CN106294540B - Multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles - Google Patents
Multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles Download PDFInfo
- Publication number
- CN106294540B CN106294540B CN201610583484.6A CN201610583484A CN106294540B CN 106294540 B CN106294540 B CN 106294540B CN 201610583484 A CN201610583484 A CN 201610583484A CN 106294540 B CN106294540 B CN 106294540B
- Authority
- CN
- China
- Prior art keywords
- data
- hash
- styles
- event
- local sensitivity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Abstract
The invention discloses a kind of multiple spot geological statistics modeling methods based on p-stable local sensitivity Hash retrieval Data Styles, the feature vector of Data Styles (event) is calculated using multi-block technique frame, is then hashed these feature vectors to multiple Hash tables using p-stable local sensitivity hash method.The feature vector wait the data event at estimating is extracted when modeling and carries out p-stable local sensitivity Hash calculation, obtain data event Hash barrel number, take out Data Styles building target patterns database all in these Hash buckets, the manhatton distance for comparing all Data Styles in data event and target patterns database, obtains most like data event.And SIMPAT algorithm is in simulation process, need to by wait at estimating data event and pattern database in all Data Styles carry out Similarity measures.Compared to SIMPAT algorithm, present invention application local sensitivity Hash retrieval technique greatly improves the efficiency of Multiple-Point Geostatistics modeling algorithm.
Description
Technical field
The present invention relates to reservoir geologic modeling technical fields, are examined in particular to one kind based on p-stable local sensitivity Hash
The multiple spot geological statistics modeling method (LSHSIM, Local Sensitive Hashing Simulation) of rope Data Styles.
Background technique
There is important supporting function to oil field Efficient Development based on Multiple-Point Geostatistics modeling algorithm.Arpat is 2003
Multiple-Point Geostatistics algorithm SIMPAT, algorithm SIMPAT based on pattern are proposed in year Stanford Forecast Oil Reservoir Distribution center meeting
Image reconstruction thought is introduced into reservoir geologic modeling, core concept is between matching " Data Styles " and " data event "
Similarity, detailed process are to match in priori geology pattern base (i.e. pattern database) (to count with simulation work area region to be estimated first
According to event) the maximum reservoir pattern of similarity measurement (Data Styles)；Then the reservoir pattern (Data Styles) covering is replaced
It simulates work area region to be estimated (data event).
With deepening continuously for oil field development, the precision of geological research is higher and higher, so that the scale of geological model is more next
It is smaller, such as the old filed of many mid-later development phases, it is 10m × 10m that the precision of Geologic modeling, which reaches planar mesh, longitudinal
0.5m is even more small.Generalling use stochastic simulation technology establishes geological model at present, it usually needs establishes multiple realizations (such as 100
It is a), even four dimension modules, therefore, the efficiency of Geologic modeling are increasingly becoming the hot issue of concern.Calculating hot spot is program generation
Code needs to take considerable time the part of operation, usually calculation performance bottleneck, the calculating hot spot of algorithm SIMPAT mainly around in
The Similarity measures of matched data event and all patterns of the pattern database of vast number, calculated performance become universal and are based on sample
The bottleneck of formula modeling algorithm.Limit the application of algorithm SIMPAT.
Therefore, it is badly in need of studying a kind of acceleration calculating improved method for SIMPAT algorithm, so as to efficiently be based on sample
Formula matching strategy carries out the modeling of multiple spot geological statistics.
Summary of the invention
It can be improved it is an object of that present invention to provide a kind of based on pattern multiple spot geological statistics modeling algorithm computational efficiency
Method (the multiple spot geological statistics modeling method i.e. based on p-stable local sensitivity Hash retrieval Data Styles).Compared to SIMPAT
The efficiency of Multiple-Point Geostatistics modeling algorithm is greatly improved in algorithm, this algorithm.
LSHSIM algorithm of the present invention is based on local sensitivity Hash technology and retrieves set of metadata of similar data pattern, first statistics pattern data
Multi-block technique, is converted to the feature vector of Data Styles by the sum of multi-block technique internal variable value of the total data pattern in library, into
And the cryptographic Hash of feature vector is calculated based on p-stable local sensitivity hash algorithm, identical (similar) the data sample of cryptographic Hash
Formula is stored in identical Hash bucket, constitutes Data Styles Hash library.The calculation of p-stable local sensitivity Hash is first based on when simulation
Method calculates the cryptographic Hash of data event, the Data Styles of identical Hash bucket is then retrieved from Data Styles Hash library, then calculate
Compare the similarity of data event Yu these Data Styles, finally finds out most like Data Styles.The present invention is based on Hash
Algorithm searches most like Data Styles, therefore greatly improves the efficiency of simulation.
To achieve the above object, provided by the invention a kind of based on p-stable local sensitivity Hash retrieval Data Styles
Multiple spot geological statistics modeling method, comprising the following steps:
1) training image TI is inputted, size, the size of data template T of simulated implementation R are defined；
2) size of multi-block technique BlockGrid is set, the parameter of p-stable local sensitivity Hash, including Hash are inputted
Bucket wide w, Hash table quantity N；
3) training image TI is scanned with data template T, establishes pattern database PatDB；
4) the sum of the multi-block technique block internal variable for counting total data pattern, obtains the feature vector of Data Styles, then
P-stable local sensitivity Hash calculation is carried out, Data Styles Hash library PatLSHLib is obtained；
5) random walk is created according to simulated implementation R；
If 6) have non-analog node U in random walk, into following step 7) path；
Otherwise enter following step 11) path；
7) the data event DataEvent at extraction node U, the sum of multi-block technique block internal variable of statistical data event,
The feature vector of data event is obtained, p-stable local sensitivity Hash calculation is carried out, obtains the cryptographic Hash of data event
DevLSH；
8) it is retrieved from Data Styles Hash library PatLSHLib identical with data event cryptographic Hash DevLSH Hash barrel number
Data Styles constitute target data style library ANNPatDB；
9) it is searched and the maximum Data Styles Pat of data event Dev similarity value from target data style library ANNPatDB；
10) it is integrally covered with Data Styles Pat and freezes the part in simulated implementation R at node U；Return to above-mentioned steps
6)；
11) simulation terminates, and inputs simulated implementation R.
Preferably, in the step 4), piecemeal is carried out according to Data Styles, that is, event size of mesh opening, establishes and divides
Block grid counts the sum of all nodal values in each piece, and formula is
Wherein, ICount, JCount are the number of grid of training image grid in both the horizontal and vertical directions,
MCount, NCount are the number of grid of multi-block technique in both the horizontal and vertical directions, BlockGridm,nBe index [m,
N] piecemeal in the sum of all nodal values, TIGridi,jIt is the nodal value that [i, j] is indexed in training image grid, sum expression is asked
And calculating.
Preferably, it in the step 4), just can be used as after multi-block technique data are converted to linear data structure
The data of p-stable local sensitivity hash algorithm input, linear data structure index and the two dimensional data structure rope of multi-block technique
The relationship of drawing is
BlockGridi=BlockGridM, n, wherein i=m*NCount+n
Wherein i is the linear directory of multi-block technique, and m and n are the 2-d indexs of multi-block technique.Table 1 is 4 data in Fig. 2
The multi-block technique statistical value of pattern (event), belongs to linear directory data structure.
Table 1 is based on the feature vector that Block Grid calculates Data Styles (event)
Preferably, in the step 4), there is the node of not variate-value in Data Styles, the change of training image
Minimum value is measured as dummy variable and participates in statistics calculating, formula is
Wherein, TI is training image, minTIIt is the variable minimum value of training image, var is the change of node in data event
Magnitude participates in calculating if some node of data event is empty (NULL) with the variable minimum value replacement null value of training image.
Algorithm core concept of the invention
The present invention is based on the feature vectors that multi-block technique frame calculates Data Styles (event), then use p-stable innings
Portion's sensitive hash method hashes these feature vectors in multiple Hash tables.According to the definition of local sensitivity Hash, same
In a Hash table, the probability that similar Data Styles (event) are in the same Hash bucket is far longer than dissimilar Data Styles
(event).When modeling, the feature vector and p-stable local sensitivity Hash meter for extracting data event are set using identical parameters
Its Hash barrel number in Hash table is calculated, Data Styles all in these Hash buckets is taken out and is referred to as similar closest pattern
Database.Compare the manhatton distance of data to all Data Styles in similar closest pattern database, selects Manhattan
Data event is integrally covered apart from the smallest Data Styles, completes the secondary simulation.
The definition of local sensitivity Hash is that (D indicates for space length measurement, P if a family of functions meets the following conditions
Probability)
If 1) the distance between two o'clock p and q D (p, q)<r1, P { h (p)==h (q) }>p1 in space
If 2) the distance between two o'clock p and q D (p, q)>r2, P { h (p)==h (q) } in space<p2
Significant under conditions of r1<r2, p1>p2, then family of functions H is local sensitivity.
Local sensitivity hash method can make the point being closer be mapped to the general of the same position by mapping calculation
Rate is big, and the probability for being mapped to the same position apart from farther away point is small.
Local sensitivity hash algorithm based on p-stable distribution assigns each feature vector v using the thought of p-stable
Give a cryptographic Hash h.Since the hash function is local sensitivity, it is therefore assumed that two feature vectors v1 and v2 are very close, it
The probability that will be mapped in identical bucket of cryptographic Hash it is very big.Hash function based on p-stable distribution is defined as
hA, b(v): Rd→N (1)
Map feature vector v a to set of integers of d dimension.
There are two stochastic variable a and b in hash function, and wherein a is the random vector of d dimension, and Euclidean distance is quantitative
The local sensitivity hash function in variable, Euclidean distance of distance is defined as between two points in characterization d dimension space
Wherein, a be one obey p-stable distribution independent random vector, w is that bucket is wide, b be in [0, w] range with
Machine number.Fig. 1 illustrates the principle based on p-stable local sensitivity hash algorithm by one: having n=5 on two-dimensional surface
A yellow dots.Inquiry and the hithermost yellow dots of blue dot, traditional directory method are to calculate the Europe of blue dot and all yellow dots
Formula distance is simultaneously ranked up, and takes the smallest yellow dots of distance value, the calculating time complexity O (n) of traditional directory method.Based on p-
The local sensitivity hash algorithm of stable carries out projection calculating to all yellow dots and blue dot according to given random vector a,
It is assumed that 3 different axis of projection X1, X2 and X3, each axis of projection are divided into multiple Hash buckets according to the wide w of bucket, all points are through projecting
After fall in different buckets, inquire identical with blue dot barrel number yellow dots.It is located at the Huang of identical Hash bucket in example with blue dot
Color dot has the point 1 in axis of projection X2, the point 2 in axis of projection X3, calculates all yellow dots in the same Hash bucket of blue dot
Euclidean distance takes the smallest point of distance value as query result.Query time is compared, traditional directory time complexity is O (5),
Query time complexity O (2) based on p-stable local sensitivity Hash, improves search efficiency.
The present invention carries out piecemeal (Fig. 2) according to the size of mesh opening of Data Styles (event), establishes multi-block technique (Block
Grid), the sum of all nodal values in each piece (Block) are counted, formula is
Wherein ICount, JCount are the number of grid of training image grid in both the horizontal and vertical directions,
MCount, NCount are the number of grid of multi-block technique in both the horizontal and vertical directions, BlockGridm,nBe index [m,
N] piecemeal in the sum of all nodal values, TIGridi,jIt is the nodal value that [i, j] is indexed in training image grid, sum expression is asked
And calculating.
It just can be used as p-stable local sensitivity hash algorithm after multi-block technique data are converted to linear data structure
Data input, the linear data structure of multi-block technique, which is indexed with two dimensional data structure index relative, is
BlockGridi=BlockGridM, n, wherein (4) i=m*NCount+n
Wherein i is the linear directory of multi-block technique, and m and n are the 2-d indexs of multi-block technique.Table 1 is 4 data in Fig. 2
The multi-block technique statistical value of pattern (event), belongs to linear directory data structure.
The Data Styles [a] and the geometric shape of Data Styles [b] of Fig. 1 are closely similar, correspond to multi-block technique in respective table 1
Feature vector it is very close.Otherwise the shape and feature vector of Data Styles [a] and Data Styles [c] all have larger difference
Not.When simulation, need to calculate the feature vector of data event, there may be the nodes of not variate-value for data event.At this point, handle
The variable minimum value of training image participates in statistics as dummy variable and calculates, and formula is
The beneficial effects of the present invention are:
The present invention introduces local sensitivity Hash retrieval technique and models to multiple spot geological statistics, differentiation when greatly improving modeling
Computational efficiency of the inquiry for the Data Styles of replacement data event, enhances the practicability of multiple spot geological statistics modeling method.
Detailed description of the invention
Fig. 1 is the searching principle schematic diagram based on p-stable local sensitivity hash algorithm；
Fig. 2 is the feature vector principle that Data Styles (event) is calculated based on Block Grid；
Fig. 3 is the flow chart of algorithm LSHSIM；
Fig. 4 is algorithm LSHSIM modeled example-type of sedimentary facies variable (classified variable),
In figure, a is training image, and b is simulated implementation figure；
Fig. 5 is algorithm LSHSIM modeled example-porosity categorical variable (continuous variable),
In figure, a is training image, and b is simulated implementation.
Specific embodiment
In order to better explain the present invention, below in conjunction with the specific embodiment main contents that the present invention is furture elucidated, but
The contents of the present invention are not limited solely to following embodiment.
Before illustrating the Multiple-Point Geostatistics modeling method LSHSIM based on local sensitivity Hash, related art is defined first
Language:
1, grid cell (C-Cell): have along the X direction, in Y-direction and Z-direction specified long (ISize), wide
(JSize), the rectangular cubic unit of high (KSize), grid cell storage specific value represent its attribute.
2, grid body (G-Grid): the three-dimensional structure being made of many grid cell C, in X-direction, Y-direction and the side Z
To dimension be I × J × K, essence is three-dimensional matrice.G (i, j, k) is meant that X-direction index is equal to i, Y-direction index is equal to
J, Z-direction index is equal to the grid cell of k.
3, training image (TI-TrainImage): priori geologic concept model, using grid body GTIAs data medium,
It is the digital model that can state actual reservoir structure, geometric shape and its distribution pattern.
4, simulated implementation (R-Realization): the model result of simulation, using grid body GRAs data medium, it is
The digital model of actual reservoir structure, geometric shape and its distribution pattern can be stated.
5, data template (T-Template): center cell symmetrical structure body is surrounded, using grid body GTAs data
Carrier is the local digital model that can state actual reservoir structure, combining form and its distribution pattern, is based on pattern
The basic structural unit of Multiple-Point Geostatistics modeling method.
6, Data Styles (Pat-Pattern): the local space number that training image obtains is scanned by frame of data template
According to assembly, using grid body GPatAs data medium.
7, data event (Dev-DataEvent): the local space that simulated implementation obtains is scanned by frame of data template
Data assembly, using grid body GDevAs data medium.
6, it pattern database (PatDB-Pattern Database): can be obtained using data template scanning training image
Take all Data Styles of the training image, referred to as pattern database.
7, the block of Data Styles (event) block grid (BlockGrid-Block Grid): is calculated based on piecemeal frame
The sum of interior all variate-values are the feature vectors of Data Styles (event), are the inputs of p-stable local sensitivity Hash calculation
Data are the core information libraries of LSHSIM modeling algorithm.
8, Data Styles Hash library (PatLSHLib-Pattern LSH Library): it is based on p-stable local sensitivity
Hash technology calculates the cryptographic Hash of Data Styles feature vector, and the Hash table of composition is known as Data Styles Hash library.
9, similar neighbouring pattern database (ANNPatDB-Approximate Nearest Neighbors Pattern
Database): in modeling process, Data Styles group identical with data event cryptographic Hash is retrieved from Data Styles Hash library
At pattern database.
Embodiment 1
Based on the multiple spot geological statistics modeling method of p-stable local sensitivity Hash retrieval Data Styles, as sedimentary facies
The modeling of categorical variable, the specific steps are as follows:
(1) using Fig. 4 a as training image TI, training image illustrates a sedimentary facies model, wherein including two kinds of depositions
Microfacies, black be river microfacies, white be interchannel gulf microfacies, define simulated implementation R size and training image size
Unanimously, data template T is having a size of 15 × 15；
(2) setting multi-block technique BlockGrid is having a size of 5 × 5, then the feature vector dimension of training image is defeated equal to 25
Enter p-stable local sensitivity Hash parameter, wherein the wide w of Hash bucket is equal to 0.01, and Hash table quantity is equal to the dimension of feature vector
25；
(3) training image TI is scanned with data template T, establishes pattern database PatDB；
(4) the sum of the multi-block technique internal variable for counting all Data Styles in pattern database PatDB, obtains training image
Feature vector, as input variable participate in p-stable local sensitivity Hash calculation, establish Data Styles Hash library
PatLSHLib；
(5) random walk is created according to simulated implementation R；
(6) it if there is the node U not simulated in random walk, enters step 7), otherwise enters step 11)；
(7) the data event DataEvent at node U is extracted, using the identical parametric statistics data event of step 4)
Feature vector obtains the cryptographic Hash DevLSH of data event；
(8) all Data Styles identical with DevLSH Hash barrel number, group are retrieved from Data Styles Hash library PatLSHLib
At target patterns database ANNPatDB；
(9) it is searched and the maximum Data Styles Pat of data event Dev similarity value in ANNPatDB.(10) data are used
Pattern Pat is integrally covered and is freezed the part in simulated implementation R at node U, return step 6)；
(11) simulation terminates, and exports simulated implementation R.Fig. 4 b is that a stochastic simulation is realized.
Embodiment 2
The present embodiment is the modeling method of porosity categorical variable, and the operating procedure of this method is substantially the same manner as Example 1,
The difference is that:
The training image of input is Fig. 5 a, and Fig. 5 b is that a stochastic simulation of porosity categorical variable is realized.
Fig. 4 and Fig. 5 respectively shows the method for the present invention in the application of simulation phase model and physical property model.Phase model belongs to
Classified variable, Fig. 4 include two kinds of facies types, and Fig. 4 a is training image --- and fluvial facies model, white and black respectively represent river
Road phase and interchannel gulf, Fig. 4 b are that a stochastic simulation is realized, observe simulated implementation, the spatial distribution and continuity in river obtain
Preferably reproduce.Fig. 5 is the example for simulating continuous categorical variable, and Fig. 5 a is the corresponding porosity model of Fig. 4 fluvial facies model, Fig. 5 b
It is simulated implementation, the porosity distribution characteristics of simulated implementation meets very much with training image.
Two aspect comprehensive analysis advantage of the invention relative to conventional method is occupied from simulated time and memory (table 2)
Place.Fig. 4 a is training image, and dimension is 250 × 250, and grid cell is set having a size of 10m × 10m, the dimension of data template
15 × 15 are set to, selection 200 × 200 and 500 × 500 two dimension of simulated implementation.Referring to the calculating time of table 2, the present invention
The calculating time well below traditional multiple spot geological statistics algorithm such as SIMPAT and Filtersim, it is improved compared to later
DisPat and PSCSIM algorithm also has great advantages.In terms of EMS memory occupation, new method committed memory 36MB is controlled well
Occupancy of the Hash table processed to memory.Comprehensively consider time-consuming and memory, present invention greatly enhances computational efficiencies.
Table 2LSHSIM and other multiple spot geological statistics algorithms calculate time-consuming and EMS memory occupation
Other unspecified parts are the prior art.Although above-described embodiment is made that the present invention and retouches in detail
State, but it is only a part of the embodiment of the present invention, rather than whole embodiments, people can also according to the present embodiment without
Other embodiments are obtained under the premise of creativeness, these embodiments belong to the scope of the present invention.
Claims (3)
1. a kind of multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles, feature exist
In, comprising the following steps:
1) training image TI is inputted, size, the size of data template T of simulated implementation R are defined；
2) it is wide that the size of multi-block technique BlockGrid, the parameter of input p-stable local sensitivity Hash, including Hash bucket are set
W, Hash table quantity N；
3) training image TI is scanned with data template T, establishes pattern database PatDB；
4) the sum of the multi-block technique block internal variable for counting total data pattern, obtains the feature vector of Data Styles, then carries out
P-stable local sensitivity Hash calculation obtains Data Styles Hash library PatLSHLib；Wherein, by multi-block technique data conversion
Just to be inputted as the data of p-stable local sensitivity hash algorithm after linear data structure, the linear data knot of multi-block technique
Structure is indexed with two dimensional data structure index relative
BlockGridi=BlockGridM, n, wherein i=m*NCount+n
Wherein i is the linear directory of multi-block technique, and m and n are the 2-d indexs of multi-block technique, and NCount is multi-block technique vertical
Number of grid on direction；
5) random walk is created according to simulated implementation R；
If 6) have non-analog node U in random walk, into following step 7) path；
Otherwise enter following step 11) path；
7) the data event DataEvent at node U is extracted, the sum of multi-block technique block internal variable of statistical data event obtains
The feature vector of data event carries out p-stable local sensitivity Hash calculation, obtains the cryptographic Hash DevLSH of data event；
8) data identical with data event cryptographic Hash DevLSH Hash barrel number are retrieved from Data Styles Hash library PatLSHLib
Pattern constitutes target data style library ANNPatDB；
9) it is searched and the maximum Data Styles Pat of data event Dev similarity value from target data style library ANNPatDB；
10) it is integrally covered with Data Styles Pat and freezes the part in simulated implementation R at node U；Return to above-mentioned steps 6)；
11) simulation terminates, and inputs simulated implementation R.
2. the multiple spot geological statistics modeling according to claim 1 based on p-stable local sensitivity Hash retrieval Data Styles
Method, it is characterised in that: in the step 4), piecemeal is carried out according to Data Styles, that is, event size of mesh opening, establishes piecemeal net
Lattice count the sum of all nodal values in each piece, and formula is
Wherein, ICount, JCount are the number of grid of training image grid in both the horizontal and vertical directions, MCount,
NCount is the number of grid of multi-block technique in both the horizontal and vertical directions, BlockGridm,nIt is the piecemeal for indexing [m, n]
The sum of interior all nodal values, TIGridi,jIt is the nodal value that [i, j] is indexed in training image grid, sum indicates read group total.
3. the multiple spot geological statistics according to claim 1 based on p-stable local sensitivity Hash retrieval Data Styles are built
Mould method, it is characterised in that: in the step 4), Data Styles have the node of not variate-value, the variable of training image
Minimum value participates in statistics as dummy variable and calculates, and formula is
Wherein, TI is training image, minTIIt is the variable minimum value of training image, var is the variate-value of node in data event,
If some node of data event is empty (NULL), participate in calculating with the variable minimum value replacement null value of training image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610583484.6A CN106294540B (en) | 2016-07-22 | 2016-07-22 | Multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610583484.6A CN106294540B (en) | 2016-07-22 | 2016-07-22 | Multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106294540A CN106294540A (en) | 2017-01-04 |
| CN106294540B true CN106294540B (en) | 2019-07-09 |
Family
ID=57652330
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610583484.6A Active CN106294540B (en) | 2016-07-22 | 2016-07-22 | Multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106294540B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108986217B (en) * | 2017-05-31 | 2021-07-27 | 中国石油化工股份有限公司 | Multipoint geostatistical modeling method based on pattern vector distance |
| CN109102326A (en) * | 2018-07-15 | 2018-12-28 | 山东工业职业学院 | A kind of cloud food and drink platform and analysis method based on big data signature analysis |
| CN112861331B (en) * | 2021-01-28 | 2022-02-25 | 西南石油大学 | Method for rapidly constructing coefficient matrix of oil and gas reservoir numerical simulator |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104850682A (en) * | 2015-04-17 | 2015-08-19 | 长江大学 | Multiple-point geostatistics modeling method based on position |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6164899B2 (en) * | 2013-04-05 | 2017-07-19 | キヤノン株式会社 | Hash value generation device, system, determination method, program, storage medium |
-
2016
- 2016-07-22 CN CN201610583484.6A patent/CN106294540B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104850682A (en) * | 2015-04-17 | 2015-08-19 | 长江大学 | Multiple-point geostatistics modeling method based on position |
Non-Patent Citations (3)
| Title |
|---|
| Digital Elevation Data Fusion Using Multiple-Point Geostatistical Simulation;Yunwei Tang et.al;《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》;20151031;第8卷(第10期);第4922-4934页 |
| 利用Simpat模拟河流相储层分布;尹艳树 等;《西南石油大学学报（自然科学版）》;20080430;第30卷(第2期);第19-22页 |
| 地质统计学反演-从两点到多点;杨培杰;《地球物理学进展》;20141231;第29卷(第5期);第2293-2300页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106294540A (en) | 2017-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109345619B (en) | Mass point cloud space management method based on octree-like coding | |
| CN105513127B (en) | Shaft regularization three-dimensional modeling method and system based on density peaks cluster | |
| CA2846327C (en) | Systems and methods for generating a large scale polygonal mesh | |
| CN108428015B (en) | Wind power prediction method based on historical meteorological data and random simulation | |
| CN104346481B (en) | A kind of community detection method based on dynamic synchronization model | |
| WO2019019653A1 (en) | Device and method for extracting topographical boundary | |
| CN106294540B (en) | Multiple spot geological statistics modeling method based on p-stable local sensitivity Hash retrieval Data Styles | |
| CN102890703A (en) | Network heterogeneous multidimensional scaling (HMDS) method | |
| CN106326923A (en) | Sign-in position data clustering method in consideration of position repetition and density peak point | |
| CN110147775A (en) | Utilize refinement method of the space separation method from data reduction indoor navigation element | |
| CN110363299A (en) | Space reasoning by cases method towards delamination-terrane of appearing | |
| CN110674326A (en) | Neural network structure retrieval method based on polynomial distribution learning | |
| Limbach et al. | Detection, tracking and event localization of jet stream features in 4-D atmospheric data | |
| CN107818338A (en) | A kind of method and system of building group pattern-recognition towards Map Generalization | |
| CN115713605A (en) | Commercial building group automatic modeling method based on image learning | |
| CN105138607B (en) | A kind of KNN querying methods based on combination grain distributed memory grid index | |
| CN104537254B (en) | A kind of drafting method that becomes more meticulous based on social statistics data | |
| CN106227929A (en) | Based on anisotropic non-stationary modeling method | |
| CN106780747B (en) | A kind of method that Fast Segmentation CFD calculates grid | |
| CN106652032B (en) | A kind of parallel contour lines creation method of DEM based on Linux cluster platform | |
| CN105426626B (en) | Multiple-Point Geostatistics modeling method based on set of metadata of similar data pattern cluster | |
| CN116011564A (en) | Entity relationship completion method, system and application for power equipment | |
| CN108052778A (en) | For the proximate particle efficient double searching method of mesh free particle simulation technology | |
| CN109241628A (en) | Three-dimensional CAD model dividing method based on Graph Spectral Theory and cluster | |
| CN108986217B (en) | Multipoint geostatistical modeling method based on pattern vector distance |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |