CN106407678B - One kind being based on phased nonparametric anisotropy variogram construction method - Google Patents
One kind being based on phased nonparametric anisotropy variogram construction method Download PDFInfo
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Abstract
The present invention discloses a kind of based on phased nonparametric anisotropic variogram construction method, different anisotropy variogram fittings is carried out based on stratum difference sedimentary facies, the structure to exponential type variogram and solution are realized, is mutually modeled, is had the following advantages by rational：Compared with traditional variogram building mode, the variogram that the present invention fits closer to true geological condition, effectively prevent because variogram build it is unreasonable due in subsequent stochastic simulation work caused by error；The present invention establish it is a kind of based on the point of inverting data to randomly selecting method, by all angles interval point pair randomly select be used as variogram structure when parameter input, can preferably react the anisotropic character of variogram；The present invention proposes a kind of variogram parameter fitness method based on ant group algorithm, and by that by the conversion of problem, can improve the accuracy of variogram parameter fitting, technical guarantee is provided for later work.
Description
Technical field
The present invention relates to geostatistics field, more particularly to a kind of variogram construction method.
Background technology
The main tool of stochastic modeling is geostatistics, and geostatistics are to go statistic sampling data using variogram
Between spatial relationship rule, a kind of statistics for simulating the value of stochastic variable at non-sampling location is then gone using this statistical law
Method.For geostatistics on the basis of the spatial distribution structure characteristic rule of survey region variable, it is various suitable to select
Ordinary kringing method, with achieve the purpose that it is more accurate estimation or to regionalized variable carry out condition simulation.It is big that it generally comprises three
Element：Correlation analysis, Kriging estimation and the condition simulation of spatial function.Spatial function correlation analysis is pair
The analysis of variogram and covariance function, includes their definition and estimate variance, it is Kriging estimation and condition simulation
Basis.
Since geostatistics come out, to the Spatial Variability regionalized variable in relation to resource information in geoscience
Theory is characterized, and is not only grown a lot to Geological Prediction and Assessment theory, also to many non-geological sciences such as water and soil resources,
Hydrometeorology, biotic environment and engineering technology produce extensive influence, have been formed at present to spatial information distribution characteristics or mould
The research of quasi- its discreteness and fluctuation, available geological statistics theory carries out, and geostatistics also have become assessment
Various regionality natural phenomenas, natural resources and the new engineering science for reproducing its fluctuation process.
A basic tool of the variogram as geostatistics, is the key factor of stochastic modeling, main function
It is the spatial relationship structure for portraying space variable, people can be helped to analyze reservoir characteristic, obtain feature in modeling.Variation letter
Number can fully describe reservoir parameter spatial coherence, under different sedimentary conditions, each not phase of the spatial distribution characteristic of reservoir
Together, the especially stronger area of anisotropism, with greater need for variogram come the accurate anisotropic feature for estimating reservoir.Therefore
The structure of variogram is stochastic simulation and the core technology of stochastic inverse.
Variogram
Studied object is indicated in geostatistics with regionalized variable.Regionalized variable is with the three of spatial point X
Tie up rectangular co-ordinate (xu,xv,xw) be independent variable random field Z (xu,xv,xw)=Z (x), it is distinctive double that it reflects Geological Variable
Weight feature, i.e. random character and spatial structure characteristic.
Variogram at point x is defined as the variance half of the difference at a distance of the compartmentalization stochastic variable for h at x, is denoted as
γ (x, h), i.e.,
R (x, h)=0.5*D2[Z(x)-Z(x+h)]
=0.5*E [Z (x)-Z (x+h)]2-0.5*{E[Z(x)-Z(x+h)]}2 (1-1)
There are three basic parameters for variogram：Become journey a, block gold number c0And base station value c.
1. becoming journey
Become the coverage that journey a reflects regionalized variable, there is correlation within the scope of becoming journey, and with distance
Increase correlation gradually to weaken, does not have correlation then beyond the range.
2. block gold number
Block gold number c0=γ (0), theoretically the value of variogram at the origin should be zero, but due to the error of measurement and
Microcosmic variability so that larger variation occur between random areas variable within the distance of very little.
3. base station value
Base station value reflects variation intensity of the regionalized variable within the scope of survey region.
Variogram can describe the feature of Geological Variable well, such as become the coverage that journey reflects variable, base station value
The variation intensity of variable is reflected, variogram is reflected in the slope of zero crossings the initial segment in smaller distance, variable
Variation be violent or gentle, the variation diagram on different directions can reflect anisotropy of variable etc.[1]。
Stochastic simulation
The structure of variogram is the premise for carrying out stochastic simulation.Stochastic modeling refers to being used according to the information being collected into
Random function is theoretical, using Method of Stochastic, the method to generate optional, equiprobable, high-precision reservoir model.
This method recognizes that the reservoir parameter other than control point has certain uncertainty, that is, has certain randomness.Its flow
As shown in Figure 1.
Invention content
The present invention is in order to solve the above technical problems, propose a kind of based on phased nonparametric anisotropy variogram structure
Method builds nonparametric anisotropy variogram respectively in conjunction with the different sedimentary facies phase in work area in different sedimentary facies, fitting
A kind of mode based on ant group algorithm is proposed when parameter, can preferably be estimated variogram and be corresponded to parameter.It is further
It simulates work and good technical support is provided.
The technical solution adopted by the present invention is：One kind being based on phased nonparametric anisotropy variogram construction method, packet
It includes：
S1, work area sedimentary facies division divide work area sedimentary facies using instruction simulation；
The variogram of S2, the different sedimentary facies of structure, specifically include it is following step by step：
S21, initialization：Import inverting good Acoustic Impedance Data and log data；
S22, Facies Control Modeling：Facies Control Modeling is carried out using PETREL software needle log datas, obtains the mark that each pair of point is answered
Sign phase_label；
S23, decile is carried out to angle, setting angular interval quantity is M deciles, and note etc. divides M=degree_num, interval note
For θ, θ=360/M, then n-th of angular interval correspond to slope be [arctan ((n-1) θ), arctan (n θ))；
S24, it randomly selects a little pair, the point under different labels is classified, any two points random groups synthetic point pair will be every
Group point is to all into line label；
The slope of point pair selected by S25, calculating, according to its value by the point to being put into corresponding angular interval；
S26, S2 3 to step S2 5 is respectively repeated steps to each sedimentary facies, until all angles section of each sedimentary facies
There are enough sampled points；
S27, variogram is calculated separately according to the sampled data under different sedimentary facies；
S3, fitting variogram parametric solution, including：
S31, to candidate solution { x1,x2... } and each variable xiBinary system sequence { the b for being N with word lengthNbN-1…b1b0Into
Row coding, is decoded according to following formula：
Wherein, { 0,1 } b ∈, j=1,2 ... N, bN-1For highest order, b0For lowest order, variable xiLeft margin be real number value
ximin, right margin is real number value ximax, the left margin of the corresponding decimal integer value of z expressions binary system sequence is real number value；
S32, it will wait for that fitting parameter is converted into the form of digraph；
S33, parameter fitting is carried out using exponential model.
Further, further include before the step S1：Instruction transformation, specially：According to threshold value, what is continuously determined
Initial data is separated into Boolean quantity 0 or 1.
Further, the step S32 is specially：
Digraph G=(C, L) is defined, wherein vertex set C is
Wherein, vsFor starting point, vertexWithPosition b in binary system sequence is indicated respectivelyjThe state that value is 0 and 1, j
=1,2 ... N, c0(vs),Indicate the element in vertex set C, e=1,2 ..., 2N.
Further, the step S33 specifically include it is following step by step：
S331, nc=0, each τijWith Δ τijInitialization, m ant is placed on n vertex；
Wherein, nc is iterative steps or searching times, τijFor t moment on i, j lines remaining information content,For ant
The unit length trace information prime number amount that ant k leaves on side arc (i, j)；
S332, the initial starting point of each ant is placed in current solution concentration, to each ant k (k=1 ..., m), by probabilityMove to next vertex j；Vertex j is placed in current disaggregation；
S333, the target function value Z for calculating each antk, record is so that target function value ZkMinimum solution；K=1 ... m；
S334, Path is set*(t) it is the optimal path in the t search cycles, the corresponding target function value of the optimal path is
f*(t), vertex i corresponds to Κ of candidate solution in side arc (i, j), then the pheromones of ant colony search update according to the following formula
Wherein, f*(t+1) the corresponding target function value of optimal path in the t+1 search cycles, τ are indicatedij(t, k) is indicated
The pheromones of t moment, τij(t+1, k) indicates that the pheromones at t+1 moment, L are the binary-coded code length of candidate solution, is
Positive integer, k indicate k-th of ant；
S335, to each arc side (i, j), set Δ τij=0；Nc=nc+1；
If S336, nc<Scheduled iterations, go to step S332.
Beneficial effects of the present invention：The present invention is based on stratum difference sedimentary facies, to carry out different anisotropy variograms quasi-
It closes, realizes the structure to exponential type variogram and solution, mutually modeled, had the following advantages by rational：
(1) variogram constructed by the present invention is the anisotropy variogram based on different sedimentary facies, and traditional
Variogram building mode is compared, and the variogram that the present invention fits is effectively prevented closer to true geological condition
Because variogram build it is unreasonable due in subsequent stochastic simulation work caused by error；
(2) present invention establish it is a kind of based on the point of inverting data to randomly selecting method, by all angles section
The parameter input of point pair randomly selected when being built as variogram, the anisotropy that can preferably react variogram are special
Sign；
(3) present invention proposes a kind of variogram parameter fitness method based on ant group algorithm, passes through turning problem
Change, the accuracy of variogram parameter fitting can be improved, technical guarantee is provided for later work.
Description of the drawings
Fig. 1 is the flow chart of stochastic simulation.
Fig. 2 is the structure flow chart of variogram provided by the invention.
Specific implementation mode
For ease of those skilled in the art understand that the present invention technology contents, below in conjunction with the accompanying drawings to the content of present invention into one
Step is illustrated.
It is the solution of the present invention flow chart as shown in Figure 1, the technical scheme is that：One kind is each based on phased nonparametric
Anisotropy variogram construction method, including：
S1, work area sedimentary facies division divide work area sedimentary facies using instruction simulation；Instruction simulation is to divide work area
The effective ways of sedimentary facies.Before being simulated, the application will first carry out instruction transformation, i.e., according to a series of threshold values,
The initial data continuously determined is separated into the process of a Boolean quantity 0 or 1.If { Z (xα), α=1 ..., N } it is studied storage
Collect one group of observation data on layer, N expressions have carried out n times observation, obtained N number of different value, give a threshold value Z, can be institute
Some observation data conversions are at indicated value I (xα；Z)；
Then with x0Centered on the areas Dai Gu regionalized variable I*The probability Estimation of (x, Z)For
Weight coefficient λ in formulaαThe estimated value in region to be estimated can may finally be acquired by indicating that Kriging equation groups are found out
Z*(X)；
Weight coefficient λ in formulabIt can be by indicating that Kriging equation groups are found out, Z (X) indicates regional change amount, is one random
Variable.
The division of sedimentary facies can be carried out in the method for Selection utilization instruction simulation in PETREL softwares.It is surveyed importing work area
After well data, PETREL softwares can be clustered according to log data, and under normal circumstances, three kinds of different sedimentary facies can meet
It needs, therefore, adjusts be divided into three classes manually, select instruction simulation mode to model it later, so that it may to obtain work
Corresponding phase label is each put in area, the label of each point is stored in the matrix of phase_label, label value is taken as
1,2,3.
The variogram of S2, the different sedimentary facies of structure, traditional variogram are built just with log data, no
Variogram curve can be fitted well.The present invention is proposed using the good data of inverting using the data compared with horn of plenty
A kind of point is to randomly selected method, for different sedimentary facies labels, carries out the structure of variogram respectively, as shown in Fig. 2,
Specifically include it is following step by step：
S21, initialization：Import inverting good Acoustic Impedance Data and log data；
S22, Facies Control Modeling：Facies Control Modeling is carried out using PETREL software needle log datas, obtains the mark that each pair of point is answered
Sign phase_label；
S23, decile is carried out to angle, setting angular interval quantity is M deciles, and M=degree_num, interval is denoted as θ, θ
=360/M, then n-th of angular interval correspond to slope be [arctan ((n-1) θ), arctan (n θ))；
S24, it randomly selects a little pair, the point under different labels is classified, any two points random groups synthetic point pair will be every
Group point is to all into line label；The application is that every group of point is random to generating one using rsenne Twister randomizers
Number, rsenne Twister randomizer recurrence formula are
Wherein, initial value is (x0,x1,...,xn-1), it is a n dimension row vector, each x=x(w)x(w-1)...x(0)It is
W binary representations, the left side are a high position；Indicate xsPreceding w-r (u upper),Indicate xs+1Rear r (l is
Lower),It is by xsPreceding w-r and xs+1Rear r composition the new positions w data；1≤m<N, r are Character segmentation
Point, 0≤r≤w-1；" | " is or operator, and " ⊕ " is xor operator, ">>" it is to move to right, "<<" it is to move to left；A is MT algorithms
Convolution transformation matrix, size be w × w dimension；“:=" it is a kind of representation, mean that definition is equal to.
N group points are selected to be inputted to being calculated as variogram to being ranked up point according to random number size.
The slope of point pair selected by S25, calculating, according to its value by the point to being put into corresponding angular interval；
S26, S2 3 to step S2 5 is respectively repeated steps to each sedimentary facies, until all angles section of each sedimentary facies
Sampled point number is greater than or equal to N；Judged according to the angular interval size of division, with regard to more one if angular interval is larger
A bit, it can lack a little if smaller, sampled point number N is set in the application as 20 or so.
S27, variogram is calculated separately according to the sampled data under different sedimentary facies；
S3, fitting variogram parametric solution, although having been presented for the approximating method of a variety of variation functions at present, these
Method has used many inconvenient and disadvantages.If weighted polynomial fitting process relies heavily on the experience of geological personnel,
The determination of weight coefficient is also more difficult；Linear programming technique is only seeking multinomial coefficient side compared with weighted polynomial regression method
It improves to some extent in method, does not account for Weighted problem；Goal programming rule is again too complicated；Weighted linear law of planning considers difference
Lag is weighted away from gained experimental variations functional value under h, it is ensured that and the success of fitting has been carried out at the same time manual intervention, but
This method proposes that object function is too complicated on weighted polynomial fitting process and linear programming technique, it is difficult to solve.The present invention
The characteristics of according to these approximating methods, it is proposed that a method of being fitted variogram parameter with ant group algorithm.Including：
S31, to candidate solution { x1,x2... } and each variable xiBinary system sequence { the b for being N with word lengthNbN-1…b1b0Into
Row coding, is decoded according to following formula：
Wherein, { 0,1 } b ∈, j=1,2 ... N, bN-1For highest order, b0For lowest order, variable xiLeft margin be real number value
ximin, right margin is real number value ximax, the left margin of the corresponding decimal integer value of z expressions binary system sequence is real number value；
S32, it will wait for that fitting parameter is converted into the form of digraph；Digraph G=(C, L) is defined, wherein vertex set C is
vsFor starting point, vertexWithPosition b in binary system sequence is indicated respectivelyjThe state that value is 0 and 1, j=0,1,
2 ..., N, c1It is actual vertex, in bracketIndicate that its generating mode, subscript 0,1 indicate its two states, subscript
It indicates since N, then directed arc L is
That is, for j=2,3 ... N, on all vertexWithPlace has and is only directed toward respectivelyTwo it is oriented
Arc；
S33, parameter fitting is carried out using exponential model, formula is
Wait for that fitting parameter is a and c.
A ∈ (40,100), c ∈ (8000,16000) can be obtained by experiment variogram, it is carried out respectively by step S32
Encode and be converted into the form of digraph G.Then for problem
Its minimum problems can be converted into the ant group algorithm minimal path Solve problems using parameter a and c as figure, wherein
γ*(hi) it is corresponding experiment variogram lag away from for hiFunctional value.Its solution procedure is as follows：
S331, nc=0, each τijWith Δ τijInitialization, m ant is placed on n vertex；
Wherein, nc is iterative steps or searching times, τijFor t moment on i, j lines remaining information content,For ant
The unit length trace information prime number amount that ant k leaves in arc side (i, j)；
S332, the initial starting point of each ant is placed in current solution concentration, to each ant k, k=1 ..., m, by probabilityMove to next vertex j；Vertex j is placed in current disaggregation；
S333, the target function value Z for calculating each antk, record is so that target function value ZkMinimum solution；
S334, Path is set*(t) it is the optimal path in the t search cycles, the corresponding target function value of the optimal path is
f*(t), vertex i corresponds to Κ of candidate solution in arc side (i, j), then the pheromones of ant colony search update according to the following formula
Wherein, f*(t+1) the corresponding target function value of optimal path in the t+1 search cycles, τ are indicatedij(t, k) is indicated
The pheromones of t moment, τij(t+1, k) indicates that the pheromones at t+1 moment, L are the binary-coded code length of candidate solution, is
Positive integer, k indicate k-th of ant；
S335, to each arc side (i, j), set Δ τij=0；Nc=nc+1；
If S336, nc<Scheduled iterations, go to step S332.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.For ability
For the technical staff in domain, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made by
Any modification, equivalent substitution, improvement and etc. should be included within scope of the presently claimed invention.
Claims (4)
1. one kind being based on phased nonparametric anisotropy variogram construction method, which is characterized in that including：
S1, work area sedimentary facies division divide work area sedimentary facies using instruction simulation；
The variogram of S2, the different sedimentary facies of structure, specifically include it is following step by step：
S21, initialization：Import inverting good Acoustic Impedance Data and log data；
S22, Facies Control Modeling：Facies Control Modeling is carried out for log data using PETREL softwares, obtains the label that each pair of point is answered
phase_label；
S23, decile being carried out to angle, setting angular interval quantity is M deciles, and note etc. divides M=degree_num, interval to be denoted as θ,
θ=360/M, then it is [arctan ((n-1) θ), arctan (n θ)] that n-th of angular interval, which corresponds to slope,；
S24, it randomly selects a little pair, the point under different labels is classified, any two points random groups synthetic point pair, by every group of point
To all into line label；
The slope of point pair selected by S25, calculating, according to its value by the point to being put into corresponding angular interval；
S26, S23 to step S25 is respectively repeated steps to each sedimentary facies, until all angles section of each sedimentary facies has enough
Sampled point；
S27, variogram is calculated separately according to the sampled data under different sedimentary facies；
S3, fitting variogram parametric solution, including：
S31, to candidate solution { x1,x2... } and each variable xiBinary system sequence { the b for being N with word lengthNbN-1…b1Encoded,
It is decoded according to following formula：
Wherein, bj∈ { 0,1 }, j=1,2 ... N, bNFor highest order, b1For lowest order, variable xiLeft margin be real number value ximin,
Right margin is real number value ximax, the left margin of the corresponding decimal integer value of z expressions binary system sequence is real number value；
S32, it will wait for that fitting parameter is converted into the form of digraph；
S33, parameter fitting is carried out using exponential model.
2. according to claim 1 a kind of based on phased nonparametric anisotropy variogram construction method, feature exists
In the step S1 further includes before：Instruction transformation, specially：According to threshold value, the initial data continuously determined is separated into
Boolean quantity 0 or 1.
3. according to claim 1 a kind of based on phased nonparametric anisotropy variogram construction method, feature exists
In the step S32 is specially：
Digraph G=(C, L) is defined, wherein vertex set C is
Wherein, L is directed arc, vsFor starting point, vertexWithPosition b in binary system sequence is indicated respectivelyjThe shape that value is 0 and 1
State, j=1,2 ... N, c0(vs),Indicate the element in vertex set C, e=1,2 ..., 2N.
4. according to claim 1 a kind of based on phased nonparametric anisotropy variogram construction method, feature exists
In, the step S33 specifically include it is following step by step：
S331, nc=0, each τijWithInitialization, m ant is placed on n vertex；
Wherein, nc is iterative steps or searching times, τijFor t moment on i, j lines remaining information content,For ant k
The unit length trace information prime number amount left on side arc (i, j)；
S332, the initial starting point of each ant is placed in current solution concentration, to each ant k, k=1 ..., m, by probabilityIt moves
To next vertex j；Vertex j is placed in current disaggregation；
S333, the target function value Z for calculating each antk, record is so that target function value ZkMinimum solution；K=1 ... m；
S334, Path is set*(t) it is the optimal path in the t search cycles, the corresponding target function value of the optimal path is f*
(t), vertex i corresponds to K of candidate solution in side arc (i, j), then the pheromones of ant colony search update according to the following formula
Wherein, f*(t+1) the corresponding target function value of optimal path in the t+1 search cycles, τ are indicatedijWhen (t, k) indicates t
The pheromones at quarter, τij(t+1, k) indicates the pheromones at t+1 moment, and L is the binary-coded code length of candidate solution, for just
Integer, k indicate k-th of ant；
S335, to each arc side (i, j), setNc=nc+1；
If the scheduled iterations of S336, nc ＜, go to step S332.
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