CN103278864B - The geologic characteristic parameter α of hole seam type reservoir stratum and the assay method of distribution and device - Google Patents
The geologic characteristic parameter α of hole seam type reservoir stratum and the assay method of distribution and device Download PDFInfo
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Abstract
The present invention relates to the geologic characteristic parameter α of hole seam type reservoir stratum and the assay method of distribution and device.The assay method of the geologic characteristic parameter α of a kind of hole seam type reservoir stratum comprises the following steps: segmentation step, based on predetermined segmentation threshold, the core image of hole seam type reservoir stratum is divided into foreground area and background area；Extraction step, extracts described foreground area as FRACTURE CHARACTERISTICS region the core image after being split；Region set-up procedure, for each FRACTURE CHARACTERISTICS region, in being refined by region expansion, zonal corrosion and region, at least one carries out region adjustment to described FRACTURE CHARACTERISTICS region；And calculation procedure, according to the FRACTURE CHARACTERISTICS region extracted, calculate the geologic characteristic parameter α of this hole seam type reservoir stratum.
Description
Technical field
The present invention relates to assay method that use in petroleum geology exploration, the geologic characteristic parameter α of hole seam type reservoir stratum and
Determinator and hole seam type reservoir stratum distribution Forecasting Methodology and prognoses system.
Background technology
At present, in petroleum geology exploration, by the analyzing geological features of traditional hole seam type reservoir stratum, especially carbonate rock
Core surface FRAC, it is provided that a large amount of information intuitively are used for stratum qualitative analysis, and can be from exploration scene
The core image of the hole seam type reservoir stratum obtained extracts geologic characteristic parameter α, for quantitative analysis reservoir.
But, in traditional carbonate rock core surface FRAC technology, FRACTURE CHARACTERISTICS is manually described in many employings
Region, inefficiency.Being additionally, since the difference of analysis personnel, analytical data there is also difference in various degree.In current science and technology
The epoch of Informatization Development, it is impossible to well meet the work requirements of geology research worker.
The present invention is primarily directed to the problems referred to above, and the core image of fractured-vuggy reservoir is carried out computer quantitative Analysis, from
And measure the geologic characteristic parameter α of fractured-vuggy reservoir.
Summary of the invention
It is an object of the present invention however that provide a kind of geology spy being processed by image digitazation and measuring hole seam type reservoir stratum
Levy the assay method of parameter and device and the Forecasting Methodology of hole seam type reservoir stratum distribution and prognoses system.
A first aspect of the present invention provides the assay method of the geologic characteristic parameter α of a kind of hole seam type reservoir stratum, this mensuration side
Method comprises the following steps: segmentation step, based on predetermined segmentation threshold, the core image of hole seam type reservoir stratum is divided into foreground zone
Territory and background area；Extraction step, extracts described foreground area as FRACTURE CHARACTERISTICS region the core image after being split；
Region set-up procedure, for each FRACTURE CHARACTERISTICS region, in being refined by region expansion, zonal corrosion and region, at least one is to institute
State FRACTURE CHARACTERISTICS region and carry out region adjustment；And calculation procedure, according to the FRACTURE CHARACTERISTICS region extracted, calculate this hole seam type
The geologic characteristic parameter α of reservoir.
Providing the Forecasting Methodology of a kind of hole seam type reservoir stratum distribution according to the second aspect of the invention, this Forecasting Methodology includes
Following steps: image acquisition step, from least one position acquisition core image of hole seam type reservoir stratum；Geologic characteristic parameter α obtains
Step, for acquired each core image, uses the assay method described in first aspect present invention, obtains and this core image
The geologic characteristic parameter α of corresponding position；And reservoir distribution prediction steps, based on described in this hole seam type reservoir stratum at least
The geologic characteristic parameter α of one position, it was predicted that the distribution of hole seam type reservoir stratum.
Provide the determinator of the geologic characteristic parameter α of a kind of hole seam type reservoir stratum according to the third aspect of the invention we, this survey
Determining device to include: cutting unit, the core image of hole seam type reservoir stratum, based on predetermined segmentation threshold, is divided into foreground area by it
And background area；Extraction unit, it extracts described foreground area as FRACTURE CHARACTERISTICS region core image after being split；
Zone adjusting unit, it is for each FRACTURE CHARACTERISTICS region, and in being refined by region expansion, zonal corrosion and region, at least one is right
Described FRACTURE CHARACTERISTICS region carries out region adjustment；And computing unit, it, according to the FRACTURE CHARACTERISTICS region extracted, calculates this hole
The geologic characteristic parameter α of seam type reservoir.
Provide the prognoses system of a kind of hole seam type reservoir stratum distribution, this prognoses system bag according to the fourth aspect of the invention
Including: image acquiring device, it is from least one position acquisition core image of hole seam type reservoir stratum；Described in third aspect present invention
Determinator, it, for acquired each core image, measures position corresponding with this core image in hole seam type reservoir stratum
Geologic characteristic parameter α；And reservoir distribution prediction means, it is based at least one position described in this hole seam type reservoir stratum
Geologic characteristic parameter α, it was predicted that the distribution of hole seam type reservoir stratum.
The assay method of the geologic characteristic parameter α of the hole seam type reservoir stratum according to the present invention and device, and hole seam type reservoir stratum divides
The Forecasting Methodology of cloth and system combine rock core fracture geometry parameter quantitative calculation method to carbonate rock core surface FRACTURE CHARACTERISTICS
Carry out shelling discrete parameter to calculate, provide data the most intuitively convenient for petroleum geology exploration, improve geological research personnel
Work efficiency, preferably research and the distribution situation of predicting reservoir.
Accompanying drawing explanation
Fig. 1 shows the assay method of the geologic characteristic parameter α of hole seam type reservoir stratum according to the first embodiment of the invention
Flow chart；
Fig. 2 shows core image example before and after segmentation；
Fig. 3 shows for the schematic diagram that region expands is described；
Fig. 4 shows for the schematic diagram that region expands is described；
Fig. 5 shows the core image before and after the expansion of region；
Fig. 6 shows the schematic diagram for zonal corrosion is described；
Fig. 7 shows the schematic diagram for zonal corrosion is described；
Fig. 8 shows the core image before and after the expansion of region；
Fig. 9 shows the example of the pixel under the conditions of different eight neighborhood；
Figure 10 shows the core image before and after the refinement of region；
Figure 11 show between face, rock stratum and crack between the schematic diagram of angle calcu-lation method；
Figure 12 shows the determinator of the geologic characteristic parameter α of the hole seam type reservoir stratum according to first embodiment of the invention
Block diagram；
Figure 13 shows the assay method of the geologic characteristic parameter α of the hole seam type reservoir stratum according to second embodiment of the invention
Flow chart；
Figure 14 shows the determinator of the geologic characteristic parameter α of the hole seam type reservoir stratum according to second embodiment of the invention
Block diagram；
Figure 15 shows the core image before and after edge-smoothing；
Figure 16 shows the flow chart of the Forecasting Methodology that hole seam type reservoir stratum is distributed；And
Figure 17 shows the block diagram of the prognoses system that hole seam type reservoir stratum is distributed.
Detailed description of the invention
Hereinafter, with reference to accompanying drawing, embodiments of the present invention will be illustrated.These embodiments are only to realize this
Bright example, but it is not limited to this, but the various modification of the method described in description and structure can be included in.
In the present invention, use hole seam type carbonate rock as the example of hole seam type reservoir stratum, the hole seam of the present invention is described
The assay method of the geologic characteristic parameter α of type reservoir and device and the Forecasting Methodology of hole seam type reservoir stratum distribution and prognoses system.This
Skilled person after reading this disclosure, can should by the assay method of the present invention and device and Forecasting Methodology and system
For other hole seam type reservoir stratum.
First embodiment
Fig. 1 shows the assay method of the geologic characteristic parameter α of hole seam type reservoir stratum according to the first embodiment of the invention
Flow chart.
As it is shown in figure 1, in step ST102, based on predetermined segmentation threshold, the core image of hole seam type reservoir stratum is divided into
Foreground area and background area.
As example, same segmentation threshold is used to do dividing processing the core image of view picture.This is suitable for background
Region and foreground area have the image of substantially contrast.Wherein, this segmentation threshold is to be obtained by the rectangular histogram of this core image,
Or can be to be prestored based on the core image obtained in the past.Preferably, core image is split by following formula.
Fig. 2 shows the example of the core image before and after segmentation.Core image before (A) is segmentation in fig. 2, (B) is
The core image rendered after segmentation and through black and white.
Described foreground area is extracted as FRACTURE CHARACTERISTICS region step ST104, the core image after being split.?
Step ST106, for each FRACTURE CHARACTERISTICS region, in being refined by region expansion, zonal corrosion and region, at least one is to described
FRACTURE CHARACTERISTICS region carries out the region set-up procedure of region adjustment.
(1) region expands
Region expand (dilation) be the first predetermined structural element is moved in core image as process right
The first displacement images is obtained after the pixel of elephant, if any one pixel in the first displacement images and crack to be inflated
Characteristic area is overlapping, then belong to a pixel in the FRACTURE CHARACTERISTICS region after expansion as this pixel processing object.So-called
First structural element is moved to the pixel as processing object refer to translate the first structural element so that this first structural element
In predetermined benchmark pixel (such as, the central point of the first structural element) with as process object pixel overlap.Namely
Say, for a specific FRACTURE CHARACTERISTICS region, region expand through using predetermined first structural element translation with locate as waiting
The first displacement images is obtained after the translational movement that the position of pixel of reason object is corresponding, and appointing in the first displacement images
Those overlapping with FRACTURE CHARACTERISTICS region to be inflated of the pixel of anticipating are as processing the pixel of object as splitting after expanding
Pixel in seam characteristic area, expands core image.
Fig. 3 shows for the schematic diagram that region expands is described.For FRACTURE CHARACTERISTICS region X(to be inflated as in Fig. 3
(A) shown in), using the first structural element B(as shown in (B) in Fig. 3) move to obtain the first translation after the some a processing object
Image, i.e. translate the first structural element B and make the element (element shown in initial point o in (B) of Fig. 3) as benchmark element
Overlap with an a, if the first displacement images hits FRACTURE CHARACTERISTICS region X, i.e. any one pixel in the first displacement images with want
Inflated FRACTURE CHARACTERISTICS region X is overlapping, then we write down this point.The collection of all a point compositions meeting above-mentioned condition is collectively referred to as
Make the FRACTURE CHARACTERISTICS region after FRACTURE CHARACTERISTICS region X is expanded by B (region as shown in (C) bend of Fig. 3).It is formulated
For: D(X)=a | Ba ↑ X}=X B.
Fig. 4 shows for the schematic diagram that region expands is described.Core image X before (A) shows expansion in Fig. 4 shows
Example, wherein the circle of blacking is included in the pixel in FRACTURE CHARACTERISTICS region in representing core image X；Hollow circle represents rock core
Not included in the pixel in this FRACTURE CHARACTERISTICS region in image X.In Fig. 4, (B) shows the example of the first structural element B, wherein,
R represents the benchmark element in the first structural element B, and the example that (C) is the core image after expanding, the wherein circle table of blacking
The pixel in FRACTURE CHARACTERISTICS region it is included in after showing expansion；Hollow circle represents after expansion not included in this FRACTURE CHARACTERISTICS region
Interior pixel.
Fig. 5 shows the core image before and after the expansion of region.Core image before wherein (A) shows expansion in Fig. 5,
(B) core image after the expansion of region is shown.
(2) zonal corrosion
Zonal corrosion by the second predetermined structural element is put down move in described core image as process object
Pixel after obtain the second displacement images, if all pixels in the second displacement images and FRACTURE CHARACTERISTICS district to be corroded
Territory is overlapping, then belong to a pixel in the FRACTURE CHARACTERISTICS region after corrosion as this pixel processing object.So-called second
Structural element moves to pixel and refers to translate the second predetermined structural element so that the predetermined benchmark in this second structural element
Pixel (such as, the central point of the second structural element) overlaps with as the pixel processing object.It is to say, zonal corrosion passes through
Second is obtained after translational movement corresponding with the position as the pixel processing object for the second predetermined structural element translation
Displacement images, and using overlapping with FRACTURE CHARACTERISTICS region to be corroded for all pixels in the second displacement images those as
Core image, as the pixel in the FRACTURE CHARACTERISTICS region after corrosion, is corroded by the pixel of process object.
Fig. 6 shows the schematic diagram for zonal corrosion is described.Zonal corrosion can be regarded as the antithesis fortune that region expands
Calculate.As shown in Figure 6, for FRACTURE CHARACTERISTICS region X(to be corroded as shown in (A) in Fig. 6), the second structural element B(such as figure
In 6 shown in (B), wherein initial point o is the benchmark element of the second structural element) move to a some b in coordinate after obtain second
Displacement images, if the second displacement images is contained in FRACTURE CHARACTERISTICS region X, we write down this b point, all meet above-mentioned condition
The set of b point composition is referred to as FRACTURE CHARACTERISTICS region X and is corroded the FRACTURE CHARACTERISTICS region after (Erosion) by the second structural element B.With
Formula is expressed as:
Fig. 7 shows the schematic diagram for zonal corrosion is described.Core image X before (A) shows corrosion in Fig. 7 shows
Example, the circle of blacking represents the pixel in core image X in this FRACTURE CHARACTERISTICS region；Hollow circle represents core image
The not pixel in FRACTURE CHARACTERISTICS region in X.In Fig. 7, (B) shows the example of the second structural element B, and wherein R represents the second knot
Benchmark element in constitutive element B, and the example that (C) is the core image after corroding, the circle of blacking represents in core image X
Pixel in this FRACTURE CHARACTERISTICS region after corrosion；Hollow circle represents in core image X after corrosion not included in this crack
Pixel in characteristic area.
Fig. 8 shows the core image before and after zonal corrosion.Wherein the showing of core image before (A) zonal corrosion in Fig. 8
Example, (B) is the example of the core image after zonal corrosion.
(3) region refinement
Region refinement is belonging to FRACTURE CHARACTERISTICS region by eight neighbors according to pixel each in FRACTURE CHARACTERISTICS region
Or background area judges whether this pixel is retained in FRACTURE CHARACTERISTICS region, thus core image is refined.
Refined by region, obtain figure that approximate with original object area shape, that be made up of simple arc or curve.
These fine rules are near rock core crack, it is simple to describe and the feature of abstract image specific region.Will be according to often in thinning process
The situation of eight consecutive points of individual pixel judges that this point is to reject or retain.We illustrate how to sentence with reference to Fig. 9 below
Disconnected current pixel whether still reject by this reservation.
Fig. 9 shows the example needing the pixel to be processed situation under the conditions of different eight neighborhood.Wherein, as judgement
The pixel of object is positioned in Fig. 9 the central point in (1) to (7).
The eight neighborhood condition of pixel be in Fig. 9 shown in (1) time, this pixel can not delete because it is an internal point, we
Being required of skeleton, if interior pixels is also deleted, skeleton also can be emptied.The eight neighborhood condition of pixel is (2) in Fig. 9
Time shown, this pixel can not be deleted, and the reason that reason can not be deleted with the pixel shown in (1) in Fig. 9 is identical.
The eight neighborhood condition of pixel be in Fig. 9 shown in (3) time, this pixel can be deleted.This is because such pixel is not
It it is skeleton.The eight neighborhood condition of pixel be in Fig. 9 shown in (4) time, this pixel can not be deleted.This is because after deleting this pixel,
The pixel being originally connected is disconnected from each other.
The eight neighborhood condition of pixel be in Fig. 9 shown in (5) time, pixel can be deleted.This is because this pixel is not skeleton.
The eight neighborhood condition of pixel be in Fig. 9 shown in (6) time, this pixel can not be deleted.This is because this pixel is the end points of straight line, as
The most such pixel is deleted, then last whole straight line is also deleted, and what does not remains.The eight neighborhood condition of pixel is in Fig. 9
(7) time shown in, this pixel can not be deleted, this is because the skeleton of isolated point is exactly its own.
In sum, it is judged that the current pixel standard whether this reservation is still rejected is as follows:
1. interior pixels can not be deleted；
2. isolated pixel can not be deleted；
3. the pixel as straight line end points can not be deleted；
If 4. pixel is boundary point, after removing this pixel, if connected component does not increases, then pixel can be deleted.
According to above-mentioned criterion, make a table in advance, have 256 elements from 0 to 255, each element or
It is 0, or is 1.We table look-up according to the situation of eight consecutive points of certain pixel (yes black color dots to be processed), if table
In element be 1, then it represents that this point can be deleted, and otherwise retains.
The method tabled look-up is, if white point is 1, stain is 0；First (lowest order) of corresponding 8 figure places of upper left side point,
The corresponding second of surface point, the 3rd that upper right side point is corresponding, corresponding 4th of left adjoint point, corresponding 5th of right adjoint point, a left side
Corresponding 6th of lower section point, corresponding 7th of underface point, the 8th that lower right point is corresponding, is gone by 8 figure places so formed
Table look-up.
Such as in above example, the eight neighborhood condition of pixel be in Fig. 9 shown in (1) time, corresponding to the 0th in table,
This should be 0；The eight neighborhood condition of pixel be in Fig. 9 shown in (2) time corresponding to 37, this should be 0；The eight neighborhood of pixel
Condition be in Fig. 9 shown in (3) time corresponding to 173, this should be 1；The eight neighborhood condition of pixel be in Fig. 9 shown in (4) time pair
Should be in 231, this should be 0；The eight neighborhood condition of pixel be in Fig. 9 shown in (5) time corresponding to 237, this should be 1；Picture
The eight neighborhood condition of element be in Fig. 9 shown in (6) time corresponding to 254, this should be 0；The eight neighborhood condition of pixel is in Fig. 9
(7) corresponding to 255 time shown in, this should be 0.Thinking over the situation of the various eight neighborhood of current pixel, we can obtain
To a Refinement operation look-up table, this table is discussed in detail in following thinning algorithm.
As example, above step can computing in 3 × 3 neighborhoods, can be by the behaviour realizing refinement that tables look-up
Make.As exemplary method, can be achieved by the steps of:
(a) one 3 × 3 template of definition and a look-up table
Table 1 below gives the example of look-up table:
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,
1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,
1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,1,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,
1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,1,
0,0,1,1,0,0,1,1,1,1,0,1,1,1,0,1,
1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,
1,1,0,0,1,1,0,0,1,1,0,1,1,1,0,0,
1,1,0,0,1,1,1,0,1,1,0,0,1,0,0,0.
Table 2 below gives the example of 3 × 3 templates of a pixel.
| 1 | 2 | 4 |
| 128 | 256 | 8 |
| 64 | 32 | 16 |
B () to bianry image from top to bottom, be from left to right scanned；This process again image is carried out after terminating from a left side to
The right side, scanning from top to bottom；If the gray value of current pixel is " 0 " in image, and (scanning process considers for the first time around
Left and right pixel) or up and down (scanning process considers upper and lower two pixels for the second time) two pixels have any one be " 255 " then
Go to step (c), be otherwise turned back to step (b)；
C each pixel value in 3x3 region and the weights in the template of definition centered by () this pixel carry out convolution and ask
With, obtain the lookup index value k in table 2.1.1；
D () obtains, according to this index value k, the data that coordinated action from without and within, if " 1 ", then the gray value of this pixel sets
For " 255 ", i.e. delete this pixel, if " 0 ", be then " 0 " by the gray value of this pixel, i.e. retain this pixel.
E () image from first to last scans two times after, if this scanning have modified the point in core image, then jump to step
Suddenly (b), a new wheel scan is started.Otherwise image-region refinement terminates.
Figure 10 shows the core image before and after the refinement of region.Core image before wherein (A) region refines in Figure 10,
(B) it is the example of core image after the refinement of region.
As preferred example, in order to avoid division object, first core image is carried out zonal corrosion, but it is to have
Condition, say, that in zonal corrosion, those removable pixels (that is, belong to crack spy's characteristic area before zonal corrosion
But after zonal corrosion and be not belonging to the pixel in FRACTURE CHARACTERISTICS region) eliminate the most immediately and be only marked；Second step is held
Row region refines, those removable pixels of labelling (but after i.e. belonging to FRACTURE CHARACTERISTICS region zonal corrosion before region refines
And it is not belonging to the pixel in FRACTURE CHARACTERISTICS region)；In the third step, only removable picture will be marked as in zonal corrosion step
Those in the middle of element are not destroyed the pixel of connectedness and are eliminated after being judged as eliminating in region refines, otherwise retain this
A little pixels are as boundary point.Wherein, so-called elimination is to instigate this pixel to be not belonging to FRACTURE CHARACTERISTICS region.
In step ST108, according to the FRACTURE CHARACTERISTICS region extracted, calculate the geologic characteristic parameter α of this hole seam type reservoir stratum.
As example, geologic characteristic parameter α can be calculated as follows:
(1) fracture length: described fracture length refers to the length in single FRACTURE CHARACTERISTICS region, by this FRACTURE CHARACTERISTICS region
AL calculate fracture length.
(2) fracture width, described fracture width refers to the width in single FRACTURE CHARACTERISTICS region, calculates by formula (1)
W=A1/ L (1)
In formula:
The fracture width in this FRACTURE CHARACTERISTICS region of W, unit: mm；
The area in A1 FRACTURE CHARACTERISTICS region, unit: mm2；
The fracture length in this FRACTURE CHARACTERISTICS region of L, unit: mm.
(3) fracture width size is evaluated
Fracture width evaluation index is as follows:
Big seam: width > 2mm；
Middle seam: width 0.5～2mm；
Crack: width < 0.5mm.
(4) angle between face, rock stratum and FRACTURE CHARACTERISTICS region
See Figure 11, as the following formula the angle between (2) face, rock stratum and FRACTURE CHARACTERISTICS region:
H: depth of section；
D: diameter of a circle in cross section；
After the angle calculated between face, rock stratum and FRACTURE CHARACTERISTICS region, according to the size of this angle, can will split
Crack in seam characteristic area is performed as follows classification:
A. horizontal joint: angle < 5 °；
B. low angle R-joining: 5 °≤angle ＜ 30 °；
C. high angle oblique seam: 30 °≤angle ＜ 70 °；
D. vertical lap seam: angle > 70 °.
(5) width is averagely stitched
So-called average seam width refers to it is the mean breadth of all slits characteristic area in selected core image.As the following formula (3)
Carry out calculating and averagely stitch width:
In formula:
Averagely stitch width, unit: mm；
The width in i-th FRACTURE CHARACTERISTICS region, unit: mm in core image selected by Wi；
The quantity in n FRACTURE CHARACTERISTICS region.
(6) face seam rate
So-called face seam rate refers to the gross area of all slits characteristic area and selected core image in selected core image
Area ratio.(4) calculating face seam rate as the following formula:
In formula:
M face seam rate, is expressed as a percentage；
I-th crack averagely stitch width, unit: mm；
LiThe length of the-the i-th crack
The area of the core image selected by A2, unit: mm2；
(7) crack face length ratio
So-called crack face length than the total length unit referred at the unit are internal fissure characteristic area of core image is
mm/mm2, (5) calculating crack face length ratio as the following formula:
The rock area of total crack length (the mm)/selected core image in the face length ratio=selected core image of crack
(mm2) (5)
(8) fracture surface density
Fracture surface density refers to the quantity of the unit are internal fissure characteristic area at core image.(6) meter as the following formula
Calculation fracture surface density:
In formula:
TM fracture surface density, unit: bar/mm2；
The quantity in the FRACTURE CHARACTERISTICS region in core image selected by n；
The area of core image selected by A2, unit: mm2；
(9) linear fracture density
Linear fracture density is the line segment normal by taking vertical fracture system in selected core image, measures line segment normal
Length, and the quantity of adding up the FRACTURE CHARACTERISTICS region that this line segment normal is cut through calculates.Wherein, so-called crack system refers to
All slits in selected core image.(7) calculating linear fracture density as the following formula:
In formula:
TL linear fracture density, unit: bar/mm；
N is the quantity in FRACTURE CHARACTERISTICS region in selected core image；
The segment method line length that LD records, mm.
(10) fracture interval
Fracture interval refers to the average distance in selected core image between FRACTURE CHARACTERISTICS region, and unit is mm.
Figure 12 shows the determinator of the geologic characteristic parameter α of the hole seam type reservoir stratum according to first embodiment of the invention
The block diagram of 10.
Determinator 10 according to first embodiment of the invention includes that cutting unit 112, extraction unit 114, region are adjusted
Whole unit 116 and computing unit 118.
Cutting unit 112 based on predetermined segmentation threshold, the core image of hole seam type reservoir stratum is divided into foreground area and
Background area.As example, cutting unit 112 can be according to the assay method with the geologic characteristic parameter α of explanation hole seam type reservoir stratum
Step ST102 time the dividing method that is previously mentioned, core image is divided into foreground area and background area.
Extraction unit 114 extracts described foreground area as FRACTURE CHARACTERISTICS region the core image after being split.
Zone adjusting unit 116 is for each FRACTURE CHARACTERISTICS region extracted, by region expansion, zonal corrosion and district
In the refinement of territory, at least one carries out region adjustment to described FRACTURE CHARACTERISTICS region.As example, zone adjusting unit 116 can wrap
Include region bulge, zonal corrosion portion, refinement portion, region and control portion.
Region bulge such as can be according to the mode expanded with reference to the region described by 3 and Fig. 4, by predetermined the
One structural element moves to obtain the first displacement images as after the pixel processing object in described core image, and such as
Really in the first displacement images, any one pixel is overlapping with FRACTURE CHARACTERISTICS region to be inflated, then as this picture processing object
Element belongs to a pixel in the FRACTURE CHARACTERISTICS region after expansion, thus generates the core image after expanding.
Zonal corrosion portion such as can be according to the mode with reference to the zonal corrosion described by 6 and Fig. 7 by predetermined the
Two structural elements obtain the second displacement images after equalling the pixel as process object moving in described core image, if
All pixels of described second displacement images are overlapping with FRACTURE CHARACTERISTICS region to be corroded, then belong to as the pixel processing object
A pixel in FRACTURE CHARACTERISTICS region after corrosion, thus generates the core image after corrosion.
Refinement portion, region such as can be according to the mode with reference to the region refinement described by 9 by according to described FRACTURE CHARACTERISTICS
In region, eight neighbors of each pixel are belonging to described FRACTURE CHARACTERISTICS region or described background area to judge this pixel
Whether it is retained in described FRACTURE CHARACTERISTICS region, generates the core image after refinement.
Control portion can be according to the instruction inputted from outside, for each FRACTURE CHARACTERISTICS Region control region bulge, region
This FRACTURE CHARACTERISTICS region is processed by least one in corrosion portion, refinement portion, region.
Computing unit 118, according to the FRACTURE CHARACTERISTICS region extracted, calculates the geologic characteristic parameter α of this hole seam type reservoir stratum.Ground
It is previously mentioned during step ST108 of assay method of the geologic characteristic parameter α of the example of matter characteristic parameter such as explanation hole seam type reservoir stratum
The computational methods of geologic characteristic parameter α.
Each parts of the determinator 10 of the geologic characteristic parameter α according to the present invention, i.e. cutting unit 112, extraction unit
114, zone adjusting unit 116 and computing unit 118, can be realized by hardware, logic circuit；Or can be filled by mensuration
Put controller in 10 to perform to be arranged on as in the determinator 10 of computer and include relative with the function of these parts
The program of the instruction answered realizes.Via computer readable recording medium storing program for performing, (e.g., CD, disk, tape, magneto-optic disk or flash are deposited
Reservoir) or via such as the means of communication of the Internet, provide program to determinator 10.
Having the beneficial effects that of first embodiment of the invention, is processed by image digitazation and extracts carbonate rock coregraph
As upper FRACTURE CHARACTERISTICS region, at least one the fracture characteristic area in being refined by region expansion, zonal corrosion and region
Carry out region adjustment；It is calculated relevant feature parameters in conjunction with rock core fracture geometry parameter quantitative calculation method, can be very convenient
Ground carries out the analysis of feature extraction and macro and micro to carbonate rock core surface crack, obtains relevant geologic characteristic parameter α, from
And be preferably to study the distribution situation with predicting reservoir to lay the foundation.
(the second embodiment)
Figure 13 shows the mensuration side of the geologic characteristic parameter α of hole seam type reservoir stratum second embodiment of the invention
The flow chart of method, Figure 14 shows the mensuration dress of the geologic characteristic parameter α of the hole seam type reservoir stratum according to second embodiment of the invention
Put.
Geologic feature referring to Figure 13 and Figure 14 description hole seam type reservoir stratum second embodiment of the invention
The assay method of parameter and determinator 100.
Determinator 100 according to first embodiment of the invention includes that cutting unit 112, extraction unit 114, region are adjusted
Whole unit 116, computing unit 118, pretreatment unit 110, denoising unit 120, edges of regions smooth unit 122 and crack are special
Levy adjustment unit 124.
The hole seam type reservoir stratum according to the second embodiment geologic characteristic parameter α assay method step ST102,
The geologic characteristic parameter α of the hole seam type reservoir stratum according to the first embodiment that ST104, ST106 and ST108 describe with reference Fig. 3
Step ST102 of assay method, ST104, ST106 identical with ST108, cutting unit 112, extraction unit 114, district in Figure 14
The geology of the hole seam type reservoir stratum according to the first embodiment that territory adjustment unit 116 and computing unit 118 describe with reference Figure 12
The cutting unit 112 of the determinator 10 of characteristic parameter, extraction unit 114, zone adjusting unit 116 and computing unit 118 phase
With, description to these steps and unit is therefore omitted below.
First, in step ST100, pretreatment unit 110 carries out pretreatment to described core image.As example, can
To use at least one in following pretreatment to process:
(1) color range adjusts
As example, in the case of core image is coloured image, each color of red, green, blue of statistics core image
Rectangular histogram, calculates red, the blue and higher limit of each color of indigo plant and lower limit according to given parameter, and according to calculate red,
The higher limit of the green and each color of indigo plant and lower limit, adjust the tone scale curve of described core image.
In the case of core image is gray level image, add up the rectangular histogram of core image similarly, according to given ginseng
Number calculating upper limit value and lower limit, and according to the higher limit calculated and lower limit, the color range adjusting described core image is bent
Line.
(2) brightness of image/contrast/gray scale adjusts
For each pixel in core image, generate corresponding mapping value according to selected brightness, contrast, tonal range value
Index；Original color component is replaced according to mapping value index offset.
(3) brightness of image/hue/saturation adjusts
Image is changed into the bit data of brightness, tone and saturation, carries out brightness adjustment, hue adjustment and saturation and adjust
Whole.
In brightness adjustment, according to the red, green, blue value of described colored core image, calculate the brightness of each pixel in image,
By in the brightness adjustment of described core image to predetermined brightness range.In hue adjustment, according to described colored core image
Red, green, blue value, calculate the tone of each pixel in image, the tone data of described colored core image adjusted to predetermined
In tone range.In saturation adjusts, according to the red, green, blue value of described colored core image, calculate each pixel in image
Saturation, in the range of being adjusted to predetermined saturation by the saturation of described core image.Then, according to adjust after brightness,
Tone and saturation, calculate the value of each color of corresponding red, green and blue of each pixel.
Such as, the domain value range of brightness is (-100-100), and the current brightness value of pixel is 20, currently this value is adjusted to
50, whole brightness adds 30, the color component that skew 30 replacement is original.
(4) image filtering
Acquired core image is filtered, to remove the noise in core image.Can use existing suitably
Filtering algorithm, described core image is filtered.Preferably, can use median filtering algorithm that core image is filtered
Ripple.Median filtering algorithm is to take the intermediate value between the maximum in filter window and minima as by the value of filtered pixel.?
In the case of filter window is 5, during to n-th pixel filter, take in N-2, N-1, N, N+1 and N+2 five pixels
Big value and the intermediate value of minima, as the value of n-th pixel.For the in each image the 1st pixel, take the 1st pixel, the 2nd
Pixel, the 3rd pixel, the intermediate value between maximum and minima in these three pixel, filtered as the 1st pixel
Value, and for the 2nd pixel, take the 1st pixel, the 2nd pixel, the 3rd pixel and the 4th pixel, the value of these four pixels
In maximum and minima between intermediate value, as the filtered value of the 2nd pixel.For pixel second-to-last pixel,
Such as m-th pixel, in taking between maximum and the minima in the value of M-2, M-1, M and tetra-pixels of M+1
Value, as the filtered value of m-th pixel；And for last pixel of pixel, the M+1 pixel, take M-1, M and
The intermediate value between maximum and minima in the value of tri-pixels of M+1, as the filtered value of M+1 pixel.
By filtering under conditions of retaining image detail feature, inhibit making an uproar of core image to a certain extent as far as possible
Sound, can improve the validity and reliability that successive image processes and analyzes.
(5) image sharpening
Image sharpening can compensate the profile of image, strengthens edge and the part of Gray Level Jump of image, makes image become
Clearly, the basic reason that excessively smooth image thickens is because image and receives average or integral operation, therefore can lead to
Cross image is carried out that inverse operation (such as differentiating) makes that image becomes clear.The image that can adopt existing or later exploitation is sharp
Core image is sharpened by change technology.
(6) image smoothing
Core image is smoothed by the Image Smoothing Skill that can use existing or later exploitation.Put down by image
Sliding, the enlarged regions in core image, low-frequency component and trunk portion can be highlighted, and suppress picture noise and interference high frequency
Composition so that the mild gradual change of brightness of image, reduces sudden change gradient, improves picture quality.
(7) image blurring
Core image is obscured by the image blurring technology that can use existing or later exploitation.
(8) Image Edge-Detection
As example, Image Edge-Detection can be completed by following steps:
A () filters, rim detection is based primarily upon derivative calculations, affected by noise.But wave filter is while reducing noise
Also the loss of edge strength is caused；
B () strengthens, strengthen algorithm and highlighted by the point that mellow lime for field degree has significant change.Typically by calculating gradient width
Value completes；
(c) detect, but in some image gradient magnitude bigger be not marginal point；And
D () positions, accurately determine the position at edge.Other image border existing inspection in this area can certainly be used
The method for detecting image edge that survey method can also be developed after using.
(9) image film result
Colored core image is converted into the monochrome image with corresponding gray level.
Additionally, as example, above-mentioned pretreatment can be performed for a part for core image or some.
After step ST104, advance to step ST112.In step ST112, denoising unit 120 splits for extraction
Seam characteristic area, adds up the quantity of the pixel in each FRACTURE CHARACTERISTICS region, and the pixel quantity counted is less than noise-removed threshold value
FRACTURE CHARACTERISTICS region is removed as noise region.The upper and lower of noise-removed threshold value can be suitably regulated according to required picture quality
Limit.
After step ST112, advance to step ST106, in step ST106 as the first embodiment, region
Adjustment unit 106 is for each FRACTURE CHARACTERISTICS region, and in being refined by region expansion, zonal corrosion and region, at least one is to described
FRACTURE CHARACTERISTICS region carries out region adjustment.
Alternative, step ST106 can also perform before step ST112.After step ST106, advance to step
ST114。
In step ST114, edges of regions smooth unit 122 carries out edges of regions and smooths each FRACTURE CHARACTERISTICS region, this district
Territory edge-smoothing includes center of gravity calculation portion, and it uses this FRACTURE CHARACTERISTICS region as by filtered pixel group, calculates by filtered pixel
The center of gravity of group；First pixel groups calculating part, it calculates a described pixel farthest by center of gravity described in distance in filtered pixel group
Group, as the first pixel groups；Second pixel groups calculating part, its calculate described by filtered pixel group by described center of gravity and described
The second pixel groups that first pixel groups described in the projector distance on straight line that first pixel groups is formed is farthest；Removal portion, it is from institute
State and filtered pixel group is removed described first pixel groups and described second pixel groups, and control portion, it controls described center of gravity
Calculating part, described first pixel groups calculating part, described second pixel groups calculating part and described removal portion repeat operation, until
Described by till the surplus next pixel groups of filtered pixel group.
Perform edges of regions by following steps to smooth:
A () center of gravity calculation step, center of gravity calculation portion uses this FRACTURE CHARACTERISTICS region as by filtered pixel group, calculates this quilt
The center of gravity of filtered pixel group.Such as, using the filter image filtering element in each FRACTURE CHARACTERISTICS region as m N-dimensional vector, regard as in N-dimensional space
M point.Obtaining the some E representated by meansigma methods of these points, E point is referred to as this by the center of gravity of filtered pixel group by us.
B () first pixel groups calculation procedure, the first pixel groups calculating part calculates described by described in distance in filtered pixel group
The pixel groups that center of gravity is farthest, as the first pixel groups；Find out that distance center of gravity E in each point is farthest one, referred to as P point." away from
From center of gravity point furthest " it is exactly the point making (xi mono-E) (xi E) value maximum, wherein xi represents i-th point.Number represent
The sum of products of inner product, i.e. respective coordinates.
(c) second pixel groups calculation procedure, second pixel groups calculating part calculate described by filtered pixel group by described
The second pixel groups that first pixel groups described in the projector distance on straight line that center of gravity and described first pixel groups are formed is farthest.Find out
Farthest for projector distance P a point on straight line PE, referred to as Q point in each point." the projector distance P on straight line PE is farthest
Point " it is exactly the xt point making (P E) (E) value minimum (negative value of maximum absolute value).
D () removal step, removal portion removes described first pixel groups and described second pixel groups from by filtered pixel group.
E () control portion controls center of gravity calculation portion, the first pixel groups calculating part, the second pixel groups calculating part and removal portion and repeats
Center of gravity calculation step, the first pixel groups calculation procedure and the second pixel groups calculation procedure, until by the surplus next one of filtered pixel group
Till pixel groups.
Processed by edge-smoothing, the borderline tiny complications of each region shape in core image can be filtered, and smear
Fall the tiny miscellaneous point on core image, the abruptness of color change on core image can not be reduced simultaneously.By the color suddenlyd change
The border formed is in the border of color of still suddenling change after edges of regions is smooth, not obfuscation.
Figure 15 shows the core image before and after edge-smoothing.Wherein core image before (A) edge-smoothing in Figure 15,
(B) it is the core image after edge-smoothing.
In step ST116, FRACTURE CHARACTERISTICS adjustment unit 124 for each FRACTURE CHARACTERISTICS region carry out associated shape drafting and
Fill, carry out zonal corrosion and process and at least one in the expansion process of region, thus carry out FRACTURE CHARACTERISTICS adjustment.
In the assay method of the geologic characteristic parameter α according to the second embodiment, can only perform step ST100,
A part in ST112, ST114 and ST116, and ST100, ST112, ST114, ST116 can be repeated as required
With part or all in ST106.Additionally, the order of ST112, ST114, ST116 and ST106 can change.
Having the beneficial effects that of embodiment of the present invention, is processed by image digitazation and extracts on carbonate rock core image
FRACTURE CHARACTERISTICS region；District is carried out by least one the fracture characteristic area in region expansion, zonal corrosion, region refinement
Territory adjusts, and by carrying out pretreatment, denoising, edge thinning process process further to image, in conjunction with rock core crack
Geometric parameter quantitative calculation method is calculated relevant feature parameters, can enter carbonate rock core surface crack more accurately
Row feature extraction and the analysis of macro and micro, obtain relevant geologic characteristic parameter α more accurately, thus be that preferably research is with pre-
The distribution situation surveying reservoir lays the foundation.
Table 1 below shows the determinator 100 part core hole from tower using the geologic characteristic parameter α according to the present invention
The example list of the geologic characteristic parameter α that core image extracts.
Table 1
The each unit of the determinator 100 of the geologic characteristic parameter α according to the present invention, such as denoising unit 120, regional edge
Edge smooth unit 124 and FRACTURE CHARACTERISTICS adjustment unit 126, can be realized by hardware, logic circuit respectively；Or can be by
Controller in determinator 100 performs to be arranged on as in the determinator 100 of computer and include and these devices
The program of the instruction that function is corresponding realizes.Via computer readable recording medium storing program for performing (e.g., CD, disk, tape, magneto-optic disk
Or flash memory) or via such as the means of communication of the Internet, provide program to determinator 100.
Having the beneficial effects that according to second embodiment of the invention, can more accurately extract FRACTURE CHARACTERISTICS and calculating
Geologic characteristic parameter α.
(the 3rd embodiment)
Figure 16 shows the flow chart of the Forecasting Methodology of the hole seam type reservoir stratum distribution according to embodiment of the present invention.
In step ST402, from least one position acquisition core image of hole seam type reservoir stratum, the figure as shown in (A) in Fig. 2
Picture.
In step ST404, for acquired each core image, use the hole seam type according to first embodiment of the invention
The assay method of the geologic characteristic parameter α of reservoir, obtains the geologic characteristic parameter α of the position corresponding with this core image.
In step ST406, the geologic characteristic parameter α of at least one position based on this hole seam type reservoir stratum, it was predicted that hole seam type
The distribution of reservoir.Known method or the method for exploitation later, at least one position based on hole seam type reservoir stratum can be used
Geologic characteristic parameter α, it was predicted that the distribution of hole seam type reservoir stratum.
Figure 17 shows the prognoses system 1 of the hole seam type reservoir stratum distribution according to embodiment of the present invention.
Prognoses system 1 according to embodiment of the present invention includes determinator 10, image acquiring device 20 and reservoir distribution
Prediction means 30.
Image acquiring device 20 is from least one position acquisition core image of hole seam type reservoir stratum.Determinator 10 is basis
Determinator illustrated in embodiment of above of the present invention or its modified example, for acquired each core image, measures hole
The geologic characteristic parameter α of position corresponding with this core image in seam type reservoir.Reservoir distribution prediction means 30 is based on this hole
The geologic characteristic parameter α of at least one position described of seam type reservoir, it was predicted that the distribution of hole seam type reservoir stratum.
Each device of the prognoses system 1 from hole seam type reservoir stratum distribution according to the present invention, i.e. determinator 10, Image Acquisition
Device 20 and reservoir distribution prediction means 30, can be realized by hardware, logic circuit respectively；Or can be by prognoses system
Controller in 1 performs to be arranged on as in the prognoses system 1 of computer and include corresponding with the function of these devices
The program of instruction realizes.Via computer readable recording medium storing program for performing (e.g., CD, disk, tape, magneto-optic disk or flash memory)
Or via such as the means of communication of the Internet, provide program to prognoses system 1.
Having the beneficial effects that of the embodiment of the present invention, is processed by image digitazation and extracts on carbonate rock core image
FRACTURE CHARACTERISTICS region, is calculated relevant feature parameters in conjunction with rock core fracture geometry parameter quantitative calculation method；Last statistical
The result of calculation of analysis the fracture character of core geologic parameter, produces corresponding FRAC form and shows the result of analytical calculation,
The storage of result of calculation can be carried out as required；As such, it is possible to easily carbonate rock core surface crack is carried out spy
Levy and extract and the analysis of macro and micro, obtain relevant geologic characteristic parameter α, the distribution situation of reservoir is better anticipated.
Although with reference to the accompanying drawings of various embodiments, but will be appreciated that the information display device according to the present invention does not limits
In this.Those skilled in the art is it should be understood that may be variously modified or revise in the range of claims.Should
Recognize that these variations or modifications are the most also subordinated to the technical scope of the present invention.
Claims (18)
1. an assay method for the geologic characteristic parameter α of hole seam type reservoir stratum, this assay method comprises the following steps:
Segmentation step, based on predetermined segmentation threshold, is divided into foreground area and background area by the core image of hole seam type reservoir stratum
Territory；
Extraction step, extracts described foreground area as FRACTURE CHARACTERISTICS region the core image after being split；
Region set-up procedure, for each FRACTURE CHARACTERISTICS region, expanded by region, zonal corrosion and region refine at least one
Described FRACTURE CHARACTERISTICS region is carried out region adjustment；And
Calculation procedure, according to the FRACTURE CHARACTERISTICS region extracted, calculates the geologic characteristic parameter α of this hole seam type reservoir stratum；
After described extraction step and before described calculation procedure, this assay method is further comprising the steps of: to each crack
Characteristic area carries out the edges of regions smoothing step that edges of regions is smooth, and this edges of regions smoothing step comprises the following steps:
Center of gravity calculation step, uses this FRACTURE CHARACTERISTICS region as by filtered pixel group, calculates this center of gravity by filtered pixel group；
First pixel groups calculation procedure, calculates a described pixel groups farthest by center of gravity described in distance in filtered pixel group, makees
It it is the first pixel groups；
Second pixel groups calculation procedure, calculates and described is being formed by described center of gravity and described first pixel groups in filtered pixel group
Straight line on projector distance described in farthest the second pixel groups of the first pixel groups；And
Removal step, from described by filtered pixel group remove described first pixel groups and described second pixel groups, then repeat
Described center of gravity calculation step, described first pixel groups calculation procedure and described second pixel groups calculation procedure, filtered until described
Till the surplus next pixel groups of ripple pixel groups.
Assay method the most according to claim 1, this assay method calculates step after described extraction step and described
Comprise the following steps before rapid:
Denoising step, for the FRACTURE CHARACTERISTICS region extracted, adds up the quantity of the pixel in each FRACTURE CHARACTERISTICS region, will be counted
Pixel quantity remove as noise region less than the FRACTURE CHARACTERISTICS region of noise-removed threshold value.
Assay method the most according to claim 2, wherein, described region expands through and following generates the rock core after expansion
Image: obtain first after the first predetermined structural element being moved to the pixel as process object in described core image
Displacement images, and if any one pixel in the first displacement images overlapping, then with FRACTURE CHARACTERISTICS region to be inflated
This pixel is as the pixel in the FRACTURE CHARACTERISTICS region after expanding；
Wherein, described zonal corrosion generates the core image after corrosion by following: the second predetermined structural element is translated
The second displacement images is obtained after the pixel as process object in described core image, and if described second translation
All pixels in image are overlapping with FRACTURE CHARACTERISTICS region to be corroded, then this pixel is as the FRACTURE CHARACTERISTICS region after corrosion
In pixel；And
Wherein, the refinement of described region is described by being belonging to according to eight neighbors of each pixel in described FRACTURE CHARACTERISTICS region
FRACTURE CHARACTERISTICS region or described background area judge whether this pixel is retained in described FRACTURE CHARACTERISTICS region, generate thin
Core image after change.
Assay method the most according to claim 3, wherein, in described zonal corrosion for zonal corrosion before belong to described
But the pixel being no longer belong to described FRACTURE CHARACTERISTICS region after the zonal corrosion of FRACTURE CHARACTERISTICS region is marked, then carry out described
Region refines, and the labeled pixel being judged as in refining in described region staying in described FRACTURE CHARACTERISTICS region is as corruption
The pixel in FRACTURE CHARACTERISTICS region after erosion.
Assay method the most according to claim 1, this assay method be additionally included in described segmentation step before, to described
Core image carries out the pre-treatment step of pretreatment, and described pretreatment includes that at least one in following process processes: color
Contrast is whole, the adjustment of brightness adjustment, saturation, gray scale adjustment, hue adjustment, setting contrast, image filtering, image sharpening, figure
As smooth, image blurring, Image Edge-Detection and image egative filmization process.
Assay method the most according to claim 1, after this assay method is additionally included in described extraction step, at described meter
Calculating the FRACTURE CHARACTERISTICS set-up procedure before step, this FRACTURE CHARACTERISTICS set-up procedure carries out associated shape for each FRACTURE CHARACTERISTICS region
Drafting and filling, carry out zonal corrosion process and region expansion process at least one.
Assay method the most according to claim 1, described geologic characteristic parameter α includes in following parameter at least one:
Angle between fracture length, fracture width, face, rock stratum and crack, types of fractures, averagely stitch width, face seam rate, crack face length
Ratio, fracture surface density, linear fracture density, fracture interval.
Assay method the most according to claim 1, wherein, described hole seam type reservoir stratum is hole seam type carbonate rock.
9. a Forecasting Methodology for hole seam type reservoir stratum distribution, this Forecasting Methodology comprises the following steps:
Image acquisition step, from least one position acquisition core image of hole seam type reservoir stratum；
Geologic characteristic parameter α obtaining step, for acquired each core image, in employing claim 1-8 described in any one
Assay method, obtain the geologic characteristic parameter α of the position corresponding with this core image；And
Reservoir distribution prediction steps, based on the geologic characteristic parameter α of at least one position described in this hole seam type reservoir stratum, it was predicted that
The distribution of hole seam type reservoir stratum.
10. a determinator for the geologic characteristic parameter α of hole seam type reservoir stratum, this determinator includes:
Cutting unit, the core image of hole seam type reservoir stratum, based on predetermined segmentation threshold, is divided into foreground area and background by it
Region；
Extraction unit, it extracts described foreground area as FRACTURE CHARACTERISTICS region core image after being split；
Zone adjusting unit, it is for each FRACTURE CHARACTERISTICS region, by region expansion, zonal corrosion and region refinement at least one
Plant and described FRACTURE CHARACTERISTICS region is carried out region adjustment；And
Computing unit, it, according to the FRACTURE CHARACTERISTICS region extracted, calculates the geologic characteristic parameter α of this hole seam type reservoir stratum；
Edges of regions smooth unit, it is smooth that it carries out edges of regions to each FRACTURE CHARACTERISTICS region, this edges of regions smooth unit bag
Include:
Center of gravity calculation portion, it uses this FRACTURE CHARACTERISTICS region as by filtered pixel group, calculates by the center of gravity of filtered pixel group；
First pixel groups calculating part, it calculates a described pixel groups farthest by center of gravity described in distance in filtered pixel group, makees
It it is the first pixel groups；
Second pixel groups calculating part, it calculates and described is being formed by described center of gravity and described first pixel groups in filtered pixel group
Straight line on projector distance described in farthest the second pixel groups of the first pixel groups；
Removal portion, its from described by filtered pixel group remove described first pixel groups and described second pixel groups；And
Control portion, it controls described center of gravity calculation portion, described first pixel groups calculating part, described second pixel groups calculating part and institute
State removal portion and repeat operation, until described by the surplus next pixel groups of filtered pixel group.
11. determinators according to claim 10, this determinator also includes:
Denoising unit, it, for each FRACTURE CHARACTERISTICS region, adds up the quantity of the pixel in each FRACTURE CHARACTERISTICS region, by counted
Pixel quantity is removed as noise region less than the FRACTURE CHARACTERISTICS region of noise-removed threshold value.
12. determinators according to claim 11, described zone adjusting unit includes:
Region bulge, described region bulge generates the core image after expansion by following: the first predetermined structure
Element obtains the first displacement images after moving to the pixel as process object in described core image, and if described
Any one pixel in first displacement images is overlapping with FRACTURE CHARACTERISTICS region to be inflated, then after this pixel is as expanding
Pixel in FRACTURE CHARACTERISTICS region；
Zonal corrosion portion, described zonal corrosion portion generates the core image after corrosion by following: the second predetermined structure
Element obtains the second displacement images after moving to the pixel as process object in described core image, and if described
All pixels in second displacement images are overlapping with FRACTURE CHARACTERISTICS region to be corroded, then this pixel is as the crack after corrosion
Pixel in characteristic area；
Refinement portion, region, refinement portion, described region is by according to eight neighbors of each pixel in described FRACTURE CHARACTERISTICS region being
Belong to described FRACTURE CHARACTERISTICS region or described background area to judge whether this pixel is retained in described FRACTURE CHARACTERISTICS region,
Generate the core image after refinement；And
Control portion, this control portion according to from outside control command, control described region bulge, described zonal corrosion portion and
The each FRACTURE CHARACTERISTICS region extracted is processed by least one in refinement portion, described region.
13. determinators according to claim 12, wherein, described zonal corrosion portion is for belonging to described before zonal corrosion
But the pixel being no longer belong to described FRACTURE CHARACTERISTICS region after the zonal corrosion of FRACTURE CHARACTERISTICS region is marked, then will be described
The labeled pixel that refinement portion, region is judged as staying in described FRACTURE CHARACTERISTICS region is as the FRACTURE CHARACTERISTICS region after corrosion
In pixel.
14. determinators according to claim 10, this determinator also includes pretreatment unit, and it is to described coregraph
As carrying out pretreatment, described pretreatment includes that at least one in following process processes: color range adjustments, brightness adjustment, satisfy
With degree adjustment, gray scale adjustment, hue adjustment, setting contrast, image filtering, image sharpening, image smoothing, image blurring, figure
As rim detection and image egative filmization process.
15. determinators according to claim 10, this determinator also includes FRACTURE CHARACTERISTICS adjustment unit, and this crack is special
Levy adjustment unit and carry out drafting and the filling of associated shape for each FRACTURE CHARACTERISTICS region, carry out zonal corrosion and process swollen with region
At least one in swollen process.
16. determinators according to claim 10, described geologic characteristic parameter α includes in following parameter at least one
: angle between fracture length, fracture width, face, rock stratum and crack, types of fractures, averagely stitch width, face seam rate, fracture surface
Long ratio, fracture surface density, linear fracture density, fracture interval.
17. determinators according to claim 10, wherein, described hole seam type reservoir stratum is hole seam type carbonate rock.
The prognoses system of 18. 1 kinds of hole seam type reservoir stratum distributions, this prognoses system includes:
Image acquiring device, it is from least one position acquisition core image of hole seam type reservoir stratum；
Determinator according to any one of claim 10-17, it is for acquired each core image, measures hole seam type storage
The geologic characteristic parameter α of position corresponding with this core image in Ceng；And
Reservoir distribution prediction means, it is based on the geologic characteristic parameter α of at least one position described in this hole seam type reservoir stratum, in advance
Survey the distribution of hole seam type reservoir stratum.
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| CN105551018B (en) * | 2015-12-03 | 2018-04-20 | 长江大学 | Towards each component extracting method of carbonate rock complicated reservoirs digital cores |
| CN107133630B (en) * | 2016-02-29 | 2020-04-03 | 中国石油化工股份有限公司 | Method for judging carbonate rock pore type based on scanned image |
| CN106443802B (en) * | 2016-09-20 | 2018-11-13 | 中石化石油工程技术服务有限公司 | A kind of quantitatively characterizing method and system of the Electrical imaging reservoir fracture hole body based on morphologic filtering |
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