CN106227929A - Based on anisotropic non-stationary modeling method - Google Patents
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- CN106227929A CN106227929A CN201610570699.4A CN201610570699A CN106227929A CN 106227929 A CN106227929 A CN 106227929A CN 201610570699 A CN201610570699 A CN 201610570699A CN 106227929 A CN106227929 A CN 106227929A
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Abstract
The invention discloses a kind of based on anisotropic non-stationary modeling method, utilize the local anisotropy of the rose quantitatively characterizing training image that different directions variogram range combines, in conjunction with classical Multidimensional Scaling and K means cluster analysis, non-stationary training image is carried out auto-partition, all subregion is separate, use tradition stationarity multiple spot modeling algorithm simulation in subregion, finally realize non-stationary modeling.Therefore, comparing SIMPAT algorithm, the present invention can utilize non-stationary training image to carry out multiple spot geological statistics modeling very well.
Description
Technical field
The present invention relates to reservoir geologic modeling technical field, in particular to one based on anisotropic non-stationary modeling side
Method ASNSIM (Non-stationary Simulation based on Segmentation using Anisotropy).
Background technology
Geostatistics theoretical basis be stationarity it is assumed that i.e. regionalized variable average and variance meet spatial stationarity property and
Unrelated with position.The training image assumed based on stationarity should be stable, i.e. Spatial Variability is unrelated with locus.And
Developing to non-stationary direction owing to the changes such as structure, water forward/back, thing source control sedimentary system in actual nature, typical case is non-flat
Steady type of sedimentary facies has alluvial fan, Braided-river Deltas system etc..
Multiple-Point Geostatistics is the geostatistics modeling method that current research is popular, and Multiple-Point Geostatistics is by instruction
Practice image statistics space multi-point joint dependency, for predicted target values, the scanning of fixing model must be used in advance to extract training image
Total data pattern, the namely sample point of all positions of repeated observation training image, so estimate destination probability.
Assume that the training image of non-stationary has stationarity feature, be equal to think that the spatial form and structure of training image has
Have position independence and can in all position reproduction, actual this be irrational.If statistical explanation is the equal of spatial data
Value, variance are relevant to locus, and now the statistical data of all positions of training image can lose practical significance.Fig. 1 is one
Fan dalta (rock) phase model, the river course, I district in the upper left corner is generous, then gradually subtracts to II, III and IV partial block in the lower right corner
Thin, river course trend comes also with direction change is discrete simultaneously.Traditional multiple spot geological statistics modeling, as a example by Simpat, if
Do not consider in training image is non-stationary, and analog result will no longer keep and reappear the depositional configuration of training image.
Appearance 3 class non-stationary modeling methods at present:
1. training image does rotation calculate with scaling, obtain the multi-point statistic information of relative position independence；
2. based on partition method, by morphological feature such as Gabor texture filter, training image is divided into some
Sub regions, adds up the space multiple spot dependency of all subregion respectively, and in the respective sub-areas of model；
3. based on distance function incident space position and deposition characteristics, modeling considers dot position information to be estimated, thus realizes
Non-stationary is simulated.
Above 3 kinds of methods existing problems, the modeling method rotating scaling based on training image cannot be avoided rotating scaling meter
Time-consuming and the EMS memory occupation problem calculated, the morphological segment parameter used based on partition method belongs to two dimensional image analysis field,
It is difficult to apply to three-dimensional case, more sensitive based on distance function method value of adjusting the distance, make randomness poor apart from too small meeting, distance
Excessive very difficult control is non-stationary.
Therefore, a kind of new non-stationary modeling algorithm of research is needed badly, it is possible to apply non-stationary training image to carry out multiple spot
Geological statistics models.
Summary of the invention
For overcoming prior art not enough, it is an object of the invention to provide a kind of can be many based on non-stationary training image
Point geological statistics modeling algorithm (i.e. based on anisotropic non-stationary modeling method).This method solve and solve " non-stationary
Training image is difficult to directly carry out multiple spot geological statistics modeling " problem, non-stationary region can be done by the method for proposition automatically
Multidomain treat-ment, is relatively stable in subregion, differs greatly between subregion, regional is simulated respectively.
For solving above-mentioned purpose, the one that the present invention provides is based on anisotropic non-stationary modeling method, including following
Step:
1) input training image, the size of definition partial block；
2) use partial block scanning training image, calculate the rose of all of partial block, build rose
Storehouse；
3) manhatton distance between calculating in Flos Rosae Rugosae picture library two-by-two, builds distance matrix；
4) use classical Multidimensional Scaling matrix of adjusting the distance to carry out dimensionality reduction, obtain the two-dimentional perceptual map of Flos Rosae Rugosae picture library；
5) to step 4) in two-dimentional perceptual map carry out K mean cluster analysis, be divided into multiple class bunch；
6) create the multiblock technique equivalently-sized with training image, multiple class bunch results are assigned to point as subregion labelling
District's grid；
7) combining multiblock technique, scan training image with data model, create the division pattern database；
8) random access path is created for training image；
9) access random walk, if random walk has a node do not simulated, then enter following step 10) path,
Or, be otherwise directly entered following step 14) path；
10) data event at node is obtained with data model scanning simulated implementation；
11) combine multiblock technique and subregion pattern database, obtain the subregion pattern database at node；
12) Data Styles that in data event and subregion pattern database, manhatton distance is minimum is compared；
13) cover and freeze the data event at node by Data Styles entirety, return above-mentioned steps 9) path；
14) simulation terminates, and inputs simulated implementation.
Preferably, described step 2) in, use rose quantitatively characterizing anisotropy based on 8 direction rangees
Method, determines that the formula of rose is:
Rose={ αi| i=0,45,90,135,180,225,270,315}
αiBe direction be the range of the variogram matched curve of i.Owing to variogram has direction symmetry, i.e. orientation
0 ° of the angle range value with 180 ° is identical, by that analogy, only need to calculate 0 °, 45 °, 90 ° and 135 ° of these 4 azimuths in reality
Variogram.
Preferably, described step 2) in, obtain local space data with partial block scanning training image, calculate
The Experiment variogram in 8 directions, local sampling district, and with its spherical theoretical model of least square fitting, build Flos Rosae Rugosae
Figure, whole rose collection of training image are collectively referred to as rose storehouse, its formula:
RoseLibrary={Rose (loc1), Rose (loc2) ..., Rose (locn)}
In formula, Rose (locn) it is that in training image, position is locnRose, RoseLibrary is training image
The Flos Rosae Rugosae picture library of all local rose combination.
Preferably, described step 3) in, step 2) in build in the distance matrix obtained, matrix element is two
Manhatton distance between rose, its formula is:
In formula: dijIt is the manhatton distance between i-th and jth rose,It is in Flos Rosae Rugosae picture library i-th
The kth range of individual rose.Table 1 is Flos Rosae Rugosae picture library distance matrix (part) built according to Fig. 3 training image.
Obtain the perceptual map of two-dimensional space based on CMDS dimensionality reduction, the distance at perceptual map midpoint reflects the similar of rose
(different) property, also reflects similar (different) property of local anisotropy.
The method design principle of the present invention
There is not absolute stationarity phenomenon in nature, is steadily relative, and non-stationary is absolute.For theory of statistics
Stationarity requirement, tradition geostatistics has done stationarity and has assumed that geological phenomenon has statistics on locus and puts down
Stability, but it cannot be guaranteed that training image possesses complete stationarity in actual modeling, otherwise arise that in Fig. 1 training image with
The contradiction of analog result.It is considered herein that non-stationary for principal contradiction, stationarity is secondary contradiction, and it is existing that this meets objective geology
The vector stability that flattens progressivelyes reach non-stationary qualitative change along with space length increase.Anisotropy is reflection (non-) consistent level
Index, in theory, the similarity of the local anisotropy of same study area is the strongest, then this region stationarity is the strongest, otherwise non-
Stationarity is the strongest.
The beneficial effects of the present invention is:
Overall situation non-stationary training image is divided into some sub relatively smoothly based on local anisotropy by the present invention automatically
Region, separate between all subregions, subregion is internal uses classical stationarity algorithm to be simulated, and analog result is permissible
Reproduce the non-stationary feature of training image very well.
Accompanying drawing explanation
Fig. 1 is training image (Arpat, 2005) and the Simpat simulated implementation of non-stationary；
Fig. 2 is matching variogram curve and the rose in I～IV district；
Fig. 3 is that partial block scans training image schematic diagram；
Fig. 4 is to reduce anisotropy (rose) the Two dimensional Distribution perceptual map after dimension based on CMDS；
Fig. 5 is cluster result based on K-means；
Fig. 6 is non-stationary training image division result based on ASN strategy；
Fig. 7 is the flow chart of ASNSIM algorithm；
Fig. 8 is based on ASNSIM algorithm simulation result.
Detailed description of the invention
In order to preferably explain the present invention, it is further elucidated with the main contents of the present invention below in conjunction with specific embodiment, but
Present disclosure is not limited solely to following example.
In order to be best understood by the present invention, relational language is given below and explains:
1, grid cell (C Cell): along the X direction, there is appointment long (ISize), wide in Y-direction and Z-direction
(JSize), the rectangular cubic unit of high (KSize), grid cell stores concrete numerical value and represents its attribute.
2, grid body (G Grid): the three-dimensional structure being made up of a lot of grid cell C, in X-direction, Y-direction and Z side
To dimension be I × J × K, essence is three-dimensional matrice.(i, j k) are meant that X-direction index is equal to equal to i, Y-direction index to G
J, the Z-direction index grid cell equal to k.
3, training image (TI TrainImage): priori geologic concept model, uses grid body GTIAs data medium,
It it is the digital model that can state actual reservoir structure, geometric shape and distribution pattern thereof.
4, simulated implementation (R Realization): the model result of simulation, uses grid body GRAs data medium, it is
The digital model of actual reservoir structure, geometric shape and distribution pattern thereof can be stated.
5, data model (T Template): around center cell symmetrical structure body, uses grid body GTAs data
Carrier, is the local digital model that can state actual reservoir structure, combining form and distribution pattern thereof, based on pattern
The basic structural unit of Multiple-Point Geostatistics modeling method.
6, Data Styles (Pat Pattern): the local space number obtained for framework scanning training image with data model
According to assembly, use grid body GPatAs data medium.
7, data event (Dev DataEvent): the local space obtained for framework scanning simulated implementation with data model
Data set is fit, uses grid body GDevAs data medium.
8, pattern database (PatDB Pattern Database): use data model scanning training image to obtain
Take all Data Styles of this training image, referred to as pattern database.
9, partial block (Local Zone): partial structurtes body, uses grid body G as data medium, is to state
The local digital model of actual reservoir structure, combining form and distribution pattern thereof.
10, rose (Rose): the combinative structure of multi-direction variogram range.
11, Flos Rosae Rugosae picture library (Rose Library): use partial block scanning training image to obtain partial model, obtain
Whole rose set.
12, classical Multidimensional Scaling (CMDS Classic Multi-dimension Scaling): analysis and research
The data of higher-dimension are reduced to by the similarity of object or a kind of Multielement statistical analysis method of diversity according to the distance matrix of object
Low-dimensional.
13, two dimension perceptual map (PMap Perceptual map): after using classical Multidimensional Scaling dimensionality reduction to calculate,
The point distribution in two dimension theorem in Euclid space of the original high dimensional data, the distance between point reflects similar (different) of original high dimensional data
Property.
14, multiblock technique (RegionGrid): identical with training image size, by many for the segmentation of non-stationary training image
Individual subregion relatively smoothly, the grid body of the Labelling Regions composition of subregion.
15, plateau region in the training image of subregion (SubRegion): non-stationary.
16, subregion pattern database (SubRegionPatDB): use the subregion of data model scanning training image
The Data Styles set obtained.
17, subregion pattern database (RegionPatDB): by the subregion pattern database group of all subregions of training image
Become.
For the stationarity requirement of theory of statistics, tradition geostatistics has done stationarity and has assumed geological phenomenon
Locus has statistics stationarity, actual modeling cannot ensure that training image possesses complete stationarity, as instructed in Fig. 1
Practice image and analog result contradiction.The rose of regional area is the most similar, then belong to the subregion probability that stationarity is consistent
The biggest.Such as in Fig. 1 a, the geometric shape difference of I～IV partial block is relatively big, and for this species diversity of quantitative analysis, Fig. 2 calculates I
～IV district is at the Experiment variogram of 4 directions (-45 °, 0 °, 45 ° and 90 °), and with its spherical model of least square fitting.
Rose (Fig. 2-1b) principal stresses angle in I district is 0 °, and the range in other directions is more than another 3 districts simultaneously；II district (Fig. 2-2b)
The principal direction of rose is 0 °, and the weight of principal direction range is significantly greater than other directions, illustrates that the main flow direction in river course is inclined
To 0 °；The rose principal direction in III district (Fig. 2-3b) becomes 90 °, illustrates that river course moves towards 90 ° of deflections；IV district (figure-2-
4b) rose principal direction is 315 °.
Rose is made up of eight direction rangees, and similar (different) of directly analyzing rose from 8 dimensions spends relatively
Difficulty[19], use classical Multidimensional Scaling that rose is reduced to low-dimensional from higher-dimension the most herein, ASNSIM algorithm is by Flos Rosae Rugosae
Floral diagram reduces to 2 dimensions from 8 dimensions, and 2 dimension dot spacings reflect similar (different) degree of rose.Obtain with partial block scanning training image
To local space data (Fig. 3), calculate the Experiment variogram in 8 directions, local sampling district, and with least square fitting its
Spherical theoretical model, builds rose, and whole rose collection of training image are collectively referred to as rose storehouse, its formula:
RoseLibary={Rose (loc1), Rose (loc2) ..., Rose (locn)}
In formula, Rose (locn) it is that in training image, position is locnRose, RoseLibrary is training image
The Flos Rosae Rugosae picture library of all local rose combination.
The distance matrix (Distance Matrix) of Flos Rosae Rugosae picture library to be built, matrix before carrying out Multidimensional Scaling
Element is the manhatton distance between two roses, its formula
In formula: dijIt is the manhatton distance between i-th and jth rose,It is in Flos Rosae Rugosae picture library i-th
The kth range of individual rose.Table 1 is Flos Rosae Rugosae picture library distance matrix (part) built according to Fig. 3 training image.
The distance matrix (part) of table 1 rose
The perceptual map (Perceptual map, Fig. 4) of two dimension theorem in Euclid space is obtained based on CMDS dimensionality reduction, the point in Fig. 4
Distance reflects similar (different) property of rose, the most just reflects similar (different) property of local anisotropy.
Use K-means clustering method, the perceptual map P-Map of Fig. 4 is carried out cluster analysis, perceptual map is divided into k class
Bunch k-Clusters (Fig. 5), given k is equal to 4, and the key words sorting of the most each point is construed to the subregion labelling of training image,
Whole classification information of 4-Clusters is assigned to partial block Local Zone as subregion labelling, the most just obtains
Non-stationary training image division result (Fig. 6).
Use SIMPAT algorithm and inventive algorithm to the mould using non-stationary training image as training image separately below
Intend realizing, compare the non-stationary modeling effect of the simulated implementation inspection inventive algorithm of both algorithms.
The computer program of ASNSIM algorithm is worked out according to Fig. 7.Input training image (Fig. 1 a) obtains 2 Stochastic implementations
(Fig. 8), analog result is compared Simpat simulated implementation (Fig. 1 b) and has been reproduced the channel deposit morphological characteristic of fan dalta well.
The content not being described in detail in this specification, belongs to prior art known to those skilled in the art.On although
State embodiment and the present invention is made that detailed description, but its a part of embodiment that is only the present invention rather than all implement
Example, people can also obtain other embodiments according to the present embodiment under without creative premise, and these embodiments broadly fall into this
Invention protection domain.
Claims (4)
1. one kind based on anisotropic non-stationary modeling method, it is characterised in that: comprise the following steps:
1) input training image, the size of definition partial block；
2) use partial block scanning training image, calculate the rose of all of partial block, build Flos Rosae Rugosae picture library；
3) manhatton distance between calculating in Flos Rosae Rugosae picture library two-by-two, builds distance matrix；
4) use classical Multidimensional Scaling matrix of adjusting the distance to carry out dimensionality reduction, obtain the two-dimentional perceptual map of Flos Rosae Rugosae picture library；
5) to step 4) in two-dimentional perceptual map carry out K mean cluster analysis, be divided into multiple class bunch；
6) create the multiblock technique equivalently-sized with training image, multiple class bunch results are assigned to partition network as subregion labelling
Lattice；
7) combining multiblock technique, scan training image with data model, create the division pattern database；
8) random access path is created for training image；
9) access random walk, if random walk has a node do not simulated, then enter following step 10) path,
Or, be otherwise directly entered following step 14) path；
10) data event at node is obtained with data model scanning simulated implementation；
11) combine multiblock technique and subregion pattern database, obtain the subregion pattern database at node；
12) Data Styles that in data event and subregion pattern database, manhatton distance is minimum is compared；
13) cover and freeze the data event at node by Data Styles entirety, return above-mentioned steps 9) path；
14) simulation terminates, and inputs simulated implementation.
It is the most according to claim 1 based on anisotropic non-stationary modeling method, it is characterised in that: described step 2)
In, use rose quantitatively characterizing anisotropic approaches based on 8 direction rangees, determine that the formula of rose is:
Rose={ai| i=0,45,90,135,180,225,270,315}
aiBe direction be the range of the variogram matched curve of i.
It is the most according to claim 1 based on anisotropic non-stationary modeling method, it is characterised in that: described step 2)
In, obtain local space data with partial block scanning training image, calculate the experiment variation letter in 8 directions, local sampling district
Number, and with its spherical theoretical model of least square fitting, build rose, whole rose collection of training image are collectively referred to as
For Flos Rosae Rugosae picture library, its formula:
RoseLibrary={Rose (loc1), Rose (loc2) ..., Rose (locn)}
In formula, Rose (locn) it is that in training image, position is locnRose, RoseLibrary be training image own
The locally Flos Rosae Rugosae picture library of rose combination.
4. according to described in Claims 2 or 3 based on anisotropic non-stationary modeling method, it is characterised in that: described step
3) in, step 2) in build in the distance matrix obtained, matrix element is the manhatton distance between two roses, and it is public
Formula is:
In formula: dijIt is the manhatton distance between i-th and jth rose,It it is i-th rose in Flos Rosae Rugosae picture library
The kth range of rare floral diagram.
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| CN107705273A (en) * | 2017-10-11 | 2018-02-16 | 上海电力学院 | The non-stationary training image processing method of MPS simulations |
| CN108132193A (en) * | 2017-12-21 | 2018-06-08 | 西北工业大学 | A kind of method based on spherical indentation pattern identification material Anisotropic Plastic parameter |
| CN108986217A (en) * | 2017-05-31 | 2018-12-11 | 中国石油化工股份有限公司 | Multiple-Point Geostatistics modeling method based on pattern vector distance |
| CN110646846A (en) * | 2019-09-26 | 2020-01-03 | 中国石油大学(北京) | VIT medium anisotropy parameter determination method, device and equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108986217A (en) * | 2017-05-31 | 2018-12-11 | 中国石油化工股份有限公司 | Multiple-Point Geostatistics modeling method based on pattern vector distance |
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| CN107705273A (en) * | 2017-10-11 | 2018-02-16 | 上海电力学院 | The non-stationary training image processing method of MPS simulations |
| CN108132193A (en) * | 2017-12-21 | 2018-06-08 | 西北工业大学 | A kind of method based on spherical indentation pattern identification material Anisotropic Plastic parameter |
| CN108132193B (en) * | 2017-12-21 | 2020-06-16 | 西北工业大学 | Method for identifying anisotropic plastic parameters of material based on spherical indentation morphology |
| CN110646846A (en) * | 2019-09-26 | 2020-01-03 | 中国石油大学(北京) | VIT medium anisotropy parameter determination method, device and equipment |
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