CN106803010A - For the Fuzzy Grey comprehensive evaluation method and device of low permeability reservoir quantitative assessment - Google Patents
For the Fuzzy Grey comprehensive evaluation method and device of low permeability reservoir quantitative assessment Download PDFInfo
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Abstract
The invention discloses a kind of Fuzzy Grey comprehensive evaluation method and device for low permeability reservoir quantitative assessment, belong to low permeability reservoir quantitative assessment technical field.The method initially sets up evaluate collection, and then determine the degree of membership Evaluations matrix of the fuzzy evaluating data of connection and grey evaluation data, then according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determine fuzzy evaluation value, according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determine grey evaluation value, finally according to the fuzzy evaluation value and the first weight coefficient sum of products described in grey evaluation value and the second weight coefficient product, determine the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, realize the quantitative assessment of low permeability reservoir, the ambiguity and grey majorized model of low permeability reservoir information are taken into account, uncertainty of the low permeability reservoir in evaluation procedure is well adapted to, improve the degree of accuracy of low permeability reservoir quantitative assessment.
Description
Technical field
The present embodiments relate to oil-gas exploration and development technique field, and in particular to low permeability reservoir quantitative assessment technology
Field, more particularly to a kind of Fuzzy Grey comprehensive evaluation method and device for low permeability reservoir quantitative assessment.
Background technology
Evaluating reservoir runs through oil-gas exploration and development process, is the important evidence that oil gas field carries out science decision deployment.Pin
To Quantitative Evaluation of Reservoirs, many scholars do a lot of work, and wherein majority is that being determined property of classification standard is set up to reservoir parameter
Evaluate, evaluation method is based on simple step analysis, factorial analysis, cluster analysis.And as low permeability reservoir progressively turns into
Research emphasis, because geological conditions becomes complicated, reservoir heterogeneity enhancing etc., it is often difficult to provide clear in quantitative assessment
Clear clear and definite standard judges and sets up explicit relational expression, shows larger uncertain and non-linear.
It is current be still to Quantitative Evaluation of Reservoirs based on fuzzy or grey single method, it is relatively simple, take into account fuzzy
Property, the Model for Comprehensive of grey majorized model are also less.
Simultaneously larger uncertainty is faced in low permeability reservoir geologic assessment：One side low permeability reservoir is more multiple in itself
Miscellaneous, often criteria for classification is indefinite, judges and relatively obscures；The input degree of another aspect low permeability reservoir is relatively low, can obtain data
Data are relatively fewer, and support evaluation information is insufficient, shows grey majorized model.Therefore the immature, evaluation information of reservoir understanding is not complete
The limitation of the subjective and objective condition such as standby brings certain difficulty to the appraisal of low permeability reservoir, also governs low permeability oil and gas field
The paces of Speeding up development.Compared to conventional reservoir evaluation, low permeability reservoir quantitative assessment will not only have comprehensive in evaluation method
Conjunction property and objectivity feature.Therefore, how to realize the quantitative assessment of low permeability reservoir becomes a technology hardly possible urgently to be resolved hurrily
Topic.
The content of the invention
In order to solve problem of the prior art, the embodiment of the invention provides a kind of for low permeability reservoir quantitative assessment
Fuzzy Grey comprehensive evaluation method and device, for while the ambiguity and grey majorized model of low permeability reservoir information is taken into account,
The uncertainty of low permeability reservoir quantitative assessment is reduced, the reliability of low permeability reservoir quantitative assessment is improved.The technical scheme
It is as follows：
In a first aspect, the embodiment of the present invention provides a kind of Fuzzy Grey Comprehensive Evaluation for low permeability reservoir quantitative assessment
Method, methods described includes：
The objective reality and the data information of the low permeability reservoir evaluated according to low permeability reservoir, set up evaluate collection, institute
Stating evaluate collection includes set of factors, Comment gathers and weight sets；
Determine degree of membership Evaluations matrix, the degree of membership Evaluations matrix is used to connect fuzzy evaluating data and grey evaluation number
According to；
According to the Comment gathers, the weight set and the degree of membership Evaluations matrix, fuzzy evaluation value is determined；
According to the Comment gathers, the weight set and the degree of membership Evaluations matrix, grey evaluation value is determined；
Grey evaluation value described in sum of products according to the fuzzy evaluation value and the first weight coefficient and the second weight coefficient
Product, determine the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, wherein, first weight coefficient with it is described
Second weight coefficient and equal to 1.
Optionally, the determination degree of membership Evaluations matrix, including：
The degree of membership of the various comments in determining each factor in the set of factors to the evaluate collection；
Each factor in the set of factors to the evaluate collection in various comments degree of membership, determine the person in servitude
Category degree Evaluations matrix, wherein, the degree of membership Evaluations matrix isR in formulaijRepresent
Degree of membership of i-th factor to jth kind comment in evaluation object.
Optionally, it is described according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, it is determined that fuzzy comment
Value, including：
The weight sets and the degree of membership Evaluations matrix are carried out into the comprehensive person in servitude that fuzzy composition computing obtains evaluation object
Category degree；
Comprehensis pertaining normalization is obtained into the fuzzy evaluation value as weights with the Comment gathers weighted sum.
Optionally, it is described that Comprehensis pertaining normalization is obtained described as weights and the Comment gathers weighted sum
Fuzzy evaluation value, including：
According to formulaDetermine the fuzzy evaluation value, wherein, K
It is the number of evaluation object, p is resolution ratio, vjIt is j-th evaluation approach in the Comment gathers, It is k-th evaluation object to the Comprehensis pertaining of the jth kind comment in the Comment gathers.
Optionally, it is described according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determine that grey is commented
Value, including：
The degree of membership Evaluations matrix is synthesized as weights with the Comment gathers, the dimensionless of evaluation object is obtained
Comparative sequences；
Optimal comment and most bad comment in the Comment gathers determine optimal reference sequences and most bad reference sequences；
The dimensionless comparative sequences and the optimal reference sequences and described most bad are determined according to Deng Shi degree of association methods
The incidence coefficient of each factor in reference sequences, and then determine optimal grey relational grade and most bad grey relational grade；
The optimal grey relational grade and the most bad grey relational grade are carried out into reinforcing contrast, the grey evaluation is obtained
Value.
Second aspect, the embodiment of the present invention provides the Fuzzy Grey Comprehensive Evaluation dress for low permeability reservoir quantitative assessment
Put, described device includes：
Module is built, for the objective reality and the data information of the low permeability reservoir evaluated according to low permeability reservoir,
Evaluate collection is set up, the evaluate collection includes set of factors, Comment gathers and weight sets；
First determining module, for determining degree of membership Evaluations matrix, the degree of membership Evaluations matrix is used to connect fuzzy commenting
Valence mumber evidence and grey evaluation data；
Second determining module, for according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, it is determined that
Fuzzy evaluation value；
3rd determining module, for according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, it is determined that
Grey evaluation value；
4th determining module, for grey evaluation described in the sum of products according to the fuzzy evaluation value and the first weight coefficient
Value and the product of the second weight coefficient, determine the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, wherein, described the
One weight coefficient and second weight coefficient and equal to 1.
Optionally, first determining module includes：
First determination sub-module, for determining each factor in the set of factors to the evaluate collection in various comments
Degree of membership；
First treatment submodule, for each factor in the set of factors to the evaluate collection in it is each
The degree of membership of comment is planted, the degree of membership Evaluations matrix is determined, wherein, the degree of membership Evaluations matrix isR in formulaijDegree of membership of i-th factor to jth kind comment in expression evaluation object.
Optionally, second determining module includes：
Second synthesis submodule, obtains for the weight sets to be carried out into fuzzy composition computing with the degree of membership Evaluations matrix
To the Comprehensis pertaining of evaluation object；
Second determination sub-module, for the Comprehensis pertaining to be normalized as weights and the Comment gathers weighted sum
Obtain the fuzzy evaluation value.
Optionally, second determination sub-module specifically for：
According to formulaDetermine the fuzzy evaluation value, wherein, K
It is the number of evaluation object, p is resolution ratio, vjIt is j-th evaluation approach in the Comment gathers, It is k-th evaluation object to the Comprehensis pertaining of the jth kind comment in the Comment gathers.
Optionally, the 3rd determining module specifically for：
The degree of membership Evaluations matrix is synthesized as weights with the Comment gathers, the dimensionless of evaluation object is obtained
Comparative sequences；
Optimal comment and most bad comment in the Comment gathers determine optimal reference sequences and most bad reference sequences；
The dimensionless comparative sequences and the optimal reference sequences and described most bad are determined according to Deng Shi degree of association methods
The incidence coefficient of each factor in reference sequences, and then determine optimal grey relational grade and most bad grey relational grade；
The optimal grey relational grade and the most bad grey relational grade are carried out into reinforcing contrast, the grey evaluation is obtained
Value.
The beneficial effect that technical scheme provided in an embodiment of the present invention is brought is：
Method provided in an embodiment of the present invention, the objective reality evaluated according to low permeability reservoir first and hyposmosis storage
The data information of layer, sets up evaluate collection, and then determine that the fuzzy evaluating data of connection and the degree of membership of grey evaluation data evaluate square
Battle array, then according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determines fuzzy evaluation value, according to institute
Comment gathers, the weight set and the degree of membership Evaluations matrix are stated, grey evaluation value is determined, finally according to the fuzzy evaluation
Grey evaluation value described in the sum of products of value and the first weight coefficient and the product of the second weight coefficient, determine that low permeability reservoir is quantified
The Fuzzy Grey comprehensive evaluation value of evaluation.The method of the embodiment of the present invention comments Fuzzy Comprehensive Evaluation method and grey correlation
Valency method combines, and realizes the quantitative assessment of low permeability reservoir, taken into account low permeability reservoir information ambiguity and
Grey majorized model, has well adapted to uncertainty of the low permeability reservoir in evaluation procedure, improves low permeability reservoir quantitative assessment
The degree of accuracy.
Brief description of the drawings
Technical scheme in order to illustrate more clearly the embodiments of the present invention, below will be to that will make needed for embodiment description
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings
Accompanying drawing.
Fig. 1 is a kind of Fuzzy Grey Comprehensive Evaluation side for low permeability reservoir quantitative assessment provided in an embodiment of the present invention
The schematic flow sheet of method；
Fig. 2 is the hypomere reservoir composite columnar section of Soviet Union Sulige gas field Soviet Union X wellblocks box 8；
Corresponding relation figure between Tu3Wei ridges shape distribution membership function curve and formula；
Fig. 4 is distributed for the degree of membership of relatively each opinion rating of sand thickness parameter；
Fig. 5 is a kind of comprehensive judge device of Fuzzy Grey for low permeability reservoir quantitative assessment provided in an embodiment of the present invention
Structural representation；
Fig. 6 is the structural representation of the first determining module 520 in Fig. 5；
Fig. 7 is the structural representation of the second determining module 530 in Fig. 5.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing to embodiment party of the present invention
Formula is described in further detail.
Fig. 1 is a kind of Fuzzy Grey Comprehensive Evaluation side for low permeability reservoir quantitative assessment provided in an embodiment of the present invention
Method, referring to Fig. 1, the method can include following several steps：
Step 101：The objective reality and the data information of the low permeability reservoir evaluated according to low permeability reservoir, foundation are commented
Valency collection, the evaluate collection includes set of factors, Comment gathers and weight sets.
Wherein, the objective reality and data information that low permeability reservoir is evaluated can be obtained by methods such as geological prospectings, this hair
Bright embodiment is not limited this.
It is the base that low permeability reservoir is evaluated that objective reality and data information according to low permeability reservoir evaluation set up evaluate collection
Plinth.Evaluate collection is made up of set of factors, Comment gathers and weight sets.Wherein, set of factors is assessment indicator system, the index of evaluating reservoir
Have a lot, its selection need to defer to certain principle, example, such as principle of whole, scientific principle, principle of comparability, practicality
Property principle etc., while to meet objective reality, it is ensured that the availability of data, reliability, for the specific choosing of the factor of set of factors
Take, the embodiment of the present invention is not done tool and limited, those skilled in the art refer to prior art.Comment gathers are also referred to as opinion rating, can
Qualitatively described in order with being one group, such as (fine, good, medium, poor, very poor) or (one-level, two grades, three-level), it is also possible to
Be one group it is quantitative have numerical sequence, such as (0.8,0.6,0.4,0.2) etc., general evaluation grade classification is 3-5, certainly, herein
It is merely illustrative of, the Comment gathers for not representing the embodiment of the present invention are confined to this.Weight sets is for each in reflected appraisal factor
The correlation and significance level of reservoir index, common Weight Determination have Delphi method, Principal Component Analysis Method, step analysis
Method, entropy assessment, feature vector method etc., wherein preferably, weight sets is determined according to entropy assessment, because entropy assessment is from comentropy
Angle sets out according to index variation size to determine weight, can avoid the randomness for artificially judging to bring, relatively more objective.
Example, the set of factors that finally objective reality according to low permeability reservoir evaluation and data information are set up can be designated as：U
={ u1,u2,...,un, wherein u1,u2,...,unIt is n factor of evaluation.
Example, the Comment gathers that finally objective reality according to low permeability reservoir evaluation and data information are set up can be designated as：V
={ v1,v2,...,vm, wherein v1,v2,...,vmIt is m evaluation approach.
Explanation is needed, entropy assessment computing formula is：
Wherein K is the number for evaluating sample object, xkiIt is k-th evaluation object, i-th kind of sample data of index, EiIt is
The comentropy of i kind indexs.
The final weight sets set up according to the objective reality and data information of low permeability reservoir evaluation can be designated as：A=
{a1,a2,...,an, wherein a1,a2,...,anIt is n weighted value.
Step 102：Determine degree of membership Evaluations matrix, the degree of membership Evaluations matrix is used to connect fuzzy evaluating data and ash
Color evaluating data.
Wherein, degree of membership Evaluations matrix is determined according to membership function, and degree of membership Evaluations matrix is used for connection mode
Paste evaluating data and grey evaluation data.Membership function provides quantification for the description treatment of uncertain things relation
Method means.Evaluating reservoir of the prior art is main to carry out Reservoir Classification and scoring according to grade scale, according to rate in row
Determine that each attributes object can only be fixed and be attributed to a certain class, do not have other intermediatenesses may, and low permeability reservoir is not in reality
Boundary line between generic is not strict and accurately, there is uncertainty.The membership function of the embodiment of the present invention is used
The fuzzy partitioning of many-valued " being this or that " replaces the hard plot of " either-or ", and each object changed to difference by degree of membership
Opinion rating has certain membership, and is stated with the numerical value of [0,1], no longer definitely belongs to or is definitely not belonging to
Relation, can preferably embody the property of gradual transition between Reservoir levels, the application practice that low permeability reservoir of more fitting is evaluated.It is subordinate to
The establishment of category degree function determines that method includes statistical test there is presently no a set of ripe effective method, common degree of membership
Method, method of expertise, dualistic contrast compositor, distribution function method etc., wherein distribution function method are the more commonly used methods, mainly
Including distributed rectangular, trapezoidal profile, the distribution of brother west, the distribution of ridge shape etc., for the determination method of degree of membership, the embodiment of the present invention is not
It is specifically limited.Determine that degree of membership is set up basic evaluation matrix and can be designated as eventually through membership function：
Wherein rijDegree of membership of i-th factor to jth kind comment in expression evaluation object.
Step 103：According to the Comment gathers, the weight set and the degree of membership Evaluations matrix, fuzzy evaluation is determined
Value.
Specifically, after obtaining degree of membership Evaluations matrix, weight sets and degree of membership Evaluations matrix are carried out into fuzzy composition
Computing obtains the Comprehensis pertaining of evaluation object, and then Comprehensis pertaining normalization is obtained as weights with Comment gathers weighted sum
To fuzzy evaluation value.
Specifically, fuzzy evaluation value determination process is as follows：
The first step, according to formulaBy weight sets and degree of membership Evaluations matrix
The Comprehensis pertaining B that fuzzy composition computing obtains evaluation object is carried out, wherein, bjRepresent evaluation object to the comprehensive of jth kind comment
Close degree of membership.
Second step, according to formulaUsing Comprehensis pertaining normalization as weights with
Comment gathers weighted sum obtains middle fuzzy evaluation of estimate, wherein, K is the number of evaluation object,It is k-th evaluation object to commenting
The Comprehensis pertaining of the jth kind comment that language is concentrated.
3rd step, according to formulaDetermine fuzzy evaluation value, its
In, K is the number of evaluation object, and p is resolution ratio, vjJ-th evaluation approach in for Comment gathers.It should be noted that being
Ensure result give preferential treatment to the families of the armymen and martyrs relation size order it is constant in the case of can preferably be distinguished effect, it is excellent according to Relative Fuzzy
The value experience of category degree model and the general cognitive usual p of custom of people take 2, and certainly, those skilled in the art also visually evaluate
Effect and estimator need and preference and accordingly adjust, the embodiment of the present invention is not limited this.
In the prior art, depending on fuzzy overall evaluation result data dimension is with Comment gathers, and distribution concentrates on Comment gathers
In the range of, it is not easy to follow-up unified and compares, set up during Subject Matrix by degree of membership conversion in addition, though relative improve
The generality of data representation, embodies the obscure understanding of people, helps to reduce uncertain, but is also objectively causing original
Part detailed information is lost so that the easy overlapping redundancy of evaluation result, discrimination is not high, is analogous to the Fuzzy processing of image,
Influence the effect of evaluating reservoir.The embodiment of the present invention, on the basis of existing technology with reference to Relative Fuzzy subordinate degree model structure
Fuzzy overall evaluation is improved, makes evaluation result naturally between 0-1, while by right with optimal, most bad comment
Than strengthening different information, effectively expand result data scope and dispersion, the calculating that improve fuzzy overall evaluation numerical value is accurate
Degree degree.
Step 104：According to the Comment gathers, the weight set and the degree of membership Evaluations matrix, grey evaluation is determined
Value.
Specifically, first being synthesized degree of membership Evaluations matrix with Comment gathers as weights, the nothing of evaluation object is obtained
Analysis with dimension sequence, then the optimal comment in Comment gathers and most bad comment determine optimal reference sequences and most bad reference sequence
Row, and then each is determined in dimensionless comparative sequences and optimal reference sequences and most bad reference sequences according to Deng Shi degree of association methods
The incidence coefficient of factor, so determine optimal grey relational grade and most bad grey relational grade, finally by optimal grey relational grade with
Most bad grey relational grade carries out reinforcing contrast, obtains grey evaluation value.
According to formulaDetermine the dimensionless comparative sequences of evaluation object, its
In, diRepresent the gray system value of i-th kind of factor of evaluation object.It should be noted that evaluation object in the prior art is immeasurable
Guiding principle comparative sequences are typically with just value, equalization, normalization etc. operator and directly process to get, not in view of reservoir in itself
Ambiguity.And the dimensionless comparative sequences of the evaluation object of the embodiment of the present invention are with degree of membership as tie, to fuzzification process
Carry out detaching combination, evaluation object is obtained by the way that subordinated-degree matrix is synthesized as weights and Comment gathers in computing
Dimensionless comparative sequences, realize the combination with fuzzy overall evaluation.
Then the method for the embodiment of the present invention, with reference to the appraisal mentality of Topsis methods, makes full use of comparison information, with reservoir
The optimal comment of evaluate collection and most bad comment composition optimal reference sequences and most bad reference sequences, are designated as：WithWherein DgIt is optimal reference sequences, DbIt is most bad reference sequences.
Then ordered series of numbers is compared with optimal reference sequences using the dimensionless of Deng Shi degrees of association method Calculation Estimation object respectively
The incidence coefficient of each factor between most bad reference sequences, by trying to achieve optimal and most bad grey to incidence coefficient weighted comprehensive
The degree of association, that is, obtain relative positive ideal solution and minus ideal result, is designated as respectively：
In formula, i=1,2 ..., n；K=1,2 ..., K；ξ is resolution ratio, and ξ ∈ (0,1) can weaken due to maximum
Absolute difference numerical value causes greatly very much the influence of data distortion, and generally 0.5 [10] is taken generally according to minimum information principle.Wherein Egk、Fgk
Represent optimal incidence coefficient and optimal relational degree, Ebk、FbkRepresent most bad incidence coefficient and the most bad degree of association；Represent k-th pair
As i-th grey incidence coefficient of factor, fkK-th grey relational grade of object is represented, K is evaluation object number.
Finally according to formulaThe optimal and most bad degree of association is entered
Row reinforcing contrast, determines grey evaluation value, wherein, K is the number of evaluation object, and p is resolution ratio.It should be noted that being
Ensure result give preferential treatment to the families of the armymen and martyrs relation size order it is constant in the case of can preferably be distinguished effect, it is excellent according to Relative Fuzzy
The value experience of category degree model and the general cognitive usual resolution coefficient p of custom of people take 2, certainly, those skilled in the art
Visual evaluation effect and estimator need and preference and accordingly adjust, the embodiment of the present invention is not limited this.
Step 105：Grey evaluation value and second described in sum of products according to the fuzzy evaluation value and the first weight coefficient
The product of weight coefficient, determines the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, wherein, the first weight system
Number with second weight coefficient and equal to 1.
Specifically, determining the Fuzzy Grey comprehensive evaluation value X of low permeability reservoir quantitative assessment according to formula X=α M+ β H.Its
In, alpha+beta=1,0≤α≤1,0≤β≤1, it is preferred that α=β=0.5, certainly, those skilled in the art can also be according to reality
Effect is adjusted accordingly, and the embodiment of the present invention is not limited this.
After the Fuzzy Grey comprehensive evaluation value X for obtaining low permeability reservoir quantitative assessment, image mould can also be used for reference
The algorithm for image enhancement of inversely processing is pasted, differentiation treatment is carried out to Fuzzy Grey Comprehensive Evaluation result by nonlinear transformation, with
Strengthen final evaluation effect, specifically, referring to formula Z=(σ+τ -2) X3+(3-2σ-τ)X2+ σ X treatment, wherein, 0≤σ
≤ 1,0≤τ≤1, σ, τ control the degree and pattern of conversion, can respectively take 0.5, and also visual specific actual effect is adjusted, when
It is original transform when being both 1.
Method provided in an embodiment of the present invention, the objective reality evaluated according to low permeability reservoir first and hyposmosis storage
The data information of layer, sets up evaluate collection, and then determine that the fuzzy evaluating data of connection and the degree of membership of grey evaluation data evaluate square
Battle array, then according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determines fuzzy evaluation value, according to institute
Comment gathers, the weight set and the degree of membership Evaluations matrix are stated, grey evaluation value is determined, finally according to the fuzzy evaluation
Grey evaluation value described in the sum of products of value and the first weight coefficient and the product of the second weight coefficient, determine that low permeability reservoir is quantified
The Fuzzy Grey comprehensive evaluation value of evaluation.The method of the embodiment of the present invention comments Fuzzy Comprehensive Evaluation method and grey correlation
Valency method combines, and realizes the quantitative assessment of low permeability reservoir, taken into account low permeability reservoir information ambiguity and
Grey majorized model, has well adapted to uncertainty of the low permeability reservoir in evaluation procedure, improves low permeability reservoir quantitative assessment
The degree of accuracy.
Below by taking the low permeability reservoir quantitative assessment of the hypomere of Sulige gas field Soviet Union X wellblocks box 8 of reviving as an example, to the embodiment of the present invention
The idiographic flow of Fuzzy Grey comprehensive evaluation method illustrate.The low permeability reservoir of the hypomere of Soviet Union Sulige gas field Soviet Union X wellblocks box 8
Reservoir is complex in evaluation, data is relatively deficient.
(1) evaluate collection is set up
1. set of factors：Current low permeability reservoir evaluation index mainly includes Reservoir type (lithology, petrofacies, sedimentary facies), storage
Thickness degree (sand thickness, effective thickness), reservoir properties (porosity, permeability), Reservoir Microproperties (pore structure, diagenesis
Effect), many contents such as reservoir feature (coefficient of permeability variation, coefficient of advancing by leaps and bounds), naturally it is also possible to including pin
The new index proposed to property is such as satisfied by pressing mercury, nuclear-magnetism to test acquisition replacement pressure, pore throat median radius, minimum non-mercury
With degree percentage, movable fluid ratio etc., but these indexs are limited larger by objective, and are used for Analysis on Mechanism, microscopic sdIBM-2+2q.p.approach
Etc. aspect, it is difficult to effectively instruct macro-regions to evaluate, especially to study area for, due to input the development time it is shorter, study journey
Degree is relatively low, and data is shorter, relatively fewer using evaluating reservoir resource, and many indexs cannot be obtained or without representativeness,
Therefore this time according to Fuzzy Grey comprehensive evaluation method according to evaluating selection principle, proceed from the reality to consider and determine
More comprehensively, the relatively unambiguous evaluation index set of attribute meaning, including sand thickness H (m), porosity Φ (%), infiltration
Rate K (× 10-3 μm 2), gas saturation Sg (%), shale content Sh (%), coefficient T of advancing by leaps and bounds k.Wherein sand thickness reflection storage
The development scale of layer；Porosity reflects reservoir space size, permeability reflection Reservoir Seepage ability, gas saturation reflection reservoir
Significant degree, shale content reflect reservoir sedimentary energy height, coefficient of advancing by leaps and bounds reflection reservoir degree.The parameter set
The general characteristic of reservoir can substantially be reflected and can be readily available, wherein sand thickness, porosity, permeability and gassiness saturation
Degree is proportional with reservoir quality, is positive correlation index, shale content with advance by leaps and bounds coefficient in contrast, be negatively correlated index.
The set of factors finally set up is designated as：
2. Comment gathers：It is special according to the general hypomere reservoir development situation of Reservoir Classification custom binding area box 8 and index
Levy, comment is divided into 4 ranks, setting up Comment gathers is：V={ I II III IV }={ 0.8 0.6 0.4 0.2 }.
3. weight sets：The weight of different evaluation index is this time mainly determined by entropy assessment.In general index variation
Degree is bigger, and comentropy is smaller, and the information content that representative can be provided is more, also just relatively more important [24] in overall merit.
By in the sample data substitution entropy assessment computing formula of the multiple wells of work area 100, the weight of each index of set of factors is obtained, set up weight
Collect and be：A={ 0.225 0.170 0.180 0.175 0.125 0.125 }
(2) degree of membership is determined
Quantitative expression degree of membership and set up different classes of membership be to determine membership function main purpose and appoint
Business, the ridge shape Sequence distribution curve wherein in Fuzzy Distribution has that principal value interval is wide, intermediate zone gentle, the spy of strong antijamming capability
Point, closer to the understanding feature and custom of people, using wide in evaluation.The distribution of ridge shape with other Fuzzy Distributions it is the same according to
Description relation has type less than normal (distribution of Jiang Ban ridges shape), type bigger than normal (distribution of Sheng Ban ridges shape) and osculant (middle ridge shape is distributed) three
Specific manifestation form (with reference to shown in Fig. 2) is planted, this time according to the curve form and formula of ridge shape Sequence distribution membership function, together
When with reference to research area's reservoir index regularity of distribution and evaluation criterion construct each evaluating and different evaluation grade be subordinate to
Degree distribution function.The reservoir sample of single parameter can be converted to each Reservoir levels using the membership function curve set up
Many-valued membership set, and then the uncertainty relation mapping from set of factors to Comment gathers is realized, with x-12-21 well boxes of reviving
As a example by 8 hypomere reservoirs, its sample data U=(13.75,6.59,0.41,32.49,10.55,1.88), wherein sand thickness H=
13.75, the membership function curve equation (described in reference diagram 3) of substitution sand thickness parameter obtains the person in servitude to four class reservoir comments
Category degree is (0.854,1,0.146,0), and the degree of membership set of whole set of factors is obtained by that analogy, constitutes x-12-21 wells box 8 of reviving
The degree of membership Evaluations matrix R of hypomere evaluating reservoir, is designated as：
(3) fuzzy overall evaluation
By taking the above-mentioned hypomere reservoir of Soviet Union x-12-21 wells box 8 as an example, the basis matrix R substitution fuzzy synthesis that are subordinate to that will be obtained are commented
Valency formula In can calculate respectively Comprehensis pertaining B=(0.593,0.958,
0.407,0.026), middle fuzzy evaluation of estimate C=0.614, fuzzy evaluation value M=0.831, further genralrlization obtains the mould of the whole district
Paste comprehensive evaluation result.
(4) gray correlation assessment
The same basis matrix R substitution grey that is subordinate to that by taking the above-mentioned hypomere reservoir of Soviet Union x-12-21 wells box 8 as an example, will be obtained is closed
Connection judgement schematicsObtain the reservoir grey comparative sequences D=(0.671,
0.680,0.502,0.470,0.694,0.707)；By formula
It is determined that the optimal ordered series of numbers for comparing be Dg=(0.8,0.8,0.8,0.8,0.8,0.8) and most bad ordered series of numbers for Db=(0.2,0.2,
0.2,0.2,0.2,0.2)；Using formula Calculate
It is Eg=(0.699,0.714,0.502,0.476,0.739,0.763), Fg=to go out optimal grey incidence coefficient and the degree of association
0.640；Using formulaCalculate most bad grey incidence coefficient and association
Spend is Eb=(0.389,0.385,0.498,0.526,0.378,0.372), Fb=0.428；The grey correlation knot for finally obtaining
Fruit is H=0.727, and the gray correlation assessment result of the whole district is can obtain according to same method.
(5) the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment is determined
By taking the above-mentioned hypomere reservoir of Soviet Union x-12-21 wells box 8 as an example, the fuzzy evaluation value M=0.831 after improving is being respectively obtained
After grey evaluation value H=0.727, the comprehensive evaluation value X=0.627 that formula X=α M+ β H are obtained is substituted into, substituted into
Formula Z=(σ+τ -2) X3+(3-2σ-τ)X2+ σ X obtain last reservoir comprehensive quantitative evaluation value Z=0.827.
(6) experimental verification analysis
Target For Drilling is ranked and preferably using the comprehensive quantitative evaluation result after finally improving, and it is poly- by system
Class combination gas field is actual to mark off I class reservoir (comprehensive score is 0.7-1.0), II class reservoir (comprehensively by the research hypomere of area's box 8
Be scored at 0.5-0.7), III class reservoir (comprehensive score be 0.3-0.5) and IV class reservoir (comprehensive score is 0-0.3).It is comprehensive
Classification results effective integration each reservoir parameter, it is to avoid the not unique and inconsistency of single classification, by with existing pilot production
Development control matches substantially, is preferably checked to a certain extent, can be used as geological control foundation：Generally produce
Tolerance is more, the preferable well reservoir geology of development effectiveness evaluates also of a relatively high, mostly I, II class reservoir, and its average productivity can be
1.0 × more than 104m3/d, constitutes main payzone, and III class reservoir gas production is relatively low, development effectiveness deviation, and IV class is stored up
Layer aerogenesis is micro, and majority is dried layer, does not possess commercial development value substantially.For specific evaluation example Soviet Union x-12-21 wells box 8
Hypomere reservoir comprehensive score is 0.827, and opinion rating belongs to I class reservoir, after tested, its individual well day gas production 1.25 ×
104m3 or so, possesses production capacity higher, and evaluation result is consistent with produce reality.
Fig. 5 is a kind of Fuzzy Grey Comprehensive Evaluation dress for low permeability reservoir quantitative assessment provided in an embodiment of the present invention
The structural representation put, referring to Fig. 5, the device can include：
Module 510 is built, for the objective reality and the data money of the low permeability reservoir evaluated according to low permeability reservoir
Material, sets up evaluate collection, and the evaluate collection includes set of factors, Comment gathers and weight sets；
First determining module 520, for determining degree of membership Evaluations matrix, the degree of membership Evaluations matrix is used to connect fuzzy
Evaluating data and grey evaluation data；
Second determining module 530, for according to the Comment gathers, the weight set and the degree of membership Evaluations matrix,
Determine fuzzy evaluation value；
3rd determining module 540, for according to the Comment gathers, the weight set and the degree of membership Evaluations matrix,
Determine grey evaluation value；
4th determining module 550, for grey described in the sum of products according to the fuzzy evaluation value and the first weight coefficient
Evaluation of estimate and the product of the second weight coefficient, determine the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, wherein, institute
State the first weight coefficient and second weight coefficient and equal to 1.
Optionally, with reference to shown in Fig. 6, first determining module 520 includes：
First determination sub-module 5201, for determining each factor in the set of factors to the evaluate collection in it is various
The degree of membership of comment；
First treatment submodule 5202, for each factor in the set of factors to the evaluate collection in it is various
The degree of membership of comment, determines the degree of membership Evaluations matrix, wherein, the degree of membership Evaluations matrix isR in formulaijDegree of membership of i-th factor to jth kind comment in expression evaluation object.
Optionally, with reference to shown in Fig. 7, second determining module 530 includes：
Second synthesis submodule 5301, for the weight sets to be carried out into fuzzy composition fortune with the degree of membership Evaluations matrix
Calculation obtains the Comprehensis pertaining of evaluation object；
Second determination sub-module 5302, for Comprehensis pertaining normalization to be weighted as weights with the Comment gathers
Summation obtains the fuzzy evaluation value.
Optionally, second determination sub-module 4302 specifically for：
According to formulaDetermine the fuzzy evaluation value, wherein, K
It is the number of evaluation object, p is resolution ratio, vjIt is j-th evaluation approach in the Comment gathers, It is k-th evaluation object to the Comprehensis pertaining of the jth kind comment in the Comment gathers.
Optionally, the 3rd determining module 540 specifically for：
The degree of membership Evaluations matrix is synthesized as weights with the Comment gathers, the dimensionless of evaluation object is obtained
Comparative sequences；
Optimal comment and most bad comment in the Comment gathers determine optimal reference sequences and most bad reference sequences；
The dimensionless comparative sequences and the optimal reference sequences and described most bad are determined according to Deng Shi degree of association methods
The incidence coefficient of each factor in reference sequences, and then determine optimal grey relational grade and most bad grey relational grade；
The optimal grey relational grade and the most bad grey relational grade are carried out into reinforcing contrast, the grey evaluation is obtained
Value.
Device provided in an embodiment of the present invention, the objective reality evaluated according to low permeability reservoir first and hyposmosis storage
The data information of layer, sets up evaluate collection, and then determine that the fuzzy evaluating data of connection and the degree of membership of grey evaluation data evaluate square
Battle array, then according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determines fuzzy evaluation value, according to institute
Comment gathers, the weight set and the degree of membership Evaluations matrix are stated, grey evaluation value is determined, finally according to the fuzzy evaluation
Grey evaluation value described in the sum of products of value and the first weight coefficient and the product of the second weight coefficient, determine that low permeability reservoir is quantified
The Fuzzy Grey comprehensive evaluation value of evaluation.The device of the embodiment of the present invention comments Fuzzy Comprehensive Evaluation method and grey correlation
Valency method combines, and realizes the quantitative assessment of low permeability reservoir, taken into account low permeability reservoir information ambiguity and
Grey majorized model, has well adapted to uncertainty of the low permeability reservoir in evaluation procedure, improves low permeability reservoir quantitative assessment
The degree of accuracy.
It should be noted that：A kind of Fuzzy Grey synthesis for low permeability reservoir quantitative assessment that above-described embodiment is provided
Evaluation method is carried out when Fuzzy Grey Comprehensive Evaluation is carried out, only with the division of above-mentioned each functional module for example, actual should
In, can be completed by different functional module as needed and by above-mentioned functions distribution, will the internal structure of equipment divide
Into different functional modules, to complete all or part of function described above.In addition, above-described embodiment provide for low
The Fuzzy Grey Comprehensive Evaluation device of permeable reservoir strata quantitative assessment and the Fuzzy Grey synthesis for low permeability reservoir quantitative assessment
Evaluation method embodiment belongs to same design, and it implements process and refers to embodiment of the method, repeats no more here.
The embodiments of the present invention are for illustration only, and the quality of embodiment is not represented.
One of ordinary skill in the art will appreciate that realizing that all or part of step of above-described embodiment can be by hardware
To complete, it is also possible to instruct the hardware of correlation to complete by program, described program can be stored in a kind of computer-readable
In storage medium, storage medium mentioned above can be read-only storage, disk or CD etc..
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all it is of the invention spirit and
Within principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Finally it should be noted that：Various embodiments above is merely illustrative of the technical solution of the present invention, rather than its limitations；To the greatest extent
Pipe has been described in detail with reference to foregoing embodiments to the present invention, it will be understood by those within the art that：Its according to
The technical scheme described in foregoing embodiments can so be modified, or which part or all technical characteristic are entered
Row equivalent；And these modifications or replacement, the essence of appropriate technical solution is departed from various embodiments of the present invention technology
The scope of scheme.
Claims (10)
1. a kind of Fuzzy Grey comprehensive evaluation method for low permeability reservoir quantitative assessment, it is characterised in that methods described bag
Include：
The objective reality and the data information of the low permeability reservoir evaluated according to low permeability reservoir, set up evaluate collection, institute's commentary
Valency collection includes set of factors, Comment gathers and weight sets；
Determine degree of membership Evaluations matrix, the degree of membership Evaluations matrix is used to connect fuzzy evaluating data and grey evaluation data；
According to the Comment gathers, the weight set and the degree of membership Evaluations matrix, fuzzy evaluation value is determined；
According to the Comment gathers, the weight set and the degree of membership Evaluations matrix, grey evaluation value is determined；
Grey evaluation value and the second weight coefficient multiplies according to the sum of products of the fuzzy evaluation value and the first weight coefficient
Product, determines the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, wherein, first weight coefficient and described second
Weight coefficient and equal to 1.
2. method according to claim 1, it is characterised in that the determination degree of membership Evaluations matrix, including：
The degree of membership of the various comments in determining each factor in the set of factors to the evaluate collection；
Each factor in the set of factors to the evaluate collection in various comments degree of membership, determine the degree of membership
Evaluations matrix, wherein, the degree of membership Evaluations matrix isR in formulaijRepresent and evaluate
Degree of membership of i-th factor to jth kind comment in object.
3. method according to claim 1, it is characterised in that described according to the Comment gathers, the weight set and institute
Degree of membership Evaluations matrix is stated, fuzzy evaluation value is determined, including：
The weight sets is carried out into the Comprehensis pertaining that fuzzy composition computing obtains evaluation object with the degree of membership Evaluations matrix；
Comprehensis pertaining normalization is obtained into the fuzzy evaluation value as weights with the Comment gathers weighted sum.
4. method according to claim 3, it is characterised in that it is described using Comprehensis pertaining normalization as weights with
The Comment gathers weighted sum obtains the fuzzy evaluation value, including：
According to formulaDetermine the fuzzy evaluation value, wherein, K is to comment
The number of valency object, p is resolution ratio, vjIt is j-th evaluation approach in the Comment gathers, It is k-th evaluation object to the Comprehensis pertaining of the jth kind comment in the Comment gathers.
5. method according to claim 1, it is characterised in that described according to the Comment gathers, the weight set and institute
Degree of membership Evaluations matrix is stated, grey evaluation value is determined, including：
The degree of membership Evaluations matrix is synthesized as weights with the Comment gathers, the dimensionless for obtaining evaluation object compares
Sequence；
Optimal comment and most bad comment in the Comment gathers determine optimal reference sequences and most bad reference sequences；
Determine the dimensionless comparative sequences with the optimal reference sequences and the most bad reference according to Deng Shi degree of association methods
The incidence coefficient of each factor in sequence, and then determine optimal grey relational grade and most bad grey relational grade；
The optimal grey relational grade and the most bad grey relational grade are carried out into reinforcing contrast, the grey evaluation value is obtained.
6. a kind of Fuzzy Grey Comprehensive Evaluation device for low permeability reservoir quantitative assessment, it is characterised in that described device bag
Include：
Module is built, for the objective reality and the data information of the low permeability reservoir evaluated according to low permeability reservoir, is set up
Evaluate collection, the evaluate collection includes set of factors, Comment gathers and weight sets；
First determining module, for determining degree of membership Evaluations matrix, the degree of membership Evaluations matrix is used to connect fuzzy evaluation number
According to grey evaluation data；
Second determining module, for according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, it is determined that fuzzy
Evaluation of estimate；
3rd determining module, for according to the Comment gathers, the weight set and the degree of membership Evaluations matrix, determining grey
Evaluation of estimate；
4th determining module, for the grey evaluation value according to the sum of products of the fuzzy evaluation value and the first weight coefficient with
The product of the second weight coefficient, determines the Fuzzy Grey comprehensive evaluation value of low permeability reservoir quantitative assessment, wherein, first power
Weight coefficient and second weight coefficient and equal to 1.
7. device according to claim 6, it is characterised in that first determining module includes：
First determination sub-module, for determining each factor in the set of factors to the evaluate collection in various comments person in servitude
Category degree；
First treatment submodule, for each factor in the set of factors to the evaluate collection in it is each
The degree of membership of comment is planted, the degree of membership Evaluations matrix is determined, wherein, the degree of membership Evaluations matrix isR in formulaijDegree of membership of i-th factor to jth kind comment in expression evaluation object.
8. device according to claim 6, it is characterised in that second determining module includes：
Second synthesis submodule, is commented for the weight sets to be carried out into fuzzy composition computing with the degree of membership Evaluations matrix
The Comprehensis pertaining of valency object；
Second determination sub-module, for Comprehensis pertaining normalization to be obtained as weights with the Comment gathers weighted sum
The fuzzy evaluation value.
9. device according to claim 8, it is characterised in that second determination sub-module specifically for：
According to formulaDetermine the fuzzy evaluation value, wherein, K is to comment
The number of valency object, p is resolution ratio, vjIt is j-th evaluation approach in the Comment gathers, It is k-th evaluation object to the Comprehensis pertaining of the jth kind comment in the Comment gathers.
10. device according to claim 6, it is characterised in that the 3rd determining module specifically for：
The degree of membership Evaluations matrix is synthesized as weights with the Comment gathers, the dimensionless for obtaining evaluation object compares
Sequence；
Optimal comment and most bad comment in the Comment gathers determine optimal reference sequences and most bad reference sequences；
Determine the dimensionless comparative sequences with the optimal reference sequences and the most bad reference according to Deng Shi degree of association methods
The incidence coefficient of each factor in sequence, and then determine optimal grey relational grade and most bad grey relational grade；
The optimal grey relational grade and the most bad grey relational grade are carried out into reinforcing contrast, the grey evaluation value is obtained.
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