;
(5) Edge detection and extraction is finally completed by suppressing isolated weak edges.
e. And prompting a user to additionally input a tripping reason, combining the tripping reason with 7 items of the obtained inner row teeth, outer row teeth, wear characteristics, positions, seals/bearings, gauge diameter and other wear characteristics, outputting an IADC drill bit wear grading result, and executing the step g.
f. Inputting the types and the quantity of all components of the drill bit into a database, converting all components of the drill bit with different defects into corresponding images, establishing a wear image detection training model by using an unsupervised layer-by-layer training strategy and a BP algorithm for wear image identification training, identifying and detecting the wear image of the drill bit by using the wear image detection training model when the training recognition rate reaches more than 90%, and executing the step e after the detection is finished;
g. and outputting a wear prevention measure of the drill bit according to the wear characteristics and the wear reasons, and finishing the detection of the drill bit wear.
The invention also provides a device for the input method, which comprises a database module, an image recognition module and a learning training module. The database module comprises a drill bit database unit and a database matching unit; the image identification module comprises an image judgment unit, an image acquisition unit, a drill bit identification unit, a drill bit locking unit and a component identification unit.
A database unit: the method is used for presetting an IADC drill bit abrasion grading table, and establishing a one-to-one correspondence relationship among an inner row of teeth, an outer row of teeth, abrasion characteristics, positions, gauge diameters, seals/bearings, other abrasion characteristics and drilling causes, and abrasion characteristics, abrasion causes and effective measures for preventing abrasion;
a database matching unit: for searching a database of matching drill bits;
an image determination unit: the image analysis device is used for judging whether images need to be analyzed;
an image acquisition unit: for acquiring an image of the drill bit;
a drill bit recognition unit: for identifying a drill bit in an image; each component of the drill bit has different geometric characteristics, and whether the drill bit exists in the image is analyzed by using a geometric characteristic method and a local characteristic analysis method;
a drill locking unit: for locking the position of the drill bit in the image;
a component recognition unit: for identifying and analyzing head body type and number, joint type and number, carcass type and number, cutting tooth type and number, nozzle type and number, blade type and number, bearing type and number, leg type and number, cone type and number, etc.;
an output unit: the detection device is used for outputting detection results and information and prompting a user for next operation;
a learning and training module: the parameters for new recognition are additionally recorded into the drill data, self-training is carried out by utilizing a Convolutional Neural Network (CNN) method, the one-to-one corresponding relation between the abrasion grading and the abrasion image characteristic value, the abrasion reason and the effective measure for preventing abrasion is additionally established, and the accuracy rate of drill abrasion detection and recognition is improved.
The invention improves the accuracy of drill bit abrasion detection and identification, and the applicant develops indoor experiments to show that the invention detects the drill bit abrasion based on image identification, the detection efficiency is improved by more than 20 times compared with manual work, and the detection accuracy can reach more than 90%.
Claims (2)
1. A method for detecting the abrasion of a drill bit is characterized by comprising the following steps:
a. presetting a database, recording an IADC drill bit abrasion grading standard in the database, establishing an IADC drill bit abrasion grading table, and simultaneously establishing a one-to-one corresponding relation among abrasion characteristics, abrasion reasons and abrasion prevention measures;
b. judging whether an image needs to be analyzed through the image collector, if so, executing the step c, otherwise, continuously executing the step;
c. the drill bit comprises a drill bit body, a joint, a tire body, cutting teeth, a nozzle, blades, a bearing, a leg and a cone, wherein each component has different geometric characteristics, whether the drill bit exists in an image is analyzed by using a geometric characteristic method and a local characteristic analysis method, if so, the step d is executed, otherwise, the step is continuously executed;
d. extracting each component of the drill bit in the image by adopting a SURF algorithm, determining the area of each component, determining the size, the position and the distance attribute of each component, and calculating the geometric characteristic quantity of each component according to the attribute to obtain the type and the quantity of each component; then, detecting and extracting the edges of all the components by using a Canny edge detection algorithm, comparing the extracted edges of all the components with an IADC (intrinsic induced wear) drill bit wear grading table in a database, outputting 7 contents of inner row teeth, outer row teeth, wear characteristics, positions, seals or bearings, gauge diameters and other wear characteristics according to an IADC drill bit wear grading standard in the database after comparison, if the wear detection is normal, normally outputting an IADC drill bit wear code, executing the step e, otherwise, executing the step f;
e. prompting a user to additionally input a tripping reason, combining the tripping reason with 7 items of obtained inner row teeth, outer row teeth, wear characteristics, positions, seals or bearings, gauge diameters and other wear characteristics, outputting an IADC drill bit wear grading result, and executing the step g;
f. inputting the types and the quantity of all components of the drill bit into a database, converting all components of the drill bit with different defects into corresponding images, establishing a wear image detection training model by using an unsupervised layer-by-layer training strategy and a BP algorithm for wear image identification training, identifying and detecting the wear image of the drill bit by using the wear image detection training model when the training recognition rate reaches more than 90%, and executing the step e after the detection is finished;
g. and outputting a wear prevention measure of the drill bit according to the wear characteristics and the wear reasons, and finishing the detection of the drill bit wear.
2. A method of detecting wear in a drill bit as claimed in claim 1, wherein: the detection flow of the Canny edge detection algorithm in the step d is as follows:
(1) Using a Gaussian filter to smooth the image and filter noise of the image;
(2) Calculating the gradient strength and direction of each pixel point in the image;
(3) Applying non-maximum suppression to eliminate spurious responses caused by edge detection;
(4) Applying dual threshold detection to determine true and potential edges of various components of the drill bit;
(5) Edge detection and extraction is finally completed by suppressing isolated weak edges.
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