CN108133474B - Permeability prediction method based on core sample two-dimensional pore image - Google Patents
Permeability prediction method based on core sample two-dimensional pore image Download PDFInfo
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Abstract
The permeability prediction method based on the two-dimensional pore image of the core sample comprises the following steps of firstly, preprocessing the two-dimensional pore image of the core sample; binaryzation of a two-dimensional pore image of the core sample; thirdly, counting the number of pores in the two-dimensional pore image of the core sample and the area of each pore; step four: solving the equivalent radius of all pores in the two-dimensional pore image of the rock core sample; step five: obtaining the pore conductivity coefficient in the two-dimensional pore image of the core sample; step six: predicting the permeability based on the two-dimensional pore image of the core sample; step seven, calculating the permeability of the core sample; the method is based on the computer image processing technology, organically combines the two-dimensional pore image of the core sample with the permeability prediction, reduces the economic cost of predicting the permeability of the core sample by using a physical experiment, shortens the prediction time of the permeability of the core sample, and can improve the accuracy of the permeability prediction to a certain extent.
Description
Technical Field
The invention belongs to the technology of petroleum and natural gas exploration and development, and particularly relates to a method for predicting the permeability of a core sample through a two-dimensional pore image of the core sample.
Background
Permeability is a reservoir parameter of major concern to oilfield developers. This is because permeability is an important criterion in determining whether a well is completed and put into production; meanwhile, the method is also an important reference standard established by oil layer protection during well drilling, perforation scheme selection during well completion, optimal liquid discharge position determination, production rate and tertiary oil recovery measures. Permeability has been determined in the past primarily by physical experiments. And the accuracy of the exploitation scheme made by oil field developers can be directly determined by the coincidence degree of the permeability value determined by physical experiments and the actual formation permeability. However, due to the complexity of the rock structure of the underground reservoir, the permeability of the underground reservoir often shows nonlinear characteristics, so that the conventional research is difficult to obtain an accurate analytic solution of the permeability; the economic cost for carrying out physical experiments is high, the consumed time is long, and the permeability characteristics of certain micro-pores of underground reservoir rock are difficult to quantitatively characterize. In view of this, it is urgent to provide new technologies and methods for predicting the permeability of rock in underground reservoirs. The new technologies and methods are required to improve the accuracy of the rock permeability prediction of the underground reservoir, shorten the time consumed by the prediction process and reduce the investment.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a permeability prediction method based on a two-dimensional pore image of a core sample, wherein the two-dimensional pore image can be used as the most basic input to predict the permeability of the corresponding core sample as long as pores and roars can be separated from surrounding rock particles, and the permeability value of the core sample is predicted by counting the number of pores and the area of each pore in the two-dimensional pore image of the core sample.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the permeability prediction method based on the core sample two-dimensional pore image comprises the following steps:
step one, preprocessing a two-dimensional pore image of a rock core sample: cutting a two-dimensional pore image of an existing rock core sample by using Photoshop software to obtain an effective research area of pores and a throat, converting all non-bmp format images into bmp format gray level images, and then removing noise and enhancing contrast by using a wavelet transform technology;
step two, binaryzation of a two-dimensional pore image of the core sample: carrying out binarization processing on the two-dimensional pore image of the core sample by adopting an iterative threshold method, wherein the whole two-dimensional pore image of the core sample consists of two colors: black and white, wherein the black represents pores and roars in the two-dimensional pore image of the core sample, and the white represents rock particles around the pores and roars;
thirdly, counting the number of pores in the two-dimensional pore image of the core sample and the area of each pore: firstly, the definition of pores and throat in a two-dimensional pore image of a core sample after binarization is given, on the basis, the throat in the two-dimensional pore image of the core sample is eliminated by utilizing image corrosion operation, then, the pore profile in the two-dimensional pore image of the core sample is tracked by a profile tracking algorithm, and the number num of pores in the two-dimensional pore image is countedpore(ii) a Finally, calculating the area of each pore by using a scanning line seed filling algorithm;
step four: solving the equivalent radius of all pores in the two-dimensional pore image of the core sample: the radius of each pore in the two-dimensional pore image of the core sample is obtained by adopting an equivalent circle method,namely: and (3) equating the area of each pore in the two-dimensional pore image of the rock core sample obtained by the third step into the area of a circle, and then calculating the formula S ═ pi r according to the area of the circle2Solving the equivalent radius r of each pore;
step five, solving the pore conductivity coefficient C of the two-dimensional pore image of the rock core sample: the pore conductivity coefficient C of the two-dimensional pore image of the core sample is obtained by using a formula (1), wherein the specific formula is as follows:
in the above formula, N is the total number of pores in the two-dimensional pore image of the core sample, and its value is equal to num in step threeporeI is the number of the pores in the two-dimensional pore image of the rock core sample, ciThe conductivity coefficient r of the ith pore in the two-dimensional pore image of the core sampleiThe equivalent radius of the ith pore in the two-dimensional pore image of the core sample obtained in the fourth step is calculated;
and step six, predicting the permeability based on the two-dimensional pore image of the rock core sample: the permeability prediction formula based on the two-dimensional pore image of the core sample is as follows:
in the above formula, k is a permeability value of the core sample at the cross section corresponding to the two-dimensional pore image of the core sample, C is a pore conductivity coefficient of the two-dimensional pore image of the core sample, and a is an area of the two-dimensional pore image of the core sample;
step seven, calculating the permeability of the core sample: the above steps are only to predict the permeability of the core sample at a certain section, calculate the permeability of the whole core sample, process the two-dimensional pore images of a plurality of core samples to obtain the predicted values of a plurality of permeabilities, then take the maximum value, the minimum value, the average value, the standard deviation and the like of the permeability, and represent the permeability of the whole core sample according to the parameters.
The third step is specifically as follows:
(1) definition of pores and roar in two-dimensional pore images of core samples: a space surrounded by three or more rock particles in a two-dimensional pore image of the core sample is called a pore, and a minimum channel connecting two adjacent pores is called a throat, namely a space between two rock particles;
(2) carrying out corrosion operation on the two-dimensional pore image of the core sample to eliminate a throat in the two-dimensional pore image;
(3) the specific process of counting the number of pores in the two-dimensional pore image of the rock core sample based on the contour tracing algorithm is as follows:
firstly, scanning a two-dimensional pore image of a rock core sample from left to right and from top to bottom, and searching black pixel points;
after the first black pixel point is obtained, the first black pixel point is used as a starting point of the pore contour tracking, the reverse direction of the entering direction of the first black pixel point is used as an initial direction chain code, and after the tracking starting point and the initial direction chain code are determined, the tracking of the whole pore contour is completed;
thirdly, after the first pore contour is tracked, the parameter num is setporeIs set to be 0 and is used for representing the total number of pores in the two-dimensional pore image of the rock core sample and the parameter numporeAdds 1 to the value of (f) and fills the hole with the colors RGB ((255-1), 255), representing the colors of the different holes with an RGB color pattern that reproduces 16777216 different colors on a computer screen;
fourthly, the two-dimensional pore image of the rock core sample is continuously scanned from left to right and from top to bottom to obtain a second black pixel point which is used as a starting point of second pore tracking, and the reverse direction of the entering direction of the point is used as a starting direction chain code, so that the starting point and the starting direction chain code of the second pore tracking are determined, the tracking of the whole outline of the pore is completed on the basis, and the parameter num is usedporePlus 1, the pore is filled with the color RGB ((255-2), 255);
fifthly, the two-dimensional pore images of the rock core sample are continuously processed from left to right and from top to bottomDetermining the starting point and the starting direction chain code of the ith pore space tracking, completing the tracking of the whole outline of the pore space, and calculating the parameter numporePlus 1 and filling the pore with the color RGB ((255-i), 255);
sixthly, scanning the two-dimensional pore image of the core sample from left to right and from top to bottom to finish contour tracking, color filling and number statistics of all pores in the two-dimensional pore image of the core sample, and finally realizing that different pores are expressed by different colors; using numporeThe final value of (a) represents the total number of pores in the two-dimensional pore image of the core sample;
(4) and calculating the area of each pore in the two-dimensional pore image of the core sample based on the scanning line seed filling algorithm.
The specific process for calculating the area of each pore in the two-dimensional pore image of the rock core sample based on the scanning line seed filling algorithm is as follows:
firstly, scanning a two-dimensional pore image of a rock core sample from left to right and from top to bottom to find a first non-white point (x)first,yfirst) Initializing an empty stack and putting the point on the stack;
judging whether the stack is empty, if so, ending the circulation, otherwise, taking out the top element of the stack to be used as the seed point (x, y) of the current scanning line, wherein x is xfirstY is yfirstY is the current scan line; starting from the seed point (x, y), filling to the boundary along the left and right directions of the current scanning line, and respectively marking the end points of the left and right boundaries of the filled section as xleftAnd xrightRespectively checking the interval of two adjacent scanning lines, namely y-1 and y +1
Pixel of (2) from xleftStarting towards xrightDirection searching, if non-boundary and unfilled pixel points exist, finding out the rightmost one of the adjacent pixel points, pressing the rightmost one as a seed point into a stack, and repeating the filling process;
③ in the filling process, use the parameter number1The total number of all the filled pixels is represented, and all the non-white pixel points in the filling area are converted into white pixel points after filling is finished;
continuously scanning the two-dimensional pore image of the rock core sample from left to right and from top to bottom, and recording the total number of all pixels in the scanned second filling area as number2The total number of all pixels in the N-th scanned filling area is recorded as numberN；
Fifthly, multiplying the area of a single pixel by the number1The area of the first aperture scanned, multiplied by the number by the area of the single pixel, is obtainedNThe area of the scanned nth pore can be obtained, and the area of all pores in the two-dimensional pore image of the core sample can be obtained.
The method combines the pixel processing of the two-dimensional pore gray level image of the rock core sample with the permeability prediction of the rock core sample based on the computer image processing technology, can improve the accuracy of the permeability prediction to a certain extent, reduces the economic cost consumed by predicting the permeability through a physical experiment, and shortens the time required by the permeability prediction.
Drawings
Fig. 1 is a flow chart of permeability prediction based on a two-dimensional pore image of a core sample according to the present invention.
FIG. 2 is a flow chart of the statistics of the number of pores in a two-dimensional pore image of a core sample based on a contour tracing algorithm in the present invention.
Fig. 3 is a flowchart for calculating the area of each pore in a two-dimensional pore image of a core sample based on a scan line seed filling algorithm according to the present invention.
Detailed Description
The technical scheme of the invention is described in detail in the following with reference to the accompanying drawings.
Referring to fig. 1, a permeability prediction method based on a two-dimensional pore image of a core sample comprises the following steps:
step one, preprocessing a two-dimensional pore image of a rock core sample: processing a two-dimensional pore image of an existing core sample by using Photoshop software, deleting an invalid region, obtaining an effective research region of pores and roars, and converting all images in a non-bmp format into a bmp format. Then converting the two-dimensional pore image of the color core sample into a gray image (if the image is the gray image, the conversion is not needed), and finally removing the noise by using a wavelet transform technology to enhance the contrast;
step two, binaryzation of a two-dimensional pore image of the core sample: carrying out binarization processing on the two-dimensional pore image of the rock core sample by using an iterative threshold method, and specifically comprising the following steps: firstly, calculating the arithmetic mean value Z of the gray scale of the two-dimensional pore image of the whole rock core sampleaverageIt is set to the initial threshold, i.e. T0＝Zaverage(ii) a According to threshold value Tk(k is 0,1,2,3, …) dividing the two-dimensional pore image of the core sample into two parts of a pore throat and rock particles, representing the pore throat and the throat in black and the rock particles in white, and respectively finding average gray values Z of the two partsporeAnd Zrock(ii) a ③ make Tk+1＝(Zpore+Zrock) 2(k ═ 0,1,2,3, …); if Tk＝Tk+1(k is 0,1,2,3, …), stopping iteration, if not, making k +1, and repeating (c) - (d); calculating final threshold value T by the iterative calculationfinalThen all gray levels are greater than TfinalResetting the gray value of the pixel point to 1, and making all gray values less than TfinalThe gray value of the pixel point is reset to be 0, so that binaryzation of the two-dimensional pore image of the rock core sample is completed;
thirdly, counting the number of pores in the two-dimensional pore image of the core sample and the area of each pore: firstly, the definition of pores and throat in a two-dimensional pore image of a core sample after binarization is given, then the throat in the two-dimensional pore image of the core sample is eliminated by using image corrosion operation (all discussion processes in the invention are operated and executed in the 3 × 3 field of the image, and the description is not repeated later), then the pore contour in the two-dimensional pore image of the core sample is tracked by using a contour tracking algorithm, and the number num of the pores in the image is countedpore(ii) a And finally, calculating the area of each pore by using a scanning line seed filling algorithm. The method comprises the following specific steps: (1) in two-dimensional pore image of core sampleDefinition of pore space and throat: a space surrounded by three or more rock particles in a two-dimensional pore image of the core sample is called a pore, and a minimum channel connecting two adjacent pores is called a throat, namely a space between two rock particles; (2) carrying out corrosion operation on the two-dimensional pore image of the core sample to eliminate a throat in the two-dimensional pore image; (3) the basic process of counting the number of pores in the two-dimensional pore image of the rock core sample based on the contour tracing algorithm is shown in figure 2, and the specific process is as follows: firstly, scanning a two-dimensional pore image of a rock core sample from left to right and from top to bottom, and searching black pixel points; after the first black pixel point is obtained, the first black pixel point is used as a starting point of the pore contour tracking, the reverse direction of the entering direction of the first black pixel point is used as an initial direction chain code, and after the tracking starting point and the initial direction chain code are determined, the tracking of the whole pore contour is completed; thirdly, after the first pore contour is tracked, the parameter num is setpore(initial value was set to 0 to represent the total number of pores in the two-dimensional pore image of the core sample) plus 1 and the pores were filled with the colors RGB ((255-1),255,255) (in this patent the colors of the different pores are represented by RGB color patterns which reproduce 16777216 different colors on a computer screen and are used completely here); fourthly, the two-dimensional pore image of the rock core sample is continuously scanned from left to right and from top to bottom to obtain a second black pixel point which is used as a starting point of second pore tracking, and the reverse direction of the entering direction of the point is used as a starting direction chain code, so that the starting point and the starting direction chain code of the second pore tracking are determined, the tracking of the whole outline of the pore is completed on the basis, and the parameter num is usedporePlus 1, the pore is filled with the color RGB ((255-2), 255); fifthly, continuously scanning the two-dimensional pore image of the rock core sample from left to right and from top to bottom, determining the initial point and the initial direction chain code of the ith pore tracking, completing the tracking of the whole outline of the pore, and setting the parameter numporePlus 1 and filling the pore with the color RGB ((255-i), 255); sixthly, scanning the two-dimensional pore image of the core sample from left to right and from top to bottom to finish all pores in the two-dimensional pore image of the core sampleContour tracing and color filling (different colors are used to finally realize different apertures) and number statistics (num is used to realize different apertures)poreRepresents the total number of pores in the two-dimensional pore image of the core sample); (4) the basic flow of calculating the area of each pore in the two-dimensional pore image of the rock core sample based on the scanning line seed filling algorithm is shown in figure 3, and the specific process is as follows: firstly, scanning a two-dimensional pore image of a rock core sample from left to right and from top to bottom to find a first non-white point (x)first,yfirst) Initializing an empty stack and putting the point on the stack; judging whether the stack is empty, if so, ending the circulation, otherwise, taking out the top element of the stack to be used as the seed point (x, y) of the current scanning line (where x is x)firstY is yfirst) And y is the current scan line. Starting from the seed point (x, y), filling to the boundary along the left and right directions of the current scanning line, and respectively marking the end points of the left and right boundaries of the filled section as xleftAnd xrightRespectively checking the interval of two adjacent scanning lines, namely y-1 and y +1
Pixel of (2) from xleftStarting towards xrightDirection searching, if non-boundary and unfilled pixel points exist, finding out the rightmost one of the adjacent pixel points, pressing the rightmost one as a seed point into a stack, and repeating the filling process; ③ in the filling process, the number of the parameters is used1The total number of all the filled pixels is represented, and all the non-white pixel points in the filling area are converted into white pixel points after filling is finished; continuously scanning the two-dimensional pore image of the rock core sample from left to right and from top to bottom, and recording the total number of all pixels in the scanned second filling area as number2The total number of all pixels in the N-th scanned filling area is recorded as numberN(ii) a Fifthly, multiplying the area of a single pixel by the number1The area of the first aperture scanned, multiplied by the number by the area of the single pixel, is obtainedNThe area of the nth aperture scanned is obtained, and thus, the area of the nth aperture scanned is obtainedThe area of all pores in the two-dimensional pore image of the core sample;
step four: solving the equivalent radius of all pores in the two-dimensional pore image of the core sample: the equivalent radius of each pore in the two-dimensional pore image of the core sample is obtained by adopting an equivalent circle method, namely: the area of each pore in the two-dimensional pore image of the core sample obtained by the previous calculation is equivalent to the area of a circle, and the formula S ═ pi r is calculated according to the area of the circle2Solving the equivalent radius r of each pore;
step five, solving the pore conductivity coefficient C of the two-dimensional pore image of the rock core sample: the pore conductivity coefficient C of the two-dimensional pore image of the core sample is obtained by using a formula (1), wherein the specific formula is as follows:
in the above formula, N is the total number of pores in the two-dimensional pore image of the core sample, and its value is equal to num in step threeporeI is the number of the pores in the two-dimensional pore image of the rock core sample, ciThe conductivity coefficient r of the ith pore in the two-dimensional pore image of the core sampleiThe equivalent radius of the ith pore in the two-dimensional pore image of the core sample obtained in the fourth step is calculated;
and step six, predicting the permeability based on the two-dimensional pore image of the rock core sample: mathieu et al (2007) proposed a permeability prediction formula based on two-dimensional pore images of core samples, as follows:
in the above formula, k is the permeability value of the core sample at the cross section corresponding to the two-dimensional pore image of the core sample, N is the total number of pores in the two-dimensional pore image of the core sample, a is the area of the two-dimensional pore image of the core sample, and ceffThe conductivity of individual pores in the two-dimensional pore image of the core sample was calculated. However, Mathieu et al do not give ceffThe detailed solution of the formula (a) is,therefore, in the invention, the pore conductivity coefficient C of the two-dimensional pore image of the core sample obtained in the fifth step is adopted to replace NceffThis results in a new permeability prediction equation (3), shown below:
in the above formula, k is a permeability value of the core sample at the cross section corresponding to the two-dimensional pore image of the core sample, C is a pore conductivity coefficient of the two-dimensional pore image of the core sample, and a is an area of the two-dimensional pore image of the core sample;
step seven, calculating the permeability of the core sample: the above steps are only to predict the permeability value of the core sample at a certain section, calculate the permeability of the whole core sample, process the two-dimensional pore images of a plurality of core samples to obtain the predicted values of a plurality of permeabilities, then take the maximum value, the minimum value, the average value, the standard deviation and the like of the permeability values, and represent the permeability of the whole core sample through the parameters.
Claims (3)
1. The permeability prediction method based on the core sample two-dimensional pore image is characterized by comprising the following steps of:
step one, preprocessing a two-dimensional pore image of a rock core sample: cutting a two-dimensional pore image of an existing rock core sample by using Photoshop software to obtain an effective research area of pores and a throat, converting all non-bmp format images into bmp format gray level images, and then removing noise and enhancing contrast by using a wavelet transform technology;
step two, binaryzation of a two-dimensional pore image of the core sample: carrying out binarization processing on the two-dimensional pore image of the core sample by adopting an iterative threshold method, wherein the whole two-dimensional pore image of the core sample consists of two colors: black and white, wherein the black represents pores and roars in the two-dimensional pore image of the core sample, and the white represents rock particles around the pores and roars;
step three, in the two-dimensional pore image of the rock core sampleStatistics of the number of pores and the area of each pore: firstly, the definition of pores and throat in a two-dimensional pore image of a core sample after binarization is given, on the basis, the throat in the two-dimensional pore image of the core sample is eliminated by utilizing image corrosion operation, then, the pore profile in the two-dimensional pore image of the core sample is tracked by a profile tracking algorithm, and the number num of pores in the two-dimensional pore image is countedpore(ii) a Finally, calculating the area of each pore by using a scanning line seed filling algorithm;
step four: solving the equivalent radius of all pores in the two-dimensional pore image of the core sample: the radius of each pore in the two-dimensional pore image of the core sample is obtained by adopting an equivalent circle method, namely: and (3) equating the area of each pore in the two-dimensional pore image of the rock core sample obtained by the third step into the area of a circle, and then calculating the formula S ═ pi r according to the area of the circle2Solving the equivalent radius r of each pore;
step five, solving the pore conductivity coefficient C of the two-dimensional pore image of the rock core sample: the pore conductivity coefficient C of the two-dimensional pore image of the core sample is obtained by using a formula (1), wherein the specific formula is as follows:
in the above formula, N is the total number of pores in the two-dimensional pore image of the core sample, and its value is equal to num in step threeporeI is the number of the pores in the two-dimensional pore image of the rock core sample, ciThe conductivity coefficient r of the ith pore in the two-dimensional pore image of the core sampleiThe equivalent radius of the ith pore in the two-dimensional pore image of the core sample obtained in the fourth step is calculated;
and step six, predicting the permeability based on the two-dimensional pore image of the rock core sample: the permeability prediction formula based on the two-dimensional pore image of the core sample is as follows:
in the formula, k is a permeability value of the core sample at the section corresponding to the two-dimensional pore image of the core sample, C is a pore conductivity coefficient of the two-dimensional pore image of the core sample, and A is an area of the two-dimensional pore image of the core sample;
step seven, calculating the permeability of the core sample: the above steps are only to predict the permeability of the core sample at a certain section, calculate the permeability of the whole core sample, process the two-dimensional pore images of a plurality of core samples to obtain the predicted values of a plurality of permeabilities, then take the maximum value, the minimum value, the average value and the standard deviation of the predicted values, and represent the permeability of the whole core sample through the parameters.
2. The permeability prediction method based on the core sample two-dimensional pore image as claimed in claim 1, wherein the third step is specifically as follows:
(1) definition of pores and roar in two-dimensional pore images of core samples: a space surrounded by three or more rock particles in a two-dimensional pore image of the core sample is called a pore, and a minimum channel connecting two adjacent pores is called a throat, namely a space between two rock particles;
(2) carrying out corrosion operation on the two-dimensional pore image of the core sample to eliminate a throat in the two-dimensional pore image;
(3) the specific process of counting the number of pores in the two-dimensional pore image of the rock core sample based on the contour tracing algorithm is as follows:
firstly, scanning a two-dimensional pore image of a rock core sample from left to right and from top to bottom, and searching black pixel points;
after the first black pixel point is obtained, the first black pixel point is used as a starting point of the pore contour tracking, the reverse direction of the entering direction of the first black pixel point is used as an initial direction chain code, and after the tracking starting point and the initial direction chain code are determined, the tracking of the whole pore contour is completed;
thirdly, after the first pore contour is tracked, the parameter num is setporeIs set to be 0 and is used for representing the total number of pores in the two-dimensional pore image of the rock core sample and a parameter numporeAdds 1 to the value of (f) and fills the hole with the colors RGB ((255-1), 255), representing the colors of the different holes with an RGB color pattern that reproduces 16777216 different colors on a computer screen;
fourthly, the two-dimensional pore image of the rock core sample is continuously scanned from left to right and from top to bottom to obtain a second black pixel point which is used as a starting point of second pore tracking, and the reverse direction of the entering direction of the point is used as a starting direction chain code, so that the starting point and the starting direction chain code of the second pore tracking are determined, the tracking of the whole outline of the pore is completed on the basis, and the parameter num is usedporePlus 1, the pore is filled with the color RGB ((255-2), 255);
fifthly, continuously scanning the two-dimensional pore image of the rock core sample from left to right and from top to bottom, determining the initial point and the initial direction chain code of the ith pore tracking, completing the tracking of the whole outline of the pore, and setting the parameter numporePlus 1 and filling the pore with the color RGB ((255-i), 255);
sixthly, scanning the two-dimensional pore image of the core sample from left to right and from top to bottom to finish contour tracking, color filling and number statistics of all pores in the two-dimensional pore image of the core sample, and finally realizing that different pores are expressed by different colors; using numporeThe final value of (a) represents the total number of pores in the two-dimensional pore image of the core sample;
(4) and calculating the area of each pore in the two-dimensional pore image of the core sample based on the scanning line seed filling algorithm.
3. The permeability prediction method based on the core sample two-dimensional pore image as recited in claim 2, wherein the specific process for calculating the area of each pore in the core sample two-dimensional pore image based on the scan line seed filling algorithm is as follows:
firstly, scanning a two-dimensional pore image of a rock core sample from left to right and from top to bottom to find a first non-white point (x)first,yfirst) Initializing an empty stack and stacking the point；
Judging whether the stack is empty, if so, ending the circulation, otherwise, taking out the top element of the stack to be used as the seed point (x, y) of the current scanning line, wherein x is xfirstY is yfirstY is the current scan line; starting from the seed point (x, y), filling to the boundary along the left and right directions of the current scanning line, and respectively marking the end points of the left and right boundaries of the filled section as xleftAnd xrightRespectively checking the interval of two adjacent scanning lines, namely y-1 and y +1
Pixel of (2) from xleftStarting towards xrightDirection searching, if non-boundary and unfilled pixel points exist, finding out the rightmost one of the adjacent pixel points, pressing the rightmost one as a seed point into a stack, and repeating the filling process;
③ in the filling process, the number of the parameters is used1The total number of all the filled pixels is represented, and all the non-white pixel points in the filling area are converted into white pixel points after filling is finished;
continuously scanning the two-dimensional pore image of the rock core sample from left to right and from top to bottom, and recording the total number of all pixels in the scanned second filling area as number2The total number of all pixels in the N-th scanned filling area is recorded as numberN；
Fifthly, multiplying the area of a single pixel by the number1The area of the first aperture scanned, multiplied by the number by the area of the single pixel, is obtainedNThe area of the scanned nth pore can be obtained, and the area of all pores in the two-dimensional pore image of the core sample can be obtained.
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| CN109191423B (en) * | 2018-07-18 | 2019-07-02 | 中国矿业大学 | A kind of porous media Permeability Prediction method based on machine image intelligence learning |
| CN109374624B (en) * | 2018-12-12 | 2020-11-06 | 山东大学 | Method and system for detecting blockage of permeable pavement |
| CN110222368B (en) * | 2019-08-02 | 2021-09-17 | 中国石油大学(华东) | Method for calculating three-dimensional porosity and permeability of rock core by using two-dimensional slice |
| CN110865013B (en) * | 2019-11-20 | 2021-05-11 | 成都理工大学 | Method for constructing dual coal rock pore structure model |
| CN113029899B (en) * | 2021-02-24 | 2022-07-01 | 西南石油大学 | Sandstone permeability calculation method based on microscopic image processing |
| CN116342541B (en) * | 2023-03-29 | 2024-03-22 | 中国矿业大学 | Rock-soil body permeability calculation method based on adjacent image pore fusion reconstruction |
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