CN113808052A - Method for monitoring well cleaning in real time based on machine vision - Google Patents
Method for monitoring well cleaning in real time based on machine vision Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000004140 cleaning Methods 0.000 title claims abstract description 17
- 238000012544 monitoring process Methods 0.000 title claims abstract description 16
- 239000011435 rock Substances 0.000 claims abstract description 76
- 238000012545 processing Methods 0.000 claims abstract description 36
- 238000003708 edge detection Methods 0.000 claims abstract description 10
- 230000000877 morphologic effect Effects 0.000 claims abstract description 9
- 238000001914 filtration Methods 0.000 claims abstract description 8
- 230000002708 enhancing effect Effects 0.000 claims description 3
- LFULEKSKNZEWOE-UHFFFAOYSA-N propanil Chemical group CCC(=O)NC1=CC=C(Cl)C(Cl)=C1 LFULEKSKNZEWOE-UHFFFAOYSA-N 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000005553 drilling Methods 0.000 abstract description 4
- 238000010276 construction Methods 0.000 abstract description 3
- 239000012634 fragment Substances 0.000 description 12
- 238000010586 diagram Methods 0.000 description 4
- 238000005303 weighing Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 239000007790 solid phase Substances 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention relates to the technical field of drilling construction, and discloses a method for monitoring borehole cleaning in real time based on machine vision, which comprises the following steps of S101: collecting rock debris images of the screen surface of the vibrating screen; s102: carrying out image contrast enhancement processing on the acquired image; s103: carrying out graying and binarization processing on the image subjected to the contrast enhancement processing; s104: carrying out rock debris edge detection and morphological processing on the image subjected to graying and binarization processing; s105: performing smooth filtering processing and pixel marking on the image subjected to rock debris edge detection and morphological processing; s106: and (4) calculating the area of the rock debris in the image, calculating the concentration of the returned mud rock debris, and judging whether the well is clean. The invention calculates the area of the rock debris, estimates the concentration of the returned mud rock debris and judges whether the well is clean or not.
Description
Technical Field
The invention relates to the technical field of drilling construction, and particularly discloses a method for monitoring well cleaning in real time based on machine vision.
Background
In the drilling construction process, when the extended reach well works, due to the fact that the length of a well hole is long, the final inclination angle is too large, rock debris is easy to deposit in a large-inclination and horizontal well section to form a rock debris bed, the well hole is not clean, a drill bit cuts repeatedly, the phenomena of drill sticking, well hole blocking, stratum fracture, well leakage and the like are caused, and therefore when the well hole is not clean, people need to find and take corresponding measures in time.
In the prior art, an annular rock debris density is calculated to judge whether a borehole is clean or not by generally adopting a rock debris weighing method in engineering, but the method cannot monitor borehole cleaning in real time, and the weighing process is complex and low in efficiency. In order to overcome the problems, the method for monitoring the borehole cleaning in real time based on the machine vision is provided, the machine vision is used for monitoring the screen surface state of the vibrating screen to judge whether the borehole is clean or not, and the borehole cleaning condition is monitored in real time.
Disclosure of Invention
The invention aims to provide a method for monitoring borehole cleaning in real time based on machine vision, which monitors whether a borehole is clean or not in real time through machine vision and image recognition technology.
In order to achieve the above object, the basic scheme of the invention is as follows: a method for monitoring well bore cleaning in real time based on machine vision comprises the following steps,
s101: collecting rock debris images of the screen surface of the vibrating screen;
s102: carrying out image contrast enhancement processing on the acquired image;
s103: carrying out graying and binarization processing on the image subjected to the contrast enhancement processing;
s104: carrying out rock debris edge detection and morphological processing on the image subjected to graying and binarization processing;
s105: performing smooth filtering processing and pixel marking on the image subjected to rock debris edge detection and morphological processing;
s106: and (4) calculating the area of the rock debris in the image, calculating the concentration of the returned mud rock debris, and judging whether the well is clean.
Further, in S101, the time interval of the rock debris image acquisition is T/2, wherein T is the period of the conveyor belt.
Further, in S102, the gamma value is set to 0.2 in the process of enhancing the contrast of the rock debris image, and the brightness is enhanced.
Further, in S103, the threshold value is selected to be 0.4 during the binarization processing, so as to reduce the image distortion.
Further, in S104, after the rock debris edge is detected, a strel function is used for gap filling, wherein a square structural element is adopted, and the width is set to be 3 pixels.
Further, in S106, for the processed rock fragment image, the area calculation formula of each rock fragment is as follows:
in the formula, S1For the area of the debris on each image, P1Is the total pixel of the measured rock debris, P2For reference to the square total pixel, S0The area of a single reference square is shown, and n is the number of reference squares.
Further, after calculating the area of each rock fragment, estimating the mud concentration on the vibrating screen:
in the formula, S1The area of the rock debris on each image is shown, S is the area of each image, and a is the screening efficiency of the vibrating screen;
if eta is greater than 5%, the borehole is judged to be in a clean state, and if eta is less than 5%, the borehole is judged to be unclean.
The principle and the beneficial effects of the invention are as follows: the scheme fully utilizes the characteristics of high efficiency and high precision of machine vision, collects rock debris images on the screen surface of the vibrating screen, calculates the content of returned rock debris, and calculates the concentration of returned mud rock debris to judge whether a well is clean or not. The method is a non-contact measurement method, and is safer and more reliable compared with a method for measuring a solid phase by a mass flow meter; compared with a rock debris weighing method, the method is faster and can monitor the cleaning state of the well hole in real time.
Of course, the application does not necessarily require that all of the above-described technical effects be achieved at the same time.
Drawings
FIG. 1 is a flow chart in an embodiment of the invention;
FIG. 2 is a schematic illustration of a rock fragment in an embodiment of the invention;
FIG. 3 is an enhanced contrast image of a rock fragment image in an embodiment of the invention;
FIG. 4 is a graying graph of a rock fragment image according to an embodiment of the present invention;
FIG. 5 is a rock fragment image binarization graph in the embodiment of the invention;
FIG. 6 is a rock chip edge extraction diagram in an embodiment of the invention;
FIG. 7 is a rock fragment gap filling diagram in an embodiment of the present invention;
FIG. 8 is a rock chip filling diagram according to the present invention;
FIG. 9 is a rock fragment filtering diagram in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Example (b):
substantially as shown in figure 1: a method for monitoring well bore cleaning in real time based on machine vision comprises the following steps,
s101: collecting rock debris images of the screen surface of the vibrating screen;
s102: carrying out image contrast enhancement processing on the acquired image;
s103: carrying out graying and binarization processing on the image subjected to the contrast enhancement processing;
s104: carrying out rock debris edge detection and morphological processing on the image subjected to graying and binarization processing;
s105: performing smooth filtering processing and pixel marking on the image subjected to rock debris edge detection and morphological processing;
s106: and (4) calculating the area of the rock debris in the image, calculating the concentration of the returned mud rock debris, and judging whether the well is clean.
The specific implementation mode is as follows:
s101: the industrial explosion-proof camera is installed right above the screen surface of the vibrating screen, the time interval is T/2, T is the conveying period of the conveying belt, rock debris images on the screen surface of the vibrating screen are collected, and rock debris in various shapes are unevenly distributed on the vibrating screen as shown in figure 2.
S102: preprocessing is carried out on the acquired screen surface image of the vibrating screen, as shown in fig. 3, the contrast of the image is enhanced by using an Imadjust function, the gamma value is set to be 0.2 in the process of enhancing the contrast of the image, and the brightness is enhanced, so that the contrast of rock debris in the image and other areas is clearer.
S103: the rock debris image with the enhanced contrast is subjected to graying and binarization processing, the threshold value is selected to be 0.4 during binarization processing, the distortion of the image can be reduced better, and the graying and binarization results of the image are shown in fig. 4 and 5.
S104: and then, performing edge detection on the processed rock debris image, selecting a canny operator to perform edge detection, positioning all edge points, and reducing the error rate, so that the positioned edge is closer to the real edge of the rock debris, and the edge extraction result of the rock debris image is shown in fig. 6.
After the edges of the rock debris images are extracted, morphological processing of the images is performed, gap filling is performed by using a strel function, wherein a square structural element is adopted, the width is set to be 3 pixels, the gap filling is completed, and the rock debris images after the gap filling are shown in fig. 7. The width of the edge of the rock debris is increased, the unclosed area is closed, and the filling of the image is facilitated.
And then filling the interior of the rock debris to obtain a complete rock debris image as shown in figure 8, so that the pixel marking is conveniently carried out on the rock debris image in the rear direction, and the area of the rock debris is calculated.
S105: and performing adaptive Gaussian filtering processing on the filled image to remove Gaussian noise in the image. Firstly, a two-dimensional Gaussian filter function is utilized to generate a Gaussian kernel function:
where k is the gaussian kernel radius and σ is the standard deviation. When the convolution window slides, the gaussian kernel standard deviation σ can be obtained according to the magnitude of the variance in view of the property that the gaussian kernel coefficient weight is in direct proportion to the variance. The calculation formula of the variance in a certain region of the image is as follows:
in the formula SiJ is represented as a convolution window of the center point (i, j), and the larger the variance D (i, j) is, the larger the variance D (i, j) is represented at SiIn the j region, the larger the degree of dispersion of the pixel values is, the larger the weight of the gaussian kernel coefficient generated by selecting smaller sigma is, and the smaller the influence on the region is. According to this characteristic, the variance D (i, j) is compared with a two-dimensional gaussian filter function f (i, j) to obtain the function:
in the formula, since D (i, j) is constant, R (i, j) is a function of the Gaussian kernel radius k and the standard deviation sigma, i.e.
When R is 1, the weight of the parameter in the gaussian kernel is closest to the pixel value weight, and the standard deviation σ at that point is obtained from the variance D of the pixel values in the S (x, y) region. By the analogy, iteration is repeated, so that self-adaptive Gaussian filtering is formed, finally, Gaussian filtering processing is completed after all the pixels of the rock debris image are convoluted, the filtered rock debris image is shown in FIG. 9, Gaussian noise generated in earlier-stage image processing can be well eliminated, and errors of searching for a connected region are reduced.
Searching a connected region by using a bwleabel function, selecting 4 connected regions for pixel marking, and calculating the total pixel point P of the filled region1Is 184272.
S106: after total pixel points of rock fragments in the image are obtained, calculating the area of each rock fragment:
in the formula, P1Is the total pixel of the measured rock debris, P2For reference to the square total pixel, S0The area of a single reference square is shown, and n is the number of reference squares.
After the area of each rock fragment is calculated, the concentration of the returned mud is estimated:
in the formula, S1The area of the rock debris on each image is shown, S is the total area of each image, and a is the screening efficiency of the vibrating screen.
If eta is greater than 5%, judging that the well is in a clean state; if eta is less than 5%, the borehole is judged to be unclean.
Whether the well is clean or not is judged by judging the concentration of the returned mud and rock debris so as to take corresponding measures, reduce loss and enable drilling to be carried out smoothly.
In the embodiment, the advantage lies in that the characteristics of machine vision that efficiency is high, and the precision is high have been fully utilized, gather the detritus image on the shale shaker sifter, calculate the content of returning mud detritus, calculate the mud detritus concentration of returning simultaneously and judge whether the well is clean. The method is a non-contact measurement method, and is safer and more reliable compared with a method for measuring a solid phase by a mass flow meter; compared with a rock debris weighing method, the method is faster, and whether the well hole is clean or not can be monitored in real time.
The foregoing is merely an example of the present invention and common general knowledge in the art of specific structures and/or features of the invention has not been set forth herein in any way. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (7)
1. A method for monitoring well cleaning in real time based on machine vision is characterized in that: comprises the following steps of (a) carrying out,
s101: collecting rock debris images of the screen surface of the vibrating screen;
s102: carrying out image contrast enhancement processing on the acquired image;
s103: carrying out graying and binarization processing on the image subjected to the contrast enhancement processing;
s104: carrying out rock debris edge detection and morphological processing on the image subjected to graying and binarization processing;
s105: performing smooth filtering processing and pixel marking on the image subjected to rock debris edge detection and morphological processing;
s106: and (4) calculating the area of the rock debris in the image, calculating the concentration of the returned mud rock debris, and judging whether the well is clean.
2. The machine-vision-based method of monitoring wellbore cleaning in real time of claim 1, wherein: in S101, the time interval of rock debris image acquisition is T/2, wherein T is the period of the conveyor belt.
3. The machine-vision-based method of monitoring wellbore cleaning in real time of claim 2, wherein: in S102, the gamma value is set to be 0.2 in the process of enhancing the contrast of the rock debris image, and the brightness is enhanced.
4. The machine-vision-based method of monitoring wellbore cleaning in real time of claim 3, wherein: in S103, the threshold value is selected to be 0.4 during binarization processing, so as to reduce the image distortion.
5. The machine-vision-based method of monitoring wellbore cleaning in real time of claim 4, wherein: in S104, after the rock debris edge is detected, a strel function is used for gap filling, wherein a square structural element is adopted, and the width is set to be 3 pixels.
6. The method for monitoring borehole cleaning in real time based on machine vision according to claim 5, characterized in that in S106, for the processed rock debris image, the area calculation formula of each rock debris is as follows:
in the formula, S1For the area of the debris on each image, P1Is the total pixel of the measured rock debris, P2For reference to the square total pixel, S0The area of a single reference square is shown, and n is the number of reference squares.
7. The method for machine vision based real-time monitoring of wellbore cleanout of claim 6, wherein after calculating the area of each debris, estimating the mud concentration on the shaker screen:
in the formula, S1The area of the rock debris on each image is shown, S is the area of each image, and a is the screening efficiency of the vibrating screen;
if eta is greater than 5%, the borehole is judged to be in a clean state, and if eta is less than 5%, the borehole is judged to be unclean.
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| CN116580000A (en) * | 2023-05-08 | 2023-08-11 | 江阴市良友化工设备制造有限公司 | Chemical heat exchanger cleaning effect identification system |
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