CN112581456A - Image crack real-time extraction method suitable for ultrasonic imaging logging - Google Patents

Image crack real-time extraction method suitable for ultrasonic imaging logging Download PDF

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CN112581456A
CN112581456A CN202011537010.0A CN202011537010A CN112581456A CN 112581456 A CN112581456 A CN 112581456A CN 202011537010 A CN202011537010 A CN 202011537010A CN 112581456 A CN112581456 A CN 112581456A
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张伟
李焱骏
师奕兵
吴桐
罗欣怡
李志鹏
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University of Electronic Science and Technology of China
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Abstract

The invention discloses an image crack real-time extraction method suitable for ultrasonic imaging logging, which comprises the steps of firstly, carrying out primary segmentation on an original image according to an Otus segmentation algorithm, and extracting effective crack information to establish a search space; then, referring to the crack development form, determining the crack reference position through a voting accumulation mechanism, and properly cutting the area where the crack is located according to the reference position; then, cutting the cut image along the longitudinal direction to obtain each subarea with partial crack information, and performing crack search and extraction on each subarea in parallel by using an ant colony algorithm; and finally splicing the sub-regions searched to obtain the crack path to obtain the complete crack.

Description

Image crack real-time extraction method suitable for ultrasonic imaging logging
Technical Field
The invention belongs to the technical field of well logging, and particularly relates to a real-time image crack extraction method suitable for ultrasonic imaging well logging.
Background
In the field of oil well logging, formation fractures are discontinuous sections which are widely distributed in different lithologies and are gradually formed through different diagenesis or tectonic deformation. The presence or absence of fractures is often a key indicator of the producibility of hydrocarbon reservoirs in tight formations. The well periphery imaging well logging technology is one of the mainstream stratum information acquisition methods at present. The imaging logging around the well can reflect the condition of the oil well by visual well wall images, can clearly see the development conditions of cracks and holes on the well wall, and is an important means for evaluating the oil well.
The well logging method mainly used in the field of imaging well logging around the well comprises an ultrasonic imaging method and a microresistivity scanning method, wherein the ultrasonic imaging method utilizes the echo reflection principle of ultrasonic waves in different media to digitize the arrival time and the amplitude of received echoes, and then the echoes are arrayed in the depth direction to form a well logging image. Compared with the microresistivity scanning method, the ultrasonic imaging method has lower resolution, but is widely applied due to strong penetrating power, high well coverage rate and simple instrument structure.
As shown in fig. 1, the probe of the ultrasonic imaging logging tool continuously rotates to scan the borehole wall, forming a scanning line, and the scanning lines are arranged along the depth to form a logging image, wherein each pixel in the image corresponds to the arrival time of the ultrasonic signal reflected by the borehole wall. The shaded area indicates that the echo arrival time measured in this region is small, whereas in turn the echo arrival time is large or even no echo reflections.
The crack identification method based on ultrasonic imaging logging is numerous, some mathematical models are used for fitting scattered points meeting the sine shape, but the well wall is damaged in actual drilling, and the crack shape is not necessarily standard sine, so that poor identification is caused; some maximum entropy threshold segmentation algorithms separate pixels corresponding to cracks from images, but part of non-crack information is often left, and detection results are affected. According to the method, an Otus-based threshold segmentation algorithm and an improved ant colony algorithm are combined to filter unnecessary interference information, and cracks are identified and extracted quickly and accurately.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an image crack real-time extraction method suitable for ultrasonic imaging logging.
In order to achieve the purpose, the invention discloses an image crack real-time extraction method suitable for ultrasonic imaging logging, which is characterized by comprising the following steps of:
(1) performing primary threshold segmentation on the original ultrasonic well logging image G by using an Otsu segmentation algorithm;
(1.1) calculating the between-class variance of the image G
Figure BDA0002853370930000021
Figure BDA0002853370930000022
μTPixels with omega (d) of 0 to d are classified into the crack region G as the gray-scale mean of the image G1μ (d) is the mean value of the gray levels of the pixels with gray levels from 0 to d;
(1.2) obtaining an optimal segmentation threshold T*So that the crack region G1And background region G2The sum of the information entropies of (a);
Figure BDA0002853370930000023
(1.3) all pixel points of the image G are subjected to threshold value T*Dividing the image into a crack region G1Setting the corresponding gray value as 0, and classifying the rest pixel points as background areas G2The corresponding gray value is set to 1;
(1.4) formation of crack region G by the number method1To form a feasible grid, the background region G2Converting into an infeasible grid, thereby generating a grid set corresponding to the binary image as a search space;
(2) through the base line y0Determines a target search area G in the search spaces；
(2.1) determining a baseline position; by applying the method to the fracture curve y ═ A sin (ω x + β) + y0Up searching point pair s1＝(x1,y1) And s2＝(x2,y2) To satisfy x1-x2Where a is the amplitude of the fracture curve, β is the initial phase, ω is the angular velocity, y is0Is the baseline position, and T is the period of the fracture curve;
then the midpoint s of such a point pair is satisfied0＝(x0,y0) Must fall at base line y0Then, the vertical coordinate information of all the midpoints is counted by utilizing a voting accumulation mechanism, so that the base line y is determined0The position of (a);
(2.2) taking the base line y0Cutting off the region to obtain a target search region Gs；
(3) Dividing the target search region Gs；
Searching for a target area GsDividing the substrate into L different regions along the longitudinal direction, and ensuring each sub-region G in the dividing processslThe inner crack area is still left-right through, wherein L is 1,2, …, L;
(4) simultaneously aiming each subarea G by utilizing ant colony algorithmslPerforming path search to completeExtracting image cracks in real time;
(4.1) setting the number of ant colony ants as K; the number of pixel points in the l sub-area is n; any two pixel points in the first sub-region
Figure BDA0002853370930000031
And
Figure BDA0002853370930000032
is a distance of
Figure BDA0002853370930000033
And i is not equal to j; any two pixel points in the first sub-region
Figure BDA0002853370930000034
And
Figure BDA0002853370930000035
initial pheromone concentration of
Figure BDA0002853370930000036
Any two pixel points in the first sub-area at the moment t
Figure BDA0002853370930000037
And
Figure BDA0002853370930000038
has a pheromone concentration of
Figure BDA0002853370930000039
Randomly selecting one pixel point at each of the left side and the right side in the first sub-area
Figure BDA00028533709300000310
And
Figure BDA00028533709300000311
as a starting point and an end point of a path search;
(4.2) calculating the ant k at the time tSlave pixel point in l sub-regions
Figure BDA00028533709300000312
Transfer to pixel
Figure BDA00028533709300000313
Transition probability of
Figure BDA00028533709300000314
Figure BDA00028533709300000315
Wherein the content of the first and second substances,
Figure BDA00028533709300000316
representing the current time t in the first sub-region
Figure BDA00028533709300000317
Transfer to pixel
Figure BDA00028533709300000318
The desired degree of; allkFor ant k, the set of pixel points to be searched, r represents allowkAny one pixel point; a is a pheromone importance factor; b is a heuristic function importance factor;
(4.3) Ant k from the beginning in the first subregion by roulette selection
Figure BDA00028533709300000319
Starting to go to the next pixel point according to the transfer probability until the destination is reached
Figure BDA00028533709300000320
Then, the pheromone concentration among the traversed pixel points is updated
Figure BDA00028533709300000321
Figure BDA00028533709300000322
Figure BDA00028533709300000323
Wherein rho is the volatilization rate of the pheromone,
Figure BDA00028533709300000324
for ant k to follow pixel point in the first sub-area
Figure BDA00028533709300000325
To the pixel point
Figure BDA00028533709300000326
The concentration of the pheromone of (a),
Figure BDA00028533709300000327
all ants are driven to pixel points in the first sub-area
Figure BDA00028533709300000328
To the pixel point
Figure BDA00028533709300000329
Q is a constant;
(4.4) when the number of iterations reaches the maximum value, the path search ends, each sub-region GslAnd obtaining a specific crack search path, and then splicing all sub-regions of the obtained specific crack search path to finish the crack extraction of the logging image.
The invention aims to realize the following steps:
the invention relates to an image crack real-time extraction method suitable for ultrasonic imaging logging, which comprises the steps of firstly, carrying out primary segmentation on an original image according to an Otus segmentation algorithm, and extracting effective crack information to establish a search space; then, referring to the crack development form, determining the crack reference position through a voting accumulation mechanism, and properly cutting the area where the crack is located according to the reference position; then, cutting the cut image along the longitudinal direction to obtain each subarea with partial crack information, and performing crack search and extraction on each subarea in parallel by using an ant colony algorithm; and finally splicing the sub-regions searched to obtain the crack path to obtain the complete crack.
Meanwhile, the image crack real-time extraction method suitable for the ultrasonic imaging logging further has the following beneficial effects:
(1) the invention divides the search space into a plurality of sub-areas for parallel path search, so that the algorithm is quickly converged and the search speed is increased.
(2) And because each area is independently ant colony for path search, the situation that the ant colony falls into local optimal search can be effectively improved.
(3) The method has high identification accuracy, and can accurately keep crack information no matter whether the crack is a sine crack, a horizontal crack or a crack with obvious background noise, so that the identification and extraction of the crack area in the logging image are realized.
Drawings
FIG. 1 is a schematic diagram of an ultrasonic imaging tool configuration;
FIG. 2 is a general block diagram of the image fracture real-time extraction method of the present invention suitable for ultrasonic imaging logging;
FIG. 3 is a diagram of an overall architecture suitable for use with an ultrasonic imaging tool;
FIG. 4 is a flow chart of data acquisition for an ultrasonic imaging tool;
FIG. 5 is a flow chart for establishing a search space;
FIG. 6 is a graph of threshold-partitioned binary matrix data;
FIG. 7 is a determination of a baseline position;
FIG. 8 is a target search area diagram;
FIG. 9 is a diagram of vertically divided sub-regions;
FIG. 10 is a plot of a small area path parallel search strategy;
FIG. 11 is a crack searched by the ant colony algorithm;
FIG. 12 is a final identified and extracted fracture map.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
FIG. 2 is a general block diagram of the image fracture real-time extraction method suitable for ultrasonic imaging logging according to the invention.
In this embodiment, as shown in fig. 2, the method for extracting an image fracture in real time, which is suitable for ultrasonic imaging logging, of the present invention includes the following steps:
s1, image segmentation
The FPGA internal architecture of the ultrasonic imaging logging instrument is shown in figure 3, the receiving command is mainly used for processing commands sent by an upper computer and a PIC, the transmitting module is used for controlling an ultrasonic transmitting channel, the collecting and calculating module is used for storing and processing data of the ADC, the EDIB transmitting module is used for transmitting the processed data to the upper computer through M2, M5 and M7, the upper computer is enabled to image, and the modules in the FPGA coordinate through various control signals.
As shown in fig. 4, PIC transmits a mechanical synchronization signal generated by a mechanical structure part sensor of the ultrasonic imaging logging tool to FPGA, and when receiving the tooth signal and the body max rank signal, FPGA transmits a sound wave and generates a transmission flag bit, and then judges whether the transmission reaches 250 times, namely, one week. When the emission reaches 250 times, the flag bit of the full period of emission is output, and the stage of emission of the mud wave is entered.
When the FPGA receives the transmitting zone bit and transmits the zone bit of one full period, the ADC starts to collect according to the set waiting transition time, the collected data is stored in the RAM, and a once-collecting zone bit is generated, when the zone bit of one week of transmission is received, a zone bit for completing the collection is generated, and then the slurry echo starts to be collected.
And when the FPGA receives the once-collected zone bit, the FPGA acquires data from the RAM and starts to calculate to obtain the arrival time and the amplitude of the first wave, and when the once-collected zone bit is received, the FPGA starts to calculate the sound velocity of the slurry wave.
And then constructing the calculated data into matrix data G corresponding to the image and storing the matrix data G into the RAM.
When the data in the RAM is stored to a certain amount, the FPGA on the main control board reads the data in the RAM to construct an 88 x 88 matrix required by the image G, and an inter-class variance evaluation function which is beneficial to simple calculation is adopted
Figure BDA0002853370930000061
As a criterion of threshold segmentation, the specific segmentation steps are shown in fig. 5 as follows:
s1.1, calculating the between-class variance of the image G
Figure BDA0002853370930000062
Figure BDA0002853370930000063
μTPixels with omega (d) of 0 to d are classified into the crack region G as the gray-scale mean of the image G1μ (d) is the mean value of the gray levels of the pixels with gray levels from 0 to d;
in the present embodiment, the inter-class variance is obtained
Figure BDA0002853370930000064
1852.9;
s1.2, solving an optimal segmentation threshold value T*So that the crack region G1And background region G2The sum of the information entropies of (a);
Figure BDA0002853370930000065
in this implementationIn the examples, obtained according to the previous step
Figure BDA0002853370930000066
Finding the optimal threshold T*Is 0.524;
s1.3, all pixel points of the image G are subjected to threshold T*Dividing the image into a crack region G1Setting the corresponding gray value as 0, and classifying the rest pixel points as background areas G2Setting the corresponding gray value as 1, so that the ultrasonic logging image after threshold segmentation is converted into a binary image, as shown in fig. 6;
s1.4, utilizing a sequence number method to divide a crack area G1To form a feasible grid, the background region G2Transforming into an infeasible grid, thereby generating a grid set corresponding to the binary image as a search space, as shown in fig. 5;
s2, passing through base line y0Determines a target search area G in the search spaces；
S2.1, determining a baseline position; by applying the method to the fracture curve y ═ A sin (ω x + β) + y0Up searching point pair s1＝(x1,y1) And s2＝(x2,y2) To satisfy x1-x2Where a is the amplitude of the fracture curve, β is the initial phase, ω is the angular velocity, y is0Is the baseline position, and T is the period of the fracture curve;
then the midpoint s of such a point pair is satisfied0＝(x0,y0) Must fall at base line y0Then, the address information of all the midpoints is counted by using a voting accumulation mechanism, so as to determine the baseline y0The position of (1) is the row where y is 29, i.e. the position marked yellow in fig. 7;
s2.2, comparing the base line y0Cutting off the region to obtain a target search region GsA 22 x 88 matrix is shown in fig. 8;
s3, dividing target search area Gs；
Searching for a target area GsDivided into L different regions in the longitudinal direction, each region being guaranteed during the divisionSubregion GslThe inner crack area is still left-right through, wherein L is 1,2, …, L; in this embodiment, the total division into 4 sub-regions of 22 × 22 size is shown in fig. 9;
s4, simultaneously carrying out comparison on the four sub-regions G by utilizing the ant colony algorithmslPerforming path search to finish real-time extraction of image cracks, as shown in fig. 10;
s4.1, setting the number of ants as K to 20; the number of the pixel points in the first sub-area is n-22 x 22; any two pixel points in the first sub-region
Figure BDA0002853370930000071
And
Figure BDA0002853370930000072
is a distance of
Figure BDA0002853370930000073
And i is not equal to j; any two pixel points in the first sub-region
Figure BDA0002853370930000074
And
Figure BDA0002853370930000075
initial pheromone concentration of
Figure BDA0002853370930000076
Any two pixel points in the first sub-area at the moment t
Figure BDA0002853370930000077
And
Figure BDA0002853370930000078
has a pheromone concentration of
Figure BDA0002853370930000079
Randomly selecting one pixel point at each of the left side and the right side in the first sub-area
Figure BDA00028533709300000710
And
Figure BDA00028533709300000711
as a starting point and an end point of a path search;
s4.2, calculating slave pixel points of the ant k in the first sub-area at the moment t
Figure BDA00028533709300000712
Transfer to pixel
Figure BDA00028533709300000713
Transition probability of
Figure BDA00028533709300000714
Figure BDA00028533709300000715
Wherein the content of the first and second substances,
Figure BDA00028533709300000716
representing the current time t in the first sub-region
Figure BDA00028533709300000717
Transfer to pixel
Figure BDA00028533709300000718
The desired degree of; allkFor ant k, the set of pixel points to be searched, r represents allowkAny one pixel point; a is a pheromone importance factor; b is a heuristic function importance factor;
s4.3, through roulette selection method, ant k starts from the beginning in the first sub-area
Figure BDA00028533709300000719
Starting to go to the next pixel point according to the transfer probability until the destination is reached
Figure BDA00028533709300000720
Then, the pheromone concentration among the traversed pixel points is updated
Figure BDA00028533709300000721
Figure BDA00028533709300000722
Figure BDA00028533709300000723
Wherein rho is the volatilization rate of the pheromone,
Figure BDA00028533709300000724
for ant k to follow pixel point in the first sub-area
Figure BDA00028533709300000725
To the pixel point
Figure BDA00028533709300000726
The concentration of the pheromone of (a),
Figure BDA00028533709300000727
all ants are driven to pixel points in the first sub-area
Figure BDA00028533709300000728
To the pixel point
Figure BDA00028533709300000729
Q is a constant;
s4.4, when the iteration number reaches the maximum value, the path search is finished, and the four sub-regions GslA specific crack search path is obtained as shown in fig. 11, and then each sub-area of the obtained specific crack search path is divided into sub-areasAnd (5) splicing the domains to finish the fracture extraction of the logging image as shown in figure 12.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (1)

1. An image crack real-time extraction method suitable for ultrasonic imaging logging is characterized by comprising the following steps:
(1) performing primary threshold segmentation on the original ultrasonic well logging image G by using an Otsu segmentation algorithm;
(1.1) calculating the between-class variance of the image G
Figure FDA0002853370920000011
Figure FDA0002853370920000012
μTThe pixels with the gray scale of 0 to d are classified into the crack region G as the gray scale mean of the image G and ω (d)1μ (d) is the mean value of the gray levels of the pixels with gray levels from 0 to d;
(1.2) obtaining an optimal segmentation threshold T*So that the crack region G1And background region G2The sum of the information entropies of (a);
Figure FDA0002853370920000013
(1.3) all pixel points of the image G are subjected to threshold value T*Dividing the image into a crack region G1To, forThe corresponding gray value is set to be 0, and the rest pixel points are classified as a background area G2The corresponding gray value is set to 1;
(1.4) formation of crack region G by the number method1To form a feasible grid, the background region G2Converting into an infeasible grid, thereby generating a grid set corresponding to the binary image as a search space;
(2) through the base line y0Determines a target search area G in the search spaces；
(2.1) determining a baseline position; by applying the crack curve y ═ Asin (ω x + β) + y0Up searching point pair s1＝(x1,y1) And s2＝(x2,y2) To satisfy x1-x2Where a is the amplitude of the fracture curve, β is the initial phase, ω is the angular velocity, y is0Is the baseline position, and T is the period of the fracture curve;
then the midpoint s of such a point pair is satisfied0＝(x0,y0) Must fall at base line y0Then, the vertical coordinate information of all the midpoints is counted by utilizing a voting accumulation mechanism, so that the base line y is determined0The position of (a);
(2.2) taking the base line y0Cutting off the region to obtain a target search region Gs；
(3) Dividing the target search region Gs；
Searching for a target area GsDividing the substrate into L different regions along the longitudinal direction, and ensuring each sub-region G in the dividing processslThe inner crack area is still left-right through, wherein L is 1,2, …, L;
(4) simultaneously aiming each subarea G by utilizing ant colony algorithmslPerforming path search to finish real-time extraction of image cracks;
(4.1) setting the number of ant colony ants as K; the number of pixel points in the l sub-area is n; any two pixel points V in the first sub-areai lAnd
Figure FDA0002853370920000021
in betweenA distance of
Figure FDA0002853370920000022
i,j∈[1,n]And i is not equal to j; any two pixel points V in the first sub-areai lAnd
Figure FDA0002853370920000023
initial pheromone concentration of
Figure FDA0002853370920000024
Any two pixel points V in the first sub-area at the moment ti lAnd
Figure FDA0002853370920000025
has a pheromone concentration of
Figure FDA0002853370920000026
Randomly selecting one pixel point at each of the left side and the right side in the first sub-area
Figure FDA0002853370920000027
And
Figure FDA0002853370920000028
as a starting point and an end point of a path search;
(4.2) calculating the slave pixel point V of the ant k in the ith sub-area at the moment ti lTransfer to pixel
Figure FDA0002853370920000029
Transition probability of
Figure FDA00028533709200000210
Figure FDA00028533709200000211
Wherein the content of the first and second substances,
Figure FDA00028533709200000212
represents the current time t in the first sub-area from the pixel point Vi lTransfer to pixel
Figure FDA00028533709200000213
The desired degree of; allkFor ant k, the set of pixel points to be searched, r represents allowkAny one pixel point; a is a pheromone importance factor; b is a heuristic function importance factor;
(4.3) Ant k from the beginning in the first subregion by roulette selection
Figure FDA00028533709200000214
Starting to go to the next pixel point according to the transfer probability until the destination is reached
Figure FDA00028533709200000215
Then, the pheromone concentration among the traversed pixel points is updated
Figure FDA00028533709200000216
Figure FDA00028533709200000217
Figure FDA00028533709200000218
Wherein rho is the volatilization rate of the pheromone,
Figure FDA00028533709200000219
for ant k in the first sub-area from pixel point Vi lTo the pixel point
Figure FDA00028533709200000220
The concentration of the pheromone of (a),
Figure FDA00028533709200000221
all ants are within the sub-area from pixel point Vi lTo the pixel point
Figure FDA00028533709200000222
(ii) cumulative pheromone concentration;
(4.4) when the number of iterations reaches the maximum value, the path search ends, each sub-region GslAnd obtaining a specific crack search path, and then splicing all sub-regions of the obtained specific crack search path to finish the crack extraction of the logging image.
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