CN107133630A - A kind of method that carbonate porosity type is judged based on scan image - Google Patents

A kind of method that carbonate porosity type is judged based on scan image Download PDF

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CN107133630A
CN107133630A CN201610112618.6A CN201610112618A CN107133630A CN 107133630 A CN107133630 A CN 107133630A CN 201610112618 A CN201610112618 A CN 201610112618A CN 107133630 A CN107133630 A CN 107133630A
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pore
mrow
image
porosity
carbonate
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CN107133630B (en
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廉培庆
高文彬
高慧梅
汤翔
谭学群
王付勇
张俊法
李宜强
高敏
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China Petroleum and Chemical Corp
Sinopec Exploration and Production Research Institute
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Sinopec Exploration and Production Research Institute
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Abstract

Judge carbonate porosity type method the invention discloses a kind of scan image.This method includes：The Core Scanning Image of carbonate rock is carried out including the colored pretreatment for turning gray scale and improving signal to noise ratio, pretreated image is split, reach the effect for distinguishing pore region and matrix areas, on separation hole region base, Pore genesis is extracted by the processing including committed steps such as Morphological scale-space, skeleton refinement and Euclidean distance figures, and dissolution pore, crack are identified, Pore classification coefficient is calculated after enough Pore genesis are obtained, and then judge rock core porosity type, provide understanding for the exploitation of follow-up carbonate reservoir.In addition, the present invention can also obtain the important informations such as distribution situation, development degree and the shape facility of carbonate rock rock core hole in bulk, provide the foundation parameter to Hydrocarbon Migration and oil and gas flow law study.

Description

A kind of method that carbonate porosity type is judged based on scan image
Technical field
The present invention relates to technical field of geological exploration, more particularly to a kind of method that carbonate porosity type is judged based on scan image.
Background technology
Carbonate rock is by the joint effect of many factors such as lithofacies palaeogeography, sedimentary and tectonic evolution or metharmosis, and with increasingly complex reservoir space and oil and gas flow migration rule, this causes the risk and difficulty of Carbonate Reservoir exploitation larger.Accordingly, for the development mode and development plan of correct selection carbonate rock, judge that the type of carbonate reservoir is particularly important.
Judge that the traditional mode of Types of Carbonate Reservoir focuses on to protrude sedimentary origin and hydrodynamics energy, generally using rock forming mineral grain skeleton as main classification foundation.This causes the research of law of hydrocarbon migration to be difficult to apply in this sorting technique.In a kind of common carbonate porosity genre classification methods, carbonate porosity is divided into fabric selectivity hole (including intergranular pore, intragranular hole, intracrystalline pore and mould pore etc.), non-fabric selectivity hole (including crack, lapies, dissolution pore and dissolution pore etc.) and intermediateness fabric selectivity hole (dust hole, dwelling burrow and shrinkage hole) by the key of " fabric selectivity ".This method obtains widely applying (Choquette PW during oil exploration and exploitation, Pray LC.Geological nomenclature and classification of porosity in sedimentary carbonates.AAPG Bulletin, 1970, the phase of volume 54 2,207-250).And consider that carbonate porosity space is mainly divided into intergranular pore (including inter-granular porosity and intercrystalline pore) and dissolution pore by the sorting technique of the relation between petrofabric and interstitial space, wherein intergranular pore is respectively divided into hole between coarse granule according to the size of particle, in-powder crystal intergranular pores and powder-mud crystallite hole, dissolution pore is divided into isolated dissolution pore (including mould pore according to connectedness, intragranular hole, transgranular hole and fossil endoporus) and contact dissolution pore (crack, the crack that corrosion expands, dissolution pore, dust hole) (Lucia FJ.Petrophysical parameter estimated from visual description of carbonate rocks:A field classification of pore space, Journal of Petroleum Technology, the phase of volume 1983,35 the 3rd, 626-637).
Constant speed pressure mercury experiment enters mercury by extremely low constant speed into rock sample venturi and hole, it ensure that into mercury process and carried out under quasistatic, this method can separate venturi and hole, realize accurate measurement (Li Shan, Sun Wei, Wang Li etc. of venturi and hole quantity and size, constant speed presses application of the mercury technology in RESERVOIR PORE STRUCTURE research, fault-blcok oil-gas field, 2013, the phase of volume 20 the 4th, 485-487).In recent years, the extensive use with nuclear magnetic resonance in core analysis, is distributed by rock core nuclear magnetic resonance T 2 spectrum and describes the new method of its distribution of pores and pore microgeometrical parameters as rock core Physical Property Analysis.Contrasted by intrusive mercury curve and T2 Spectral structures phase, conversion coefficient (Wang Sheng, with nuclear magnetic resonance spectroscopy rock pore structure feature, Xinjiang petroleum geology, 2009,30 (6) therebetween can be obtained:768-770).In addition, patent《A kind of method and device for obtaining carbonate rock rock core Porous Characteristic parameter》(A of CN 103325118) provides a kind of method for utilizing and drilling through hole geologic parameter on core image acquisition full-hole core yardstick, this method carries out feature extraction and the analysis of macro and micro to carbonate rock core surface hole, can provide good technique for the prediction of reservoir distribution situation and support.
In porosity type sorting technique described above, it is considered to organize the sorting technique of structure mainly or by naked eyes identification and micro-judgment, fail to set up the standards system that quantitative assessment is carried out to carbonate porosity type.Mercury injection method, which is relied primarily on into mercury saturation degree, judges pore-size distribution, it is impossible to intuitively observe various porosity type distribution characteristics.Nuclear-magnetism method image taking speed is slow, expensive, it is difficult to which the rock core for batch is scanned.And it is related to the correlation technique of image procossing in carbonate rock research process, the parameter of quantitative calculating hole and hole is primarily used to, the identification problem in crack is not related to.
The content of the invention
Quantitatively characterizing is carried out to carbonate rock rock core hole based on CT scan image and the method for carbonate porosity type is judged in view of the above-mentioned problems, present invention proposition is a kind of.This method quantitatively characterizes the developmental state in total rock heart porosity type (hole, hole, crack) using Pore genesis, the traditional mode for relying on researcher's micro-judgment carbonate porosity type in the prior art has been broken away from, has judged that reservoir core porosity type provides technical support for batch.
A kind of method that carbonate porosity type is judged based on scan image, is comprised the following steps：
S100, carries out including the colored pretreatment for turning gray scale and improving signal to noise ratio to the Core Scanning Image of carbonate rock；
S200, splits to the image after step S100 processing, reaches the effect of the pore region and matrix areas distinguished in image；
S300, the pore region isolated to step S200 extracts Pore genesis, and recognizes the crack in pore region and dissolution pore；
S400, the Pore genesis obtained according to step S300 calculates Pore classification coefficient, and the porosity type of rock core is judged according to the size of Pore classification coefficient, and the porosity type includes slit formation, dissolution pore type and matrix type；
Wherein, the Pore classification coefficient is the parameter for evaluating relative size between slit formation hole and dissolution pore type hole gap in pore region, its value TC1For
In formula, ELc is cross section crack percent continuity；ELLFor vertical section fracture development coefficient；PCrFor crack and hole relative degree of development；For rock sample porosity；And
In formula, FL is maximum fracture length in cross section；CL is rock core length；EfL is more than the fracture length summation of designated length for effective length in longitudinal section；CR is core diameter；FpPore region percentage is accounted for for flaw area；HpPore region percentage is accounted for for hole area.
In embodiments in accordance with the present invention, above-mentioned steps S100, the signal to noise ratio of image can be improved by medium filtering, adaptive median filter, gaussian filtering, LPF, high-pass filtering or Wiener filtering.
In embodiments in accordance with the present invention, above-mentioned steps S200, image can be split using the porosity bounding algorithm based on normal distribution, the algorithm comprises the following steps：
1) the grey level probability density curve at same rock core difference CT sections is counted, using just too distribution curve is fitted to grey level probability density curve, mean μ in normal distribution curve, variances sigma is obtained,
In formula, a is peak value extensive factor, and b is needle position misalignment amount, and μ is normal distribution average, and σ is Variance of Normal Distribution；
2) [the λ in given variance distance rangemin, λmax] make λ=λ0, λ0For initial variance distance；
3) different CT section place corresponding image partition threshold μ of party's gap under is calculated respectivelyi-λσi, i=1,2 ... ... n, n is scan image number, then calculates respectively and obtains black region area in bianry image, counts the Areal porosity SP under different cross sectioni；
4) the average value SP of the Areal porosity under different cross section is calculatedave；
5) the average value SP of Areal porosity is judgedaveWhether it is less than previously given conditional parameter ε with the absolute value of porosity φ difference：
If | SPave- φ | ＜ ε, condition stops, and
If SPave＜ φ, then λ=λmax, terminate；
If SPave＞ φ, then λ=λmin, terminate；
If | SPave- φ | >=ε, then), repeat step 3)~5), stop until meeting condition；
6) the constant coefficients λ based on determination, obtains the partition threshold of different images, and threshold values size is μi-λσi, i=1,2 ... ... n, n is scan image number, finally distinguishes pore region and matrix areas according to the CT images at same testing rock core different cross section position.
In embodiments in accordance with the present invention, above-mentioned steps S200, the seed growth algorithm using matrix areas as growing point can be used to split image.
In embodiments in accordance with the present invention, above-mentioned steps S200, image can be split using void area edges extraction algorithm.
In embodiments in accordance with the present invention, above-mentioned steps S300, the process for extracting Pore genesis comprises the following steps：
1) morphology opening and closing operation processing is carried out to the pore region isolated, is optionally interrupted or connects hole；
2) make skeleton micronization processes to the pore region after Morphological scale-space, obtain the topological structure of pore region；
3) topological structure based on pore region calculates the Pore genesis of pore region.
Embodiments in accordance with the present invention, above-mentioned the Pore genesis at least area including pore region, convex surface are accumulated, than one kind in surface, effective length, tortuous length, equivalent width, maximum inscribed circle radius, equivalent diameter.
Embodiments in accordance with the present invention, above-mentioned steps S300 includes following small step：
1) by setting up characteristic vector to carrying out preliminary screening like round shape or elliptoid dissolution pore hole, the slit formation hole of strip in CT scan figure, training sample is provided for follow-up SVMs training；
2) sample classification training is carried out using SVMs, and then automatically completed to the crack in pore region and the Classification and Identification of dissolution pore.
Embodiments in accordance with the present invention, EfL is fracture length summation of the effective length at least above 5mm in longitudinal section.
Further, if TC1>10, carbonate porosity is slit formation；If 1<TC1<10, carbonate porosity is dissolution pore type；If TC1<1, carbonate porosity is matrix type.
Compared with prior art, one or more embodiments of the invention can have the following advantages that：
The present invention judges the porosity type of carbonate reservoir using the concept of Pore classification coefficient, the traditional mode for judging Types of Carbonate Reservoir by professional's experience in quality is in the prior art broken away from, the quantitatively evaluating of science is made to Reservoir Fracture, dissolution pore development degree, judges that reservoir core porosity type provides strong technical support for batch.In addition, the present invention is during hole classification factor is obtained, while can also obtain the important informations such as distribution situation, development degree and the shape facility of carbonate rock rock core hole in bulk, provide the foundation parameter to Hydrocarbon Migration and oil and gas flow law study.
Other features and advantages of the present invention will be illustrated in the following description, also, is partly become apparent from specification, or is understood by implementing the present invention.The purpose of the present invention and other advantages can be realized and obtained by specifically noted structure in specification, claims and accompanying drawing.
Brief description of the drawings
Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for specification, is provided commonly for explaining the present invention with embodiments of the invention, is not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is the method flow diagram that carbonate porosity type is judged in the embodiment of the present invention；
Fig. 2 is exemplarily shown carries out the schematic diagram that CT scan obtains sectional drawing to rock core in the embodiment of the present invention；
Fig. 3 (a) and 3 (b) exemplarily show the front and rear comparison diagram pre-processed in the embodiment of the present invention to CT scan image；
Fig. 4 exemplarily shows the operation principle for improving signal noise ratio (snr) of image in the embodiment of the present invention using adaptive median filter method；
Fig. 5 exemplarily shows the schematic diagram being fitted in the embodiment of the present invention using normal distribution curve to CT scan gradation of image probability density；
Fig. 6 (a)~6 (c) exemplarily shows the pore region and matrix areas isolated in the embodiment of the present invention by porosity bounding algorithm；
Fig. 7 exemplarily shows the growth course of growing point in seed growth algorithm in the embodiment of the present invention；
Fig. 8 (a) and 8 (b) exemplarily show the pore region and matrix areas isolated in the embodiment of the present invention by seed growth algorithm；
Fig. 9 (a) and 9 (b) exemplarily show the pore region and matrix areas isolated in the embodiment of the present invention by void area edges extraction algorithm；
Figure 10 (a) and 10 (b) exemplarily show the morphological images obtained in the embodiment of the present invention, skeleton image and azimuth rose figure；
Figure 11 exemplarily shows the morphological images obtained in the embodiment of the present invention and Euclidean distance figure；
Figure 12 (a) and 12 (b) exemplarily show the class test result of the SVM training under two kinds of dimensionless group group combinations in the embodiment of the present invention；
Figure 13 exemplarily shows the reference picture of the rock core of the different aperture type corresponding to different size of Pore classification coefficient.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with drawings and examples.It should be noted that the exemplary embodiment and its explanation in the present invention are only used for explaining the present invention, it is not as a limitation of the invention.
The method that carbonate porosity type is judged based on scan image that the present invention is provided, mainly including following four big steps.
S100, carries out including the colored pretreatment for turning gray scale and improving signal to noise ratio to the Core Scanning Image of carbonate rock.
CT image scannings are carried out to rock core, scan mode can be found in accompanying drawing 2, then the image after scanning is pre-processed.Pretreatment includes coloured image switching to gray level image and improves the signal to noise ratio of image.Specifically, CT scan image is opened using Photoshop softwares, rock core region is sketched the contours using Magnetic Lasso Tool, exports to and common-format files (as shown in Figure 3) is saved as in new blank document.Pretreated image is switched into gray level image, and effective noise reduction process is carried out to gray level image, to provide basis for follow-up image segmentation.Embodiment can be as follows：Pretreated CT images under imread function lead-in paths are utilized in Matlab softwares, gray level image is converted into without symbol RGB image by 8 of importing, data volume is changed into two dimension from three-dimensional.In this example, it is preferred to use the method for adaptive median filter carries out noise reduction process to image.The operation principle of adaptive median filter is that progress selectivity is preferred on the basis of medium filtering, and the central pixel point for filtering neighborhood is judged：If the pixel being judged is neighborhood extreme value, noise reduction process is carried out to the sort method of medium filtering；Otherwise this pixel being judged is skipped, the field for carrying out next pixel judges (as shown in Figure 4).It is of course also possible to use the mode such as medium filtering, gaussian filtering, LPF, high-pass filtering or Wiener filtering improves the signal to noise ratio of image, the present invention do not limited this.
S200, splits to the image after step S100 processing, reaches the effect of the pore region and matrix areas in differentiation figure.
The pore region in image is analyzed, until reaching the effect that pore region and matrix areas make a distinction completely.In the present invention, the pore region and matrix areas in image can be distinguished using three kinds of different image segmentation algorithms：
I) the porosity bounding algorithm based on normal distribution；
Ii) the seed growth algorithm by growing point of matrix areas；
Iii) void area edges extraction algorithm.
These three methods both can individually be implemented, and can also combine implementation.
Porosity bounding algorithm based on normal distribution is a kind of the algorithm of batch threshold values constraint can be carried out for same testing rock core multiple CT scan images.The core of the algorithm is to find the threshold values that the gray value at distance gray scale peak value μ fixed range λ σ positions is split as image, it is thus determined that constant coefficients λ (usual λ number range is between 0~3) becomes the key for algorithm, comprising the following steps that for constant coefficients λ is determined：
1) the grey level probability density curve at same rock core difference CT sections is counted, using just too distribution curve is fitted to grey level probability density curve, mean μ in normal distribution curve, variances sigma is obtained.
In formula, a is peak value extensive factor, and b is needle position misalignment amount, and μ is normal distribution average, and σ is Variance of Normal Distribution.
Fig. 5 exemplarily shows a matched curve, it can be seen that normal distribution f (x) has good adaptability to CT scan image.In different scanning image, it is different to be fitted obtained peak value μ and variances sigma based on normal distribution curve, for the influence of the factors such as the contrast, the brightness that take into full account every image taking background environment difference and cause, threshold values per pictures takes the threshold values that the gray value at the same position relative to peak value μ is split as image, and then distinguishes interstitial space and matrix areas.
2) [the λ in given variance distance rangemin, λmax], it is initial variance distance lambda to make λ0.Herein, λ0Span between 0~3.
3) different CT section place corresponding image partition threshold μ of party's gap under is calculated respectivelyi-λσi(i=1,2 ... ... n, n are scan image number), then calculates and obtains black region area in bianry image, count the Areal porosity SP under different cross section respectivelyi。
4) the average value SP of the Areal porosity under different cross section is calculatedave。
5) the average value SP of Areal porosity is judgedaveWhether it is less than previously given conditional parameter ε with the absolute value of porosity φ difference：
If | SPave- φ | ＜ ε, condition stops, and
If SPave＜ φ, then λ=λmax, terminate；
If SPave＞ φ, then λ=λmin, terminate；
If | SPave- φ | >=ε, thenRepeat step 3)~5), stop until meeting condition.
Just can be obtained by above step can match the constant coefficients λ of core porosity, and then obtain the partition threshold of different images, and threshold values size is specially μi-λσi(i=1,2 ... ... n, n are scan image number), finally just can efficiently distinguish pore region and matrix areas according to the CT images at same testing rock core different cross section position.
Fig. 6 exemplarily shows the pore region and matrix areas isolated by porosity bounding algorithm.Wherein, Fig. 6 (a) is the pore region and matrix areas isolated based on slit formation CT scan image；Fig. 6 (b) is the pore region and matrix areas isolated based on hole type CT scan image；Fig. 6 (c) is the pore region and matrix areas isolated based on matrix type CT scan image.
It is of course also possible to use another dividing method：Seed growth algorithm by growing point of matrix areas.Seed growth algorithm by growing point of matrix areas is to regard the growing point defined in the way of beforehand through man-machine interaction as initial growth point, grown according to similarity criterion requirement, the growth course of pre-selection growing point can be found in Fig. 7 (seed growth similarity criterion is set to 1 in Fig. 7, is found in 8 neighborhoods).In embodiment, seed growth point is arranged in the universal matrix pixel of development, similar growth threshold values is set to 40 (not more than 50), then pore region separation is carried out to gray level image, black region (being 0 region) in the bianry image of acquisition is pore region, and white portion is then rock matrix region.
Fig. 8 exemplarily shows the pore region and matrix areas isolated by seed growth algorithm.Wherein, Fig. 8 (a) is the pore region and matrix areas isolated based on slit formation CT scan image；Fig. 8 (b) is the pore region and matrix areas isolated based on hole type CT scan image.
And void area edges extraction algorithm herein can be for verifying the segmentation effects of other partitioning algorithms (such as above two algorithm).For example, in the present embodiment, can use the edge function calls built in Matlab that there is the Canny operators of preferable noise immunity and higher accuracy of identification, parameter is preferably transferred into tonal range and is set to [40,70], call variance to be set to 1.25, void area edges are extracted.
Fig. 9 exemplarily shows the pore region and matrix areas isolated by void area edges extraction algorithm.Wherein, Fig. 9 (a) is the pore region and matrix areas isolated based on slit formation CT scan image；Fig. 9 (b) is the pore region and matrix areas isolated based on hole type CT scan image.
S300, the pore region isolated to step S200 extracts Pore genesis, and recognizes the crack in pore region and dissolution pore.
The pore region image zooming-out Pore genesis isolated based on step S200, to be set up for information such as follow-up porosity type distribution, development degrees, dimensionless group is rolled into a ball and then quantitatively characterizing provides basis.The step is broadly divided into Region Feature Extraction and crack, dissolution pore recognized for two megastages.Wherein：
(1) Region Feature Extraction (namely Pore genesis extraction) mainly includes Morphological scale-space, three small steps of bone micronization processes and box counting algorithm.
1) Morphological scale-space：Morphology opening and closing operation processing is carried out to the pore region being partitioned into, is optionally interrupted or connects hole, with to the correction in the pore region completion morphology being partitioned into and smoothing processing, and the invalid noise spot of removal.In the present invention, it is preferred to be corrected using mode associated with opening and closing operation to pore region.Embodiment be Matlab in set up square structure element using Strel functions, it is 2 to set square length of side pixel, respectively morphological operation is carried out using imopen functions and imclose function pairs bianry image, Morphological scale-space first is carried out to segmentation bianry image using opening operation, then handled again with closed operation, it is selectively connected or disconnects image mesopore region, reaches the effect of the form smoothing processing of pore region.
2) skeleton micronization processes：The important topological structures such as Medial Axis Skeleton, end points, the inner void of the pore region that are partitioned into are peeled off.In the present embodiment, specific implementation process is as follows：In 3 × 3 contiguous range, foreground (being generally black) is set to 1, background colour (being generally white) is set to 0, and the deletion of selectivity is carried out according to following rule：
1. 2≤NZ (P)≤6, NZ (P) represents 8 pixel (P around P pointsOn、PUnder、PIt is left、PIt is right、PLower-left、PUpper left、PUpper right、PBottom right) in 1 number；
②Z0(P)=1, Z0(P) number of adjacent pixel dissimilarity in P surrounding pixel points is represented；
③POn×PIt is left×PIt is right=0 or Z0(P)≠1；
④POn×PIt is left×PUnder=0 or Z0(PIt is left)≠1。
Meet simultaneously more than during rule, central pixel point P can be changed into white from black, namely delete foreground.
The skeleton structure in connected domain is separated by rejecting the redundancy edge pixel of connected domain, the shape facility of skeleton can be reflected emphatically.Figure 10 exemplarily shows the morphological images and skeleton image obtained by CT scan image.Wherein, Figure 10 (a) is the morphological images and skeleton image obtained based on slit formation CT scan image；Figure 10 (b) is the morphological images and skeleton image obtained based on hole type CT scan image.
3) box counting algorithm：Mainly the calculating of Pore genesis is extracted.The area of pore region and the parameter of the big type of length two can be obtained by the step, area, convex surface product can be included, than the remainder parameter of surface, effective length, tortuous length, equivalent width, maximum inscribed circle radius, equivalent diameter, centrifugation degree and orientation etc. 10.The following is the circular of these parameters：
(1) area of pore region and convex surface product
The calculating of region parameter needs first to be labeled pore region.Specifically, bwlabel function pair pore regions can be used to do sequence number mark, then the sequence number according to mark tries to achieve region hole elemental area using regionprops function call area operators Area；The calculating of convex surface product can use the regionprops function calls convex surface sub- ConvexArea of integrating to be solved, and the area tried to achieve and convex surface product are image pixel area.
(2) pore region direction
Pore region direction needs to obtain the topological structure of pore region to obtain by skeleton thinning algorithm, i.e., travel direction judges on the basis of hole topological structure.Specific implementation method is：Bwlabel marks are carried out to pore region, then the thicken operators in bwmorph functions are called to erode processing to single pore region, the Orientation operators in regionprops functions are used after obtaining the skeleton after corroding obtain the direction spread of pore region.Figure 10 is the azimuth rose figure for exemplarily showing the sign pore region direction obtained based on skeleton image topological structure.Wherein, Figure 10 (a) is the azimuth rose figure of slit formation；Figure 10 (b) is the azimuth rose figure of hole type.
(3) effective length and tortuous length
The effective length of hole is used for the air line distance for evaluating pore region, and it needs the rectangular aspect covered by pore region to calculate, and circular is as follows：
In formula, L, W are the length and width of region boundary rectangle, and θ is hole azimuth.
During specific implementation, the length of rectangle and width can be obtained using the Bounding Box operators in regionprops functions in above formula, then in conjunction with known directioin parameter (hole azimuth), doing coordinate rotation using Givens matrixes in above formula can obtain.
Tortuous length can be calculated and obtained based on the pore region Medial Axis Skeleton obtained by skeleton thinning algorithm by following formula：
In formula, xi、yiX-direction, the coordinate of Y-direction being located in Medial Axis Skeleton are represented respectively, and i represents the pixel for the diverse location being located on Medial Axis Skeleton.
The parameters such as tortuosity can further be obtained according to effective length and tortuous length, so as to make effective judgement for the direction spread of pore region, regular degree.
(4) equivalent width and equivalent diameter
Equivalent width is calculated according to pore area and tortuous length simultaneous and determined, circular is as follows：
Equivalent width=pore area/tortuous length
The width numerical value in the region of interconnected pore with change in location can be uniformed using equivalent width.
Further, on the basis of pore area, irregular pore region can also be modeled to homalographic circle, its diameter is referred to as equivalent diameter, and circular is as follows：
The size of irregular hole net area can be differentiated using the equivalent diameter of irregular shape pore region.
(5) maximum inscribed circle radius
Maximum inscribed circle radius is the important parameter during hole is evaluated, and the inscribed circle radius numerical value in pore region at diverse location can be obtained by Euclidean distance figure, maximum therein is then taken as maximum inscribed circle radius.When it is implemented, the background colour region of bwdist function pair bianry images can be used to carry out the conversion of Euclidean distance figure, maximum therein is then chosen as maximum inscribed circle radius.Figure 11 exemplarily shows the morphological images obtained by CT scan image and Euclidean distance figure.
(6) surface is compared
The ratio surface of pore region is the extremely important parameter of pore region, and it can be obtained by the calculating to pore region overall circumference, and circular is as follows：
SV=4/ π BA
In formula, BAFor middle boundary length, S in image as unit areaVFor the surface area in unit volume.
During specific implementation, Sobel operator edge extractings can be done to the bianry image after Morphological scale-space first, statistics is carried out to the length after the edge pixel conversion after edge extracting to add and computing, multiplied by with 2/ π, its numerical values recited is the ratio surface of all pore regions in image.
(2) crack, dissolution pore identification are main including setting up characteristic vector, SVM (SVMs) Classification and Identification two small steps of training.
1) characteristic vector is set up.The step to being screened like round shape or elliptoid dissolution pore hole, the slit formation hole (i.e. the hole with characteristic feature) of strip by setting up characteristic vector, to provide training sample for follow-up SVMs training.
Core image characteristic parameter is extracted first, is chosen and is split by image, there is the strip crack of characteristic feature in the CT scan image after Morphological scale-space and vector is characterized like round shape hole parameter.For example, table 1 is the preliminary classification result of 166H rock cores porosity type after provincial characteristics parameter extraction, each porosity communication domain determines its porosity type by principium identification in table, and wherein Class1 is slit formation, and type 2 is matrix type, and type 3 is dissolution pore type.Remaining numbering rock core is carried out in the same way after the tentatively processing of the step such as demarcation of provincial characteristics parameter extraction and porosity type, pick out the strip slit formation with characteristic feature respectively in substantial amounts of porosity communication domain and set up standard feature vector like round shape hole hole type connected component parameter, as shown in table 2 the standard feature vector to filter out.Dimensionless variable composition characteristic vector finally is calculated using the standard feature parameter filtered out, dimensionless parameter group computational methods are specific as follows：
(1) length-width ratio and aspect ratio
Length-width ratio and aspect ratio are the important parameter for the development degree for evaluating interstitial space bar form, and difference is that the parameter used respectively in calculating is different, and length-width ratio uses calculating parameter for tortuous length, circular：
Length-width ratio=tortuous length ÷ equivalent widths
Corresponding is aspect ratio, and calculating parameter is then changed into effective length, and circular is：
Aspect ratio=effective length ÷ equivalent widths
(2) tortuosity
The bending degree of Medial Axis Skeleton in tortuous length primary evaluation interstitial space, it is mainly tortuous length and the big parameter of effective length two that it, which is related to parameter, and circular is：
Tortuosity=tortuous length ÷ effective lengths
The numerical value change of tortuosity is more than 1, and numerical value is bigger, and reaction bending degree is bigger.
(3) centrifugation degree
Centrifugation degree is the important parameter for evaluating interstitial space shape facility, and computational methods are, similar to ellipticity, the length scale for obtaining sub-elliptical major axis a and short axle b to be calculated respectively, centrifugation degree is specifically calculated by interstitial space：
The number range of centrifugation degree is between 0~1, and numerical value is closer to 1, and interstitial space shape is more flat；On the contrary, numerical value closer to 0 when, interstitial space shape is closer to circle.Generally, it can be obtained in specific implementation process by calling the Eccentricity operators compiled in Matlab in function regionprops functions to calculate.
(4) form factor
Form factor is mainly used in describing the smooth and systematicness at hole edge, and its numerical values recited is used for characterizing the degree of the uniform rule of pore shape.Because form factor can make the sign of quantification to the form of hole, therefore it is subsequently to judge crack, the important parameter of dissolution pore type.The computational methods of form factor are as follows：
The π area/perimeters of form factor=42
(5) solid degree
The size accumulated according to region area and convex surface may further determine that the solid degree of region hole.The circular of the solid degree of region hole is as follows：
Solid degree=area/convex surface product
Solid degree is sized to reaction regular shape degree, inner void developmental state, for follow-up crack, dissolution pore identification provides important parameter.
After the calculating rolled into a ball by above dimensionless group, the standard feature vector being made up of dimensionless group can be generated, basis is provided for follow-up SVM (SVMs) sample learning, dimensionless standard feature vector can specifically be shown in Table 3.
The characteristic parameter and Pore classification result of the 166H rock cores of table 1 difference connected domain
The slit formation of table 2 and hole type connected component vector
The zero dimension characteristic vector of table 3
2) SVM (SVMs) Classification and Identification is trained.The category filter that the step is automated using SVMs to porosity type, so as to judge to provide great convenience for the segmentation extraction of follow-up hole, type.In an embodiment of the present invention, it is preferred to use the mode fracture of parameter combination, dissolution pore type are trained respectively two-by-two, successively classification of assessment effect.Specifically, form factor ＆ centrifugations degree, the sorting technique of solid degree ＆ centrifugation degree, specific visible Figure 12 of classifying quality are preferably gone out respectively.
Figure 12 exemplarily shows the class test result of the SVM training under two kinds of dimensionless group group combinations.Wherein, Figure 12 (a) is SVM under the parameter combination of form factor ＆ centrifugation degree for dissolution pore type hole gap and the classification based training design sketch of slit formation hole；Figure 12 (b) is SVM under the parameter combination of solid degree ＆ centrifugation degree to dissolution pore type hole gap and the classification based training design sketch of slit formation hole.It can know from Figure 12, "+" represents the standard feature vector of dissolution pore type hole gap, " * " represents the standard feature vector of slit formation hole, and the type borderlines under form factor ＆ centrifugations degree or solid degree ＆ centrifugations degree both parameter combinations are respectively provided with good classifying quality.In addition, " ☆ " represents dissolution pore type porosity type to be tested, " ◇ " represents slit formation hole to be tested, it can be found by svm classifier training, " ☆ " and " ◇ " has been all fallen within the range of corresponding porosity type, and its svm classifier training reflected under the parameter combination by form factor ＆ centrifugations degree or solid degree ＆ centrifugation degree is reliable.
S400, the Pore genesis obtained according to step S300 calculates Pore classification coefficient, and the porosity type of rock core is judged according to the size of Pore classification coefficient, and the porosity type includes slit formation, dissolution pore type and matrix type.
It should be noted that stress evaluation emphatically in the present invention is the classification between slit formation hole, erosion type hole and hole matrix type carbonate rock, does not do further subdivision to erosion type porosity type and evaluate.Here, being used as the typical case of erosion type porosity type using " dissolution pore " one word.
On the basis of Pore genesis is obtained, the judgement of single porosity type is risen to the differentiation of rock core porosity type by calculating Pore classification coefficient.In the present invention, carbonate reservoir Pore classification coefficient is the parameter for evaluating relative size between slit formation hole and dissolution pore type hole gap in pore region, its value TC1For：
In formula, ELc is cross section crack percent continuity；ELLFor vertical section fracture development coefficient；PCrFor crack and hole relative degree of development；For rock sample porosity；And
In formula, FL is maximum fracture length in cross section；CL is rock core length；EfL is more than the fracture length summation of designated length for effective length in longitudinal section；CR is core diameter；FpPore region percentage is accounted for for flaw area；HpPore region percentage is accounted for for hole area.
Because parameter is more in calculating process, first related parameter can be exported by writing function TypePoreExport files in the specific implementation, derived parameter is directly used in calculating Pore classification coefficient.In the present embodiment, EfL is set as fracture length summation of the effective length more than 5mm in longitudinal section, then obtained Pore classification coefficient T C is calculated according to above formula1：
If TC1>10, carbonate porosity is slit formation；
If 1<TC1<10, carbonate porosity is dissolution pore type；
If TC1<1, carbonate porosity is matrix type.
Figure 13 exemplarily shows the reference picture of the rock core of the different aperture type corresponding to different size of Pore classification coefficient.
It is described above, it is only the specific implementation case of the present invention, protection scope of the present invention is not limited thereto, any to be familiar with those skilled in the art in technical specification of the present invention, and modifications of the present invention or replacement all should be within protection scope of the present invention.

Claims (10)

1. a kind of method that carbonate porosity type is judged based on scan image, is comprised the following steps：
S100, carries out including the colored pre- place for turning gray scale and improving signal to noise ratio to the Core Scanning Image of carbonate rock Reason；
S200, to step S100 processing after image split, reach distinguish image in pore region and The effect of matrix areas；
S300, the pore region isolated to step S200 extracts Pore genesis, and recognizes in pore region Crack and dissolution pore；
S400, the Pore genesis obtained according to step S300 calculates Pore classification coefficient, according to Pore classification system Several sizes judges the porosity type of rock core, and the porosity type includes slit formation, dissolution pore type and matrix type；
Wherein, the Pore classification coefficient be used to evaluate in pore region slit formation hole and dissolution pore type hole gap it Between relative size parameter, its value TC1For
In formula, ELc is cross section crack percent continuity；ELLFor vertical section fracture development coefficient；PCrFor crack With hole relative degree of development；For rock sample porosity；And
<mrow> <msub> <mi>EL</mi> <mi>c</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mi>F</mi> <mi>L</mi> </mrow> <mrow> <mi>C</mi> <mi>L</mi> </mrow> </mfrac> <mo>,</mo> <msub> <mi>EL</mi> <mi>L</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mi>E</mi> <mi>f</mi> <mi>L</mi> </mrow> <mrow> <mi>C</mi> <mi>R</mi> </mrow> </mfrac> <mo>,</mo> <msub> <mi>PC</mi> <mi>r</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>F</mi> <mi>p</mi> </msub> <msub> <mi>H</mi> <mi>p</mi> </msub> </mfrac> <mo>,</mo> </mrow>
In formula, FL is maximum fracture length in cross section；CL is rock core length；EfL is effective in longitudinal section Length is more than the fracture length summation of designated length；CR is core diameter；FpPore region is accounted for for flaw area Percentage；HpPore region percentage is accounted for for hole area.
2. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by：
In the step S100, by medium filtering, adaptive median filter, gaussian filtering, LPF, High-pass filtering or Wiener filtering improve the signal to noise ratio of image.
3. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by, in the step S200, image is carried out using the porosity bounding algorithm based on normal distribution Segmentation, the algorithm comprises the following steps：
1) the grey level probability density curve at same rock core difference CT sections is counted, just too distribution curve pair is utilized Grey level probability density curve is fitted, and obtains mean μ in normal distribution curve, variances sigma,
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mi>a</mi> <mrow> <msqrt> <mrow> <mn>2</mn> <mi>&amp;pi;</mi> </mrow> </msqrt> <mi>&amp;sigma;</mi> </mrow> </mfrac> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <mi>&amp;mu;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </msup> <mo>+</mo> <mi>b</mi> </mrow>
In formula, a is peak value extensive factor, and b is needle position misalignment amount, and μ is normal distribution average, and σ is normal state point Cloth variance；
2) [the λ in given variance distance rangemin, λmax] make λ=λ0, λ0For initial variance distance；
3) different CT section place corresponding image partition threshold μ of party's gap under is calculated respectivelyi-λσi, I=1,2 ... ... n, n are scan image number, then calculate respectively and obtain black region area in bianry image, Count the Areal porosity SP under different cross sectioni；
4) the average value SP of the Areal porosity under different cross section is calculatedave；
5) the average value SP of Areal porosity is judgedaveWhether the absolute value with porosity φ difference is less than previously given Conditional parameter ε：
If | SPave- φ | ＜ ε, condition stops, and
If SPave＜ φ, then λ=λmax, terminate；
If SPave＞ φ, then λ=λmin, terminate；
If | SPave- φ | >=ε, thenRepeat step 3)~5), until meeting bar Part stops；
6) the constant coefficients λ based on determination, obtains the partition threshold of different images, and threshold values size is μi-λσi, I=1,2 ... ... n, n are scan image number, finally according to the CT at same testing rock core different cross section position Image distinguishes pore region and matrix areas.
4. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by：
In the step S200, the seed growth algorithm using matrix areas as growing point is used to divide image Cut.
5. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by：
In the step S200, image is split using void area edges extraction algorithm.
6. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by, in the step S300, the process for extracting Pore genesis comprises the following steps：
1) morphology opening and closing operation processing is carried out to the pore region isolated, optionally interruption or connecting hole Gap；
2) make skeleton micronization processes to the pore region after Morphological scale-space, obtain the topology of pore region Structure；
3) topological structure based on pore region calculates the Pore genesis of pore region.
7. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by：
The Pore genesis at least the area including pore region, convex surface product, than surface, effective length, tortuous One kind in length, equivalent width, maximum inscribed circle radius, equivalent diameter.
8. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by, the step S300 includes following small step：
1) by CT scan figure like round shape or elliptoid dissolution pore hole, the slit formation hole of strip Carry out preliminary screening to set up characteristic vector, training sample is provided for follow-up SVMs training；
2) sample classification training is carried out using SVMs, and then automatically completed in pore region Crack and the Classification and Identification of dissolution pore.
9. the method according to claim 1 that carbonate porosity type is judged based on scan image, its It is characterised by：
EfL is fracture length summation of the effective length at least above 5mm in longitudinal section.
10. the method according to claim 9 that carbonate porosity type is judged based on scan image, its It is characterised by：
If TC1>10, carbonate porosity is slit formation；
If 1<TC1<10, carbonate porosity is dissolution pore type；
If TC1<1, carbonate porosity is matrix type.
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